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Exploring New Ways to Support Faculty Research
jueves, 26 de marzo de 2020
Posted by Maggie Johnson, VP, Google Research
For the past 15 years, the
Google Faculty Research Award Program
has helped support world-class technical research in computer science, engineering, and related fields, funding over 2000 academics at ~400 Universities in 50+ countries since its inception. As Google Research continues to evolve, we continually explore new ways to improve our support of the broader research community, specifically on how to support new faculty while also strengthening our existing collaborations .
To achieve this goal, we are introducing two new programs aimed at diversifying our support across a larger community. Moving forward, these programs will replace the Faculty Research Award program, allowing us to better engage with, and support, up-and-coming researchers:
The
Research Scholar Program
supports early-career faculty (those who have received their doctorate within the past 7 years) who are doing impactful research in fields relevant to Google, and is intended to help to develop new collaborations and encourage long term relationships. This program will be open for applications in Fall 2020, and we encourage submissions from faculty at universities around the world.
We will also be piloting the
Award for Inclusion Research Program
, which will recognize and support research that addresses the needs of historically underrepresented populations. This Summer we will invite faculty—both directly and via their institutions—to submit their research proposals for consideration later this year, and we will notify award recipients by year's end.
These programs will complement our existing support of academic research around the world, including the
Latin America Research Awards
, the
PhD Fellowship Program
, the
Visiting Researcher Program
and research grant funding. To explore other ways we are supporting the research community, please visit
this page
. As always, we encourage faculty to review our
publication database
for overlapping research interests for collaboration opportunities, and apply to the above programs. We look forward to working with you!
A Neural Weather Model for Eight-Hour Precipitation Forecasting
miércoles, 25 de marzo de 2020
Posted by Nal Kalchbrenner and Casper Sønderby, Research Scientists, Google Research, Amsterdam
Predicting weather from minutes to weeks ahead with high accuracy is a fundamental scientific challenge that can have a wide ranging impact on many aspects of society. Current forecasts employed by many meteorological agencies are based on physical models of the atmosphere that, despite improving substantially over the preceding decades, are inherently constrained by their computational requirements and are sensitive to approximations of the physical laws that govern them. An alternative approach to weather prediction that is able to overcome some of these constraints uses
deep neural networks
(DNNs): instead of encoding explicit physical laws, DNNs discover patterns in the data and learn complex transformations from inputs to the desired outputs using parallel computation on powerful specialized hardware such as GPUs and
TPUs
.
Building on our
previous research
into precipitation
nowcasting
, we present “
MetNet: A Neural Weather Model for Precipitation Forecasting
,” a DNN capable of predicting future precipitation at 1 km resolution over 2 minute intervals at timescales up to 8 hours into the future. MetNet outperforms the current state-of-the-art physics-based model in use by
NOAA
for prediction times up to 7-8 hours ahead and makes a prediction over the entire US in a matter of seconds as opposed to an hour. The inputs to the network are sourced automatically from radar stations and satellite networks without the need for human annotation. The model output is a probability distribution that we use to infer the most likely precipitation rates with associated uncertainties at each geographical region. The figure below provides an example of the network’s predictions over the continental United States.
MetNet model predictions compared to the ground truth as measured by the NOAA multi-radar/multi-sensor system (MRMS). The MetNet model (top) displays the probabilities for 1 mm/hr precipitation predicted from 2 minutes to 480 minutes ahead, whereas the MRMS data (bottom) shows the areas receiving at least 1 mm/hr of precipitation over that same time period.
Neural Weather Model
MetNet does not rely on explicit physical laws describing the dynamics of the atmosphere, but instead learns by backpropagation to forecast the weather directly from observed data. The network uses precipitation estimates derived from ground based radar stations comprising the
multi-radar/multi-sensor system
(MRMS) and measurements from NOAA’s
Geostationary Operational Environmental Satellite
system that provides a top down view of clouds in the atmosphere. Both data sources cover the continental US and provide image-like inputs that can be efficiently processed by the network.
The model is executed for every 64 km x 64 km square covering the entire US at 1 km resolution. However, the actual physical coverage of the input data corresponding to each of these output regions is much larger, since it must take into account the possible motion of the clouds and precipitation fields over the time period for which the prediction is made. For example, assuming that clouds move up to 60 km/h, in order to make informed predictions that capture the temporal dynamics of the atmosphere up to 8 hours ahead, the model needs 60 x 8 = 480 km of spatial context in all directions. So, to achieve this level of context, information from a 1024 km x 1024 km area is required for predictions being made on the center 64 km x 64 km patch.
Size of the input patch containing satellite and radar images (large, 1024 x 1024 km square) and of the output predicted radar image (small, 64 x 64 km square).
Because processing a 1024 km x 1024 km area at full resolution requires a significant amount of memory, we use a spatial downsampler that decreases memory consumption by reducing the spatial dimension of the input patch, while also finding and keeping the relevant weather patterns in the input. A temporal encoder (implemented with a
convolutional LSTM
that is especially well suited for sequences of images) is then applied along the time dimension of the downsampled input data, encoding seven snapshots from the previous 90 minutes of input data, in 15-minute segments. The output of the temporal encoder is then passed to a spatial aggregator, which uses
axial self-attention
to efficiently capture long range spatial dependencies in the data, with a variable amount of context based on the input target time, to make predictions over the 64 km x 64 km output.
The output of this architecture is a discrete probability distribution estimating the probability of a given rate of precipitation for each square kilometer in the continental United States.
The architecture of the neural weather model, MetNet. The input satellite and radar images first pass through a spatial downsampler to reduce memory consumption. They are then processed by a convolutional LSTM at 15 minute intervals over the 90 minutes of input data. Then axial attention layers are used to make the network see the entirety of the input images.
Results
We evaluate MetNet on a
precipitation rate forecasting benchmark
and compare the results with two baselines — the NOAA
High Resolution Rapid Refresh
(HRRR) system, which is the physical weather forecasting model currently operational in the US, and a
baseline model
that estimates the motion of the precipitation field (i.e.,
optical flow
), a method known to perform well for prediction times less than 2 hours.
A significant advantage of our neural weather model is that it is optimized for dense and parallel computation and well suited for running on specialty hardware (e.g.,
TPUs
). This allows predictions to be made in parallel in a matter of seconds, whether it is for a specific location like New York City or for the entire US, whereas physical models such as HRRR have a runtime of about an hour on a supercomputer.
We quantify the difference in performance between MetNet, HRRR, and the optical flow baseline model in the plot below. Here, we show the performance achieved by the three models, evaluated using the
F1-score
at a precipitation rate threshold of 1.0 mm/h, which corresponds to light rain. The MetNet neural weather model is able to outperform the NOAA HRRR system at timelines less than 8 hours and is consistently better than the flow-based model.
Performance evaluated in terms of F1-score at 1.0 mm/h precipitation rate (higher is better). The neural weather model (MetNet) outperforms the physics-based model (HRRR) currently operational in the US for timescales up to 8 hours ahead.
Due to the stochastic nature of the atmosphere, the uncertainty about the exact future weather conditions increases with longer prediction times. Because MetNet is a probabilistic model, the uncertainty in the predictions is seen in the visualizations by the growing smoothness of the predictions as the forecast time is extended. In contrast, HRRR does not directly make probabilistic predictions, but instead predicts a
single
potential future. The figure below compares the output of the MetNet model to that of the HRRR model.
Comparison between the output from MetNet (
top
) and HRRR (
bottom
) to ground-truth (
middle
) as retrieved from the NOAA MRMS system. Notice that while the HRRR model predicts structure that appears to be more similar to that of the ground-truth, the structure predicted may be grossly incorrect.
The predictions from the HRRR physical model look sharper and more structured than that of the MetNet model, but the structure, specifically the exact time and location where the structure is predicted, is less accurate due to uncertainty in the initial conditions and the parameters of the model.
HRRR (
left
) predicts a single potential future outcome (in red) out of the many possible outcomes, whereas MetNet (
right
) directly accounts for uncertainty by assigning probabilities over the future outcomes.
A more thorough comparison between the HRRR and MetNet models can be found in the video below:
Future Directions
We are actively researching how to improve global weather forecasting, especially in regions where the impacts of rapid climate change are most profound. While we demonstrate the present MetNet model for the continental US, it could be extended to cover any region for which adequate radar and optical satellite data are available. The work presented here is a small stepping stone in this effort that we hope leads to even greater improvements through future collaboration with the meteorological community.
Acknowledgements
This project was done in collaboration with Lasse Espeholt, Jonathan Heek, Mostafa Dehghani, Avital Oliver, Tim Salimans, Shreya Agrawal and Jason Hickey. We would also like to thank Manoj Kumar, Wendy Shang, Dick Weissenborn, Cenk Gazen, John Burge, Stephen Hoyer, Lak Lakshmanan, Rob Carver, Carla Bromberg and Aaron Bell for useful discussions and Tom Small for help with the visualizations.
Massively Scaling Reinforcement Learning with SEED RL
lunes, 23 de marzo de 2020
Posted by Lasse Espeholt, Research Engineer, Google Research, Amsterdam
Reinforcement learning (RL) has seen impressive advances over the last few years as demonstrated by the recent success in solving games such as
Go
and
Dota 2
. Models, or
agents
, learn by exploring an environment, such as a game, while optimizing for specified goals. However, current RL techniques require increasingly large amounts of training to successfully learn even simple games, which makes iterating research and product ideas computationally expensive and time consuming.
In “
SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference
”, we present an RL agent that scales to thousands of machines, which enables training at millions of frames per second, and significantly improves computational efficiency. This is achieved with a novel architecture that takes advantage of
accelerators
(
GPUs
or
TPUs
) at scale by centralizing model inference and introducing a fast communication layer. We demonstrate the performance of SEED RL on popular RL benchmarks, such as
Google Research Football
,
Arcade Learning Environment
and
DeepMind Lab
, and show that by using larger models, data efficiency can be increased. The code has been open sourced on
Github
together with examples for running on Google Cloud with
GPUs
.
Current Distributed Architectures
The previous generation of distributed reinforcement learning agents, such as
IMPALA
, made use of accelerators specialized for numerical calculations, taking advantage of the speed and efficiency from which (un)supervised learning has benefited for years. The architecture of an RL agent is usually separated into
actors
and
learners
. The actors typically run on CPUs and iterate between taking steps in the environment and running inference on the model to predict the next action. Frequently the actor will update the parameters of the inference model, and after collecting a sufficient amount of observations, will send a trajectory of observations and actions to the learner, which then optimizes the model. In this architecture, the learner trains the model on GPUs using input from distributed inference on hundreds of machines.
Example architecture for an earlier generation RL agent,
IMPALA
. Inference is done on the actors, usually using inefficient CPUs. Updated model parameters are frequently sent from the learner to the actors increasing bandwidth requirements.
The architecture of RL agents (such as IMPALA) have a number of drawbacks:
Using CPUs for neural network inference is much less efficient and slower than using accelerators and becomes problematic as models become larger and more computationally expensive.
The bandwidth required for sending parameters and intermediate model states between the actors and learner can be a bottleneck.
Handling two completely different tasks on one machine (i.e., environment rendering and inference) is unlikely to utilize machine resources optimally.
SEED RL Architecture
The SEED RL architecture is designed to solve these drawbacks. With this approach, neural network inference is done centrally by the learner on specialized hardware (GPUs or TPUs), enabling accelerated inference and avoiding the data transfer bottleneck by ensuring that the model parameters and state are kept local. While observations are sent to the learner at every environment step, latency is kept low due to a very efficient
network library
based on the
gRPC
framework with asynchronous streaming RPCs. This makes it possible to achieve up to a million queries per second on a single machine. The learner can be scaled to thousands of cores (e.g., up to 2048 on Cloud TPUs) and the number of actors can be scaled to thousands of machines to fully utilize the learner, making it possible to train at millions of frames per second. SEED RL is based on the
TensorFlow 2
API and, in our experiments, was accelerated by
TPUs
.
Overview of the architecture of SEED RL. In contrast to the IMPALA architecture, the actors only take actions in environments. Inference is executed centrally by the learner on accelerators using batches of data from multiple actors.
In order for this architecture to be successful, two state-of-the-art algorithms are integrated into SEED RL. The first is
V-trace
, a
policy gradient
-based method, first introduced with IMPALA. In general, policy gradient-based methods predict an action distribution from which an action can be sampled. However, because the actors and the learner execute asynchronously in SEED RL, the policy of actors is slightly behind the policy of the learner, i.e., they become
off-policy
. The usual policy gradient-based methods are
on-policy
, meaning that they have the same policy for actors and learner, and suffer from convergence and numerical issues in off-policy settings. V-trace is an off-policy method and thus works well in the asynchronous SEED RL architecture.
The second algorithm is
R2D2
, a
Q-learning
method that selects an action based on the predicted future value of that action using recurrent distributed replay. This approach allows the Q-learning algorithm to be run at scale, while still allowing the use of recurrent neural networks that can predict future values based on the information of all past frames in an episode.
Experiments
SEED RL is benchmarked on the commonly used Arcade Learning Environment, DeepMind Lab environments, and on the recently released Google Research Football environment.
Frames per second comparing IMPALA and various configurations of SEED RL on DeepMind Lab. SEED RL achieves 2.4M frames per second using 4,160 CPUs. Assuming the same speed, IMPALA would need 14,000 CPUs.
On DeepMind Lab, we achieve 2.4 million frames per second with 64 Cloud TPU cores, which represents an improvement of 80x over the previous state-of-the-art distributed agent, IMPALA. This results in a significant speed-up in wall-clock time and computational efficiency. IMPALA requires 3-4x as many CPUs as SEED RL for the same speed.
Episode return (i.e., the sum of rewards) over time on the
DeepMind Lab
game “explore_goal_locations_small” using IMPALA and SEED RL. With SEED RL, the time to train is significantly reduced.
With an architecture optimized for use on modern accelerators, it’s natural to increase the model size in an attempt to increase data efficiency. We show that by increasing the size of the model and the input resolution, we are able to solve a previously unsolved Google Research Football task, “Hard”.
The score of different architectures on the Google Research Football “Hard” task. We show that by using an input resolution and a larger model, the score is improved, and with more training, the model can significantly outperform the builtin AI.
Additional details are provided in the
paper
, including our results on the Arcade Learning Environment. We believe SEED RL and the results presented, demonstrate that reinforcement learning has once again caught up with the rest of the deep learning field in terms of taking advantage of accelerators.
Acknowledgements
This project was done in collaboration with Raphaël Marinier, Piotr Stanczyk, Ke Wang, Marcin Andrychowicz and Marcin Michalski. We would also like to thank Tom Small for the visualizations.
Visual Transfer Learning for Robotic Manipulation
viernes, 20 de marzo de 2020
Posted by Yen-Chen Lin, Research Intern and Andy Zeng, Research Scientist, Robotics at Google
The idea that robots can learn to directly perceive the
affordances
of actions on objects (i.e., what the robot can or cannot do with an object) is called
affordance-based manipulation
, explored in research on learning complex vision-based manipulation skills including
grasping
,
pushing
, and
throwing
. In these systems, affordances are represented as dense pixel-wise action-value maps that estimate how good it is for the robot to execute one of several predefined motions at each location. For example, given an RGB-D image, an affordance-based grasping model might infer grasping affordances per pixel with a convolutional neural network. The grasping affordance value at each pixel would represent the success rate of performing a corresponding motion primitive (e.g. grasping action), which would then be executed by the robot at the position with the highest value.
Overview of affordance-based manipulation.
For methods such as this, the ability to do more with less data is incredibly important, since
data collection
through physical
trial and error
can be both time consuming and expensive. However,
recent
discoveries
in
transfer learning
have shown that visual feature representations learned from large-scale computer vision datasets can be reused for deep learning agents, enabling them to learn faster and generalize better in video games and simulated environments. If end-to-end affordance-based robot learning models that map from pixels to actions could similarly benefit from these visual representations, one could begin to leverage the vast amounts of labeled visual data that are
now available
in order to more efficiently learn useful skills for real-world interaction with less training.
In “
Learning to See before Learning to Act: Visual Pre-training for Manipulation
”, a collaboration with
researchers from MIT
to be presented at
ICRA 2020
, we investigate whether existing pre-trained deep learning visual feature representations can improve the efficiency of learning robotic manipulation tasks, like grasping objects. By studying how we can intelligently transfer neural network weights between vision models and affordance-based manipulation models, we can evaluate how different visual feature representations benefit the
exploration process
and enable robots to quickly acquire manipulation skills using different
grippers
. We present practical techniques to pre-train deep learning models, which enable robots to learn to pick and grasp arbitrary objects in unstructured settings in less than 10 minutes of trial and error.
Does first learning to see, improve the speed at which a robot can learn to act? In this project, we study ways in which we can transfer knowledge learned from computer vision tasks (left) to robot manipulation tasks (right).
Transfer Learning for Affordance-Based Manipulation
Affordance-based manipulation is essentially a way to reframe a manipulation task as a computer vision task, but rather than referencing pixels to object
labels
, we instead associate pixels to the
value of actions
. Since the structure of computer vision models and affordance models are so similar, one can leverage techniques from transfer learning in computer vision to enable affordance models to learn faster with less data. This approach re-purposes pre-trained neural network weights (i.e., feature representations) learned from large-scale vision datasets to initialize network weights of affordance models for robotic grasping.
In computer vision, many
deep model
architectures are composed of two parts: a “backbone” and a “head”. The backbone consists of weights that are responsible for early-stage image processing, e.g., filtering edges, detecting corners, and distinguishing between colors, while the head consists of network weights that are used in latter-stage processing, such as identifying high-level features, recognizing contextual cues, and executing spatial reasoning. The head is often much smaller than the backbone and is also more task specific. Hence, it is common practice in transfer learning to pre-train (e.g., on
ResNet
) and share backbone weights between tasks, while randomly initializing the weights of the model head for each new task.
Following this recipe, we initialized our affordance-based manipulation models with backbones based on the ResNet-50 architecture and pre-trained on different vision tasks, including a classification model from
ImageNet
and a segmentation model from
COCO
. With different initializations, the robot was then tasked with learning to grasp a diverse set of objects through trial and error.
Initially, we did not see any significant gains in performance compared with training from scratch – grasping success rates on training objects were only able to rise to 77% after 1,000 trial and error grasp attempts, outperforming training from scratch by 2%. However, upon transferring network weights from both the backbone and the head of the pre-trained COCO vision model, we saw a substantial improvement in training speed – grasping success rates reached 73% in just 500 trial and error grasp attempts, and jumped to 86% by 1,000 attempts. In addition, we tested our model on new objects unseen during training and found that models with the pre-trained backbone from COCO generalize better. The grasping success rates reach 83% with pre-trained backbone alone and further improve to 90% with both pre-trained backbone and head, outperforming the 46% reached by a model trained from scratch.
Affordance-based grasping models trained from scratch can struggle to pick up new objects after 60 minutes of training (
left
). With pre-training from visual tasks, our affordance-based grasping models can easily generalize to picking up new objects with less than 10 minutes of training, even when evaluated with different hardware (
middle:
suction,
right:
gripper).
Transfer Learning Can Improve Exploration
In our experiments with the grasping robot, we observed that the distribution of successful grasps versus failures in the generated datasets was far more balanced when network weights from both the backbone and head of pre-trained vision models were transferred to the affordance models, as opposed to only transferring the backbone.
Number of successful grasps out of the first 50 attempts using: a random initialization of weights, backbone and head pre-trained on ImageNet, COCO pre-trained backbone only, and backbone and head trained on COCO.
These results suggest that reusing network weights from vision tasks that require object localization (e.g., instance segmentation, like COCO) has the potential to significantly improve the exploration process when learning manipulation tasks. Pre-trained weights from these tasks encourage the robot to sample actions on things that look more like objects, thereby quickly generating a more balanced dataset from which the system can learn the differences between good and bad grasps. In contrast, pre-trained weights from vision tasks that potentially discard objects’ spatial information (e.g., image classification, like ImageNet) can only improve the performance slightly compared to random initialization.
To better understand this, we visualize the neural activations that are triggered by different pre-trained models and a converged affordance model trained from scratch using a suction gripper. Interestingly, we find that the intermediate network representations learned from the head of vision models used for segmentation from the COCO dataset activate on objects in ways that are similar to the converged affordance model. This aligns with the idea that transferring as much of the vision model as possible (both backbone and head) can lead to more object-centric exploration by leveraging model weights that are better at picking up visual features and localizing objects.
Affordances predicted by different models from images of cluttered objects (
a
). (
b
) Random refers to a randomly initialized model. (
c
) ImageNet is a model with backbone pre-trained on ImageNet and a randomly initialized head. (
d
) Normal refers to a model pre-trained to detect pixels with
surface normals
close to the anti-gravity axis. (
e
) COCO is the modified segmentation model (
MaskRCNN
) trained on the COCO dataset. (
f
) Suction is a converged model learned from robot-environment interactions using the suction gripper.
Limitations and Future Work
Many of the methods that we use today for end-to-end robot learning are effectively the same as those being used for computer vision tasks. Our work here on visual pre-training illuminates this connection and demonstrates that it is possible to leverage techniques from visual pre-training to improve the learning efficiency of affordance-base manipulation applied to robotic grasping tasks. While our experiments point to a better understanding of deep learning for robots, there are still many interesting questions that have yet to be explored. For example, how do we leverage large-scale pre-training for additional modes of sensing (e.g. force-torque or tactile)? How do we extend these pre-training techniques towards more complex manipulation tasks that may not be as object-centric as grasping? These areas are promising directions for future research.
You can learn more about this work in the summary video below.
Acknowledgements
This research was done by Yen-Chen Lin (Ph.D. student at MIT), Andy Zeng, Shuran Song, Phillip Isola (faculty at MIT), and Tsung-Yi Lin, with special thanks to Johnny Lee and Ivan Krasin for valuable managerial support, Chad Richards for helpful feedback on writing, and Jonathan Thompson for fruitful technical discussions.
Introducing Dreamer: Scalable Reinforcement Learning Using World Models
miércoles, 18 de marzo de 2020
Posted by Danijar Hafner, Student Researcher, Google Research
Research into how
artificial agents
can choose actions to achieve goals is making rapid progress in large part due to the use of
reinforcement learning
(RL).
Model-free
approaches to RL, which learn to predict successful actions through trial and error, have enabled DeepMind's
DQN
to play Atari games and
AlphaStar
to beat world champions at Starcraft II, but require large amounts of environment interaction, limiting their usefulness for
real-world scenarios
.
In contrast,
model-based
RL approaches additionally learn a simplified model of the environment. This
world model
lets the agent predict the outcomes of potential action sequences, allowing it to play through hypothetical scenarios to make informed decisions in new situations, thus reducing the trial and error necessary to achieve goals. In the past, it has been challenging to learn accurate world models and leverage them to learn successful behaviors. While recent research, such as our
Deep Planning Network
(PlaNet), has pushed these boundaries by learning accurate world models from images, model-based approaches have still been held back by ineffective or computationally expensive planning mechanisms, limiting their ability to solve difficult tasks.
Today, in collaboration with
DeepMind
, we present
Dreamer
, an RL agent that learns a world model from images and uses it to learn long-sighted behaviors. Dreamer leverages its world model to efficiently learn behaviors via
backpropagation
through model predictions. By learning to compute
compact model states
from raw images, the agent is able to efficiently learn from thousands of predicted sequences in parallel using just one GPU. Dreamer achieves a new state-of-the-art in performance, data efficiency and computation time on a benchmark of 20 continuous control tasks given raw image inputs. To stimulate further advancement of RL, we are releasing the
source code
to the research community.
How Does Dreamer Work?
Dreamer consists of three processes that are
typical
for model-based methods: learning the world model, learning behaviors from predictions made by the world model, and executing its learned behaviors in the environment to collect new experience. To learn behaviors, Dreamer uses a value network to take into account rewards beyond the planning horizon and an actor network to efficiently compute actions. The three processes, which can be executed in parallel, are repeated until the agent has achieved its goals:
The three processes of the Dreamer agent. The world model is learned from past experience. From predictions of this model, the agent then learns a value network to predict future rewards and an actor network to select actions. The actor network is used to interact with the environment.
Learning the World Model
Dreamer leverages the
PlaNet
world model, which predicts outcomes based on a sequence of
compact model states
that are computed from the input images, instead of directly predicting from one image to the next. It automatically learns to produce model states that represent concepts helpful for predicting future outcomes, such as object types, positions of objects, and the interaction of the objects with their surroundings. Given a sequence of images, actions, and rewards from the agent's dataset of past experience, Dreamer learns the world model as shown:
Dreamer learns a world model from experience. Using past images (o
1
–o
3
) and actions (a
1
–a
2
), it computes a sequence of compact model states (green circles) from which it reconstructs the images (ô
1
–ô
3
) and predicts the rewards (r̂
1
–r̂
3
).
An advantage to using the PlaNet world model is that predicting ahead using compact model states instead of images greatly improves the computational efficiency. This enables the model to predict thousands of sequences in parallel on a single GPU. The approach can also facilitate generalization, leading to accurate long-term video predictions. To gain insights into how the model works, we can visualize the predicted sequences by decoding the compact model states back into images, as shown below for a task of the
DeepMind Control Suite
and for a task of the
DeepMind Lab
environment:
Predicting ahead using compact model states enables long-term predictions in complex environments. Shown here are two sequences that the agent has not encountered before. Given five input images, the model reconstructs them and predicts the future images up to time step 50.
Efficient Behavior Learning
Previously developed model-based agents typically select actions either by planning through many model predictions or by using the world model in place of a simulator to reuse existing model-free techniques. Both designs are computationally demanding and do not fully leverage the learned world model. Moreover, even powerful world models are limited in how far ahead they can accurately predict, rendering many previous model-based agents shortsighted. Dreamer overcomes these limitations by learning a value network and an actor network via backpropagation through predictions of its world model.
Dreamer efficiently learns the actor network to predict successful actions by propagating gradients of rewards backwards through predicted state sequences, which is not possible for model-free approaches. This tells Dreamer how small changes to its actions affect what rewards are predicted in the future, allowing it to refine the actor network in the direction that increases the rewards the most. To consider rewards beyond the prediction horizon, the value network estimates the sum of future rewards for each model state. The rewards and values are then backpropagated to refine the actor network to select improved actions:
Dreamer learns long-sighted behaviors from predicted sequences of model states. It first learns the long-term value (v̂
2
–v̂
3
) of each state, and then predicts actions (â
1
–â
2
) that lead to high rewards and values by backpropagating them through the state sequence to the actor network.
Dreamer differs from PlaNet in several ways. For a given situation in the environment, PlaNet searches for the best action among many predictions for different action sequences. In contrast, Dreamer side-steps this expensive search by decoupling planning and acting. Once its actor network has been trained on predicted sequences, it computes the actions for interacting with the environment without additional search. In addition, Dreamer considers rewards beyond the planning horizon using a value function and leverages backpropagation for efficient planning.
Performance on Control Tasks
We evaluated Dreamer on a standard benchmark of 20 diverse tasks with continuous actions and image inputs. The tasks include balancing and catching objects, as well as locomotion of various simulated robots. The tasks are designed to pose a variety of challenges to the RL agent, including difficult to predict collisions, sparse rewards, chaotic dynamics, small but relevant objects, high degrees of freedom, and 3D perspectives:
Dreamer learns to solve 20 challenging continuous control tasks with image inputs, 5 of which are displayed here. The visualizations show the same 64x64 images that the agent receives from the environment.
We compare the performance of Dreamer to that of
PlaNet
, the previous best model-based agent, the popular model-free agent,
A3C
, as well as the current best model-free agent on this benchmark,
D4PG
, which combines several advances of model-free RL. The model-based agents learn efficiently in under 5 million frames, corresponding to 28 hours inside the simulation. The model-free agents learn more slowly and require 100 million frames, corresponding to 23 days inside the simulation.
On the benchmark of 20 tasks, Dreamer outperforms the best model-free agent (D4PG) with an average score of 823 compared to 786, while learning from 20 times fewer environment interactions. Moreover, it exceeds the final performance of the previously best model-based agent (PlaNet) across almost all of the tasks. The computation time of 16 hours for training Dreamer is less than the 24 hours required for the other methods. The final performance of the four agents is shown below:
Dreamer outperforms the previous best model-free (
D4PG
) and model-based (
PlaNet
) methods on the benchmark of 20 tasks in terms of final performance, data efficiency, and computation time.
In addition to our main experiments on continuous control tasks, we demonstrate the generality of Dreamer by applying it to tasks with discrete actions. For this, we select Atari games and DeepMind Lab levels that require both reactive and long-sighted behavior, spatial awareness, and understanding of visually more diverse scenes. The resulting behaviors are visualized below, showing that Dreamer also efficiently learns to solve these more challenging tasks:
Dreamer learns successful behaviors on Atari games and DeepMind Lab levels, which feature discrete actions and visually more diverse scenes, including 3D environments with multiple objects.
Conclusion
Our work demonstrates that learning behaviors from sequences predicted by world models alone can solve challenging visual control tasks from image inputs, surpassing the performance of previous model-free approaches. Moreover, Dreamer demonstrates that learning behaviors by backpropagating value gradients through predicted sequences of compact model states is successful and robust, solving a diverse collection of continuous and discrete control tasks. We believe that Dreamer offers a strong foundation for further pushing the limits of reinforcement learning, including better representation learning, directed exploration with uncertainty estimates, temporal abstraction, and multi-task learning.
Acknowledgements
This project is a collaboration with Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi. We further thank everybody in the Brain Team and beyond who commented on our paper draft and provided feedback at any point throughout the project.
Fast and Easy Infinitely Wide Networks with Neural Tangents
viernes, 13 de marzo de 2020
Posted by Samuel S. Schoenholz, Senior Research Scientist and Roman Novak, Research Engineer, Google Research
The widespread success of deep learning across a range of domains such as
natural language processing
,
conversational agents
, and
connectomics
, has transformed the landscape of research in machine learning and left researchers with a number of interesting and important open questions such as: Why do deep neural networks (DNNs) generalize so well despite being overparameterized? What is the relationship between architecture, training, and performance for deep networks? How can one extract salient features from deep learning models?
One of the key theoretical insights that has allowed us to make progress in recent years has been that increasing the width of DNNs results in more regular behavior, and makes them
easier
to understand. A number of recent results have shown that DNNs that are allowed to become infinitely wide
converge to
another, simpler, class of models called
Gaussian processes
. In this limit, complicated phenomena (like
Bayesian inference
or
gradient descent
dynamics
of a
convolutional neural network
) boil down to simple linear algebra equations. Insights from these
infinitely wide networks
frequently carry over to their finite counterparts. As such, infinite-width networks can be used as a lens to study deep learning, but also as
useful models in their own right
.
Left:
A schematic showing how deep neural networks induce simple input / output maps as they become infinitely wide.
Right:
As the width of a neural network increases , we see that the distribution of outputs over different random instantiations of the network becomes Gaussian.
Unfortunately, deriving the infinite-width limit of a finite network requires significant mathematical expertise and has to be worked out separately for each architecture studied. Once the infinite-width model is derived, coming up with an efficient and scalable implementation further requires significant engineering proficiency. Together, the process of taking a finite-width model to its corresponding infinite-width network could take months and might be the topic of a research paper in its own right.
To address this issue and to accelerate theoretical progress in deep learning, we present
Neural Tangents
, a
new open-source software library
written in
JAX
that allows researchers to build and train infinitely wide neural networks as easily as finite neural networks. At its core, Neural Tangents provides an easy-to-use neural network library that builds finite- and infinite-width versions of neural networks simultaneously.
As an example of the utility of Neural Tangents, imagine training a fully-connected neural network on some data. Normally, a neural network is randomly initialized and then trained using gradient descent. Initializing and training many of these neural networks results in an
ensemble.
Often researchers and practitioners average the predictions from different members of the ensemble together for better performance. Additionally, the variance in the predictions of members of the ensemble can be used to estimate uncertainty. The downside is that training an ensemble of networks requires a significant computational budget, so it is often avoided. However, when the neural networks become
infinitely wide
, the ensemble
is described by
a Gaussian process with a mean and variance that
can be computed throughout training
.
With Neural Tangents, one can construct and train
ensembles
of these infinite-width networks
at once using only five lines of code! The resulting training process is displayed below, and an interactive colaboratory notebook going through this experiment can
be found here
.
In both plots we compare training of an ensemble of finite neural networks with the infinite-width ensemble of the same architecture. The empirical mean and variance of the finite ensemble is displayed as a dashed black line between two dotted black lines. The
closed-form
mean and variance of the infinite-width ensemble is displayed as a solid colored line inside a filled color region. In both plots finite- and infinite-width ensembles match very closely and can be hard to distinguish.
Left:
Outputs (vertical f-axis) on the input data (horizontal x-axis) as the training progresses.
Right:
Train and test loss with uncertainty over the course of training.
Despite the fact that the infinite-width ensemble is governed by a simple closed-form expression, it exhibits remarkable agreement with the finite-width ensemble. And since the infinite-width ensemble is a Gaussian process, it naturally provides closed-form uncertainty estimates (filled colored regions in the figure above). These uncertainty estimates closely match the variation of predictions that are observed when training many different copies of the finite network (dashed lines).
The above example shows the power of infinite-width neural networks to capture training dynamics. However, networks built using Neural Tangents can be applied to any problem on which you could apply a regular neural network. For example, below we compare three different infinite-width neural network architectures on image recognition using the
CIFAR-10
dataset. Remarkably, we can evaluate ensembles of highly-elaborate models like infinitely
wide residual networks
in closed-form under both gradient descent and fully-Bayesian inference (an intractable task in the finite-width regime).
We see that, mimicking finite neural networks, infinite-width networks follow a similar hierarchy of performance with fully-connected networks performing worse than convolutional networks, which in turn perform worse than wide residual networks. However, unlike regular training, the learning dynamics of these models is completely tractable in closed-form, which allows unprecedented insight into their behavior.
We invite everyone to explore the infinite-width versions of their models with Neural Tangents, and help us open the black box of deep learning. To get started, please check out the
paper
, the
tutorial Colab notebook
, and the
Github repo
— contributions, feature requests, and bug reports are very welcome. This work has been accepted as a spotlight at
ICLR 2020
.
Acknowledgements
Neural Tangents is being actively developed by Lechao Xiao, Roman Novak, Jiri Hron, Jaehoon Lee, Alex Alemi, Jascha Sohl-Dickstein, and Samuel S. Schoenholz. We also thank Yasaman Bahri and Greg Yang for the ongoing contributions to improve the library, as well as Sergey Ioffe, Ben Adlam, Ravid Ziv, and Jeffrey Pennington for frequent discussion and useful feedback. Finally, we thank Tom Small for creating the animation in the first figure.
Soli Radar-Based Perception and Interaction in Pixel 4
jueves, 12 de marzo de 2020
Posted by Jaime Lien, Research Engineer and Nicholas Gillian, Software Engineer, Google Advanced Technology and Projects
The Pixel 4 and Pixel 4 XL are optimized for ease of use, and a key feature helping to realize this goal is
Motion Sense
, which enables users to
interact with their Pixel
in numerous ways without touching the device. For example, with Motion Sense you can use specific gestures to change music tracks or instantly silence an incoming call. Motion Sense additionally detects when you're near your phone and when you reach for it, allowing your Pixel to be more helpful by anticipating your actions, such as by priming the camera to provide a seamless
face unlock
experience, politely lowering the volume of a ringing alarm as you reach to dismiss it, or turning off the display to save power when you’re no longer near the device.
The technology behind Motion Sense is
Soli
, the first integrated short-range
radar
sensor in a consumer smartphone, which facilitates close-proximity interaction with the phone without contact. Below, we discuss Soli’s core radar sensing principles, design of the signal processing and machine learning (ML) algorithms used to recognize human activity from radar data, and how we resolved some of the integration challenges to prepare Soli for use in consumer devices.
Designing the Soli Radar System for Motion Sense
The basic function of radar is to detect and measure properties of remote objects based on their interactions with radio waves. A classic radar system includes a transmitter that emits radio waves, which are then scattered, or redirected, by objects within their paths, with some portion of energy reflected back and intercepted by the radar receiver. Based on the received waveforms, the radar system can detect the presence of objects as well as estimate certain properties of these objects, such as distance and size.
Radar has been under active development as a detection and ranging technology for
almost a century
. Traditional radar approaches are designed for detecting large, rigid, distant objects, such as planes and cars; therefore, they lack the sensitivity and resolution for sensing complex motions within the requirements of a consumer handheld device. Thus, to enable Motion Sense, the Soli team developed a new, small-scale radar system, novel sensing paradigms, and algorithms
from the ground up
specifically for fine-grained perception of human interactions.
Classic radar designs rely on fine spatial resolution relative to target size in order to resolve different objects and distinguish their spatial structures. Such spatial resolution typically requires broad transmission
bandwidth
, narrow antenna
beamwidth
, and large antenna arrays. Soli, on the other hand, employs a fundamentally different sensing paradigm based on motion, rather than spatial structure. Because of this novel paradigm, we were able to fit Soli’s entire antenna array for Pixel 4 on a 5 mm x 6.5 mm x 0.873 mm chip package, allowing the radar to be integrated in the top of the phone. Remarkably, we developed algorithms that specifically do not require forming a well-defined image of a target’s spatial structure, in contrast to an optical imaging sensor, for example. Therefore, no distinguishable images of a person’s body or face are generated or used for Motion Sense presence or gesture detection.
Soli’s location in Pixel 4.
Soli relies on processing temporal changes in the received signal in order to detect and resolve subtle motions. The Soli radar transmits a 60 GHz
frequency-modulated
signal and receives a superposition of reflections off of nearby objects or people. A sub-millimeter-scale displacement in a target’s position from one transmission to the next induces a distinguishable timing shift in the received signal. Over a window of multiple transmissions, these shifts manifest as a
Doppler frequency
that is proportional to the object’s velocity. By resolving different Doppler frequencies, the Soli signal processing pipeline can distinguish objects moving with different motion patterns.
The animations below demonstrate how different actions exhibit distinctive motion features in the processed Soli signal. The vertical axis of each image represents range, or radial distance, from the sensor, increasing from top to bottom. The horizontal axis represents velocity toward or away from the sensor, with zero at the center, negative velocities corresponding to approaching targets on the left, and positive velocities corresponding to receding targets on the right. Energy received by the radar is mapped into these range-velocity dimensions and represented by the intensity of each pixel. Thus, strongly reflective targets tend to be brighter relative to the surrounding noise floor compared to weakly reflective targets. The distribution and trajectory of energy within these range-velocity mappings show clear differences for a person walking, reaching, and swiping over the device.
In the left image, we see reflections from multiple body parts appearing on the negative side of the velocity axis as the person approaches the device, then converging at zero velocity at the top of the image as the person stops close to the device. In the middle image depicting a reach, a hand starts from a stationary position 20 cm from the sensor, then accelerates with negative velocity toward the device, and finally decelerates to a stop as it reaches the device. The reflection corresponding to the hand moves from the middle to the top of the image, corresponding to the hand’s decreasing range from the sensor over the course of the gesture. Finally, the third image shows a hand swiping over the device, moving with negative velocity toward the sensor on the left half of the velocity axis, passing directly over the sensor where its radial velocity is zero, and then away from the sensor on the right half of the velocity axis, before reaching a stop on the opposite side of the device.
Left
:
Presence
- Person walking towards the device.
Middle
:
Reach
- Person reaching towards the device.
Right
:
Swipe
- Person swiping over the device.
The 3D position of each resolvable reflection can also be estimated by processing the signal received at each of Soli’s three receivers; this positional information can be used in addition to range and velocity for target differentiation.
The signal processing pipeline we designed for Soli includes a combination of custom filters and coherent integration steps that boost signal-to-noise ratio, attenuate unwanted interference, and differentiate reflections off a person from noise and clutter. These signal processing features enable Soli to operate at low-power within the constraints of a consumer smartphone.
Designing Machine Learning Algorithms for Radar
After using Soli’s signal processing pipeline to filter and boost the original radar signal, the resulting signal transformations are fed to Soli’s ML models for gesture classification. These models have been trained to accurately detect and recognize the Motion Sense gestures with low latency.
There are two major research challenges to robustly classifying in-air gestures that are common to any motion sensing technology. The first is that every user is unique and performs even simple motions, such as a swipe, in a myriad of ways. The second is that throughout the day, there may be numerous extraneous motions within the range of the sensor that may appear similar to target gestures. Furthermore, when the phone moves, the whole world looks like it’s moving from the point of view of the motion sensor in the phone.
Solving these challenges required designing custom ML algorithms optimized for low-latency detection of in-air gestures from radar signals. Soli’s ML models consist of neural networks trained using millions of gestures recorded from thousands of Google volunteers. These radar recordings were mixed with hundreds of hours of background radar recordings from other Google volunteers containing generic motions made near the device. Soli’s ML models were trained using
TensorFlow
and optimized to run directly on Pixel’s low-power
digital signal processor
(DSP). This allows us to run the models at low power, even when the main application processor is powered down.
Taking Soli from Concept to Product
Soli’s integration into the Pixel smartphone was possible because the end-to-end radar system — including hardware, software, and algorithms — was carefully designed to enable touchless interaction within the size and power constraints of consumer devices. Soli’s miniature hardware allowed the full radar system to fit into the limited space in Pixel’s upper bezel, which was a significant team accomplishment. Indeed, the first Soli prototype in 2014 was the size of a desktop computer. We combined hardware innovations with our novel temporal sensing paradigm described earlier in order to shrink the entire radar system down to a single 5.0 mm x 6.5 mm
RFIC
, including antennas on package. The Soli team also introduced several innovative hardware power management schemes and optimized Soli’s compute cycles, enabling Motion Sense to fit within the power budget of the smartphone.
Hardware innovations included iteratively shrinking the radar system from a desktop-sized prototype to a single 5.0 mm x 6.5 mm RFIC, including antennas on package.
For integration into Pixel, the radar system team collaborated closely with product design engineers to preserve Soli signal quality. The chip placement within the phone and the z-stack of materials above the chip were optimized to maximize signal transmission through the glass and minimize reflections and occlusions from surrounding components. The team also invented custom signal processing techniques to enable coexistence with surrounding phone components. For example, a novel filter was developed to reduce the impact of audio vibration on the radar signal, enabling gesture detection while music is playing. Such algorithmic innovations enabled Motion Sense features across a variety of common user scenarios.
Vibration due to audio on Pixel 4 appearing as an artifact in Soli’s range-doppler signal representation.
Future Directions
The successful integration of Soli into Pixel 4 and Pixel 4 XL devices demonstrates for the first time the feasibility of radar-based machine perception in an everyday mobile consumer device. Motion Sense in Pixel devices shows Soli’s potential to bring seamless context awareness and gesture recognition for explicit and implicit interaction. We are excited to continue researching and developing Soli to enable new radar-based sensing and perception capabilities.
Acknowledgments
The work described above was a collaborative effort between Google
Advanced Technology and Projects
(ATAP) and the Pixel and Android product teams. We particularly thank Patrick Amihood for major contributions to this blog post.
Real-Time 3D Object Detection on Mobile Devices with MediaPipe
miércoles, 11 de marzo de 2020
Posted by Adel Ahmadyan and Tingbo Hou, Software Engineers, Google Research
Object detection is an extensively studied computer vision problem, but most of the research has focused on
2D object prediction
. While 2D prediction only provides 2D bounding boxes, by extending prediction to 3D, one can capture an object’s size, position and orientation in the world, leading to a variety of applications in robotics, self-driving vehicles, image retrieval, and augmented reality. Although 2D object detection is relatively mature and has been widely used in the industry, 3D object detection from 2D imagery is a challenging problem, due to the lack of data and diversity of appearances and shapes of objects within a category.
Today, we are announcing the release of MediaPipe Objectron, a mobile real-time 3D object detection pipeline for everyday objects. This pipeline detects objects in 2D images, and estimates their poses and sizes through a machine learning (ML) model, trained on a newly created 3D dataset. Implemented in
MediaPipe
, an open-source cross-platform framework for building pipelines to process perceptual data of different modalities, Objectron computes oriented 3D bounding boxes of objects in real-time on mobile devices.
3D Object Detection from a single image. MediaPipe Objectron determines the position, orientation and size of everyday objects in real-time on mobile devices.
Obtaining Real-World 3D Training Data
While there are ample amounts of 3D data for street scenes, due to the popularity of research into self-driving cars that rely on 3D capture sensors like
LIDAR
, datasets with ground truth 3D annotations for more granular everyday objects are extremely limited. To overcome this problem, we developed a novel data pipeline using mobile
augmented reality
(AR) session data. With the arrival of
ARCore
and
ARKit
,
hundreds of millions
of smartphones now have AR capabilities and the ability to capture additional information during an AR session, including the camera pose, sparse
3D point clouds
, estimated lighting, and planar surfaces.
In order to label ground truth data, we built a novel annotation tool for use with AR session data, which allows annotators to quickly label 3D bounding boxes for objects. This tool uses a split-screen view to display 2D video frames on which are overlaid 3D bounding boxes on the left, alongside a view showing 3D point clouds, camera positions and detected planes on the right. Annotators draw 3D bounding boxes in the 3D view, and verify its location by reviewing the projections in 2D video frames. For static objects, we only need to annotate an object in a single frame and propagate its location to all frames using the ground truth camera pose information from the AR session data, which makes the procedure highly efficient.
Real-world data annotation for 3D object detection.
Right:
3D bounding boxes are annotated in the 3D world with detected surfaces and point clouds.
Left:
Projections of annotated 3D bounding boxes are overlaid on top of video frames making it easy to validate the annotation.
AR Synthetic Data Generation
A popular approach is to complement real-world data with synthetic data in order to increase the accuracy of prediction. However, attempts to do so often yield poor, unrealistic data or, in the case of photorealistic rendering, require significant effort and compute. Our novel approach, called AR Synthetic Data Generation, places virtual objects into scenes that have AR session data, which allows us to leverage camera poses, detected planar surfaces, and estimated lighting to generate placements that are physically probable and with lighting that matches the scene. This approach results in high-quality synthetic data with rendered objects that respect the scene geometry and fit seamlessly into real backgrounds. By combining real-world data and AR synthetic data, we are able to increase the accuracy by about 10%.
An example of AR synthetic data generation. The virtual white-brown cereal box is rendered into the real scene, next to the real blue book.
An ML Pipeline for 3D Object Detection
We
built a single-stage model
to predict the pose and physical size of an object from a single RGB image. The model backbone has an encoder-decoder architecture, built upon
MobileNetv2
. We employ a multi-task learning approach, jointly predicting an object's shape with detection and regression. The shape task predicts the object's shape signals depending on what ground truth annotation is available, e.g. segmentation. This is optional if there is no shape annotation in training data. For the detection task, we use the annotated bounding boxes and fit a Gaussian to the box, with center at the box centroid, and standard deviations proportional to the box size. The goal for detection is then to predict this distribution with its peak representing the object’s center location. The regression task estimates the 2D projections of the eight bounding box vertices. To obtain the final 3D coordinates for the bounding box, we leverage a well established pose estimation algorithm (
EPnP
). It can recover the 3D bounding box of an object, without
a priori
knowledge of the object dimensions. Given the 3D bounding box, we can easily compute pose and size of the object. The diagram below shows our network architecture and post-processing. The model is light enough to run real-time on mobile devices (at 26 FPS on an
Adreno 650 mobile GPU
).
Network architecture and post-processing for 3D object detection.
Sample results of our network — [
left
] original 2D image with estimated bounding boxes, [
middle
] object detection by Gaussian distribution, [
right
] predicted segmentation mask.
Detection and Tracking in MediaPipe
When the model is applied to every frame captured by the mobile device, it can suffer from jitter due to the ambiguity of the 3D bounding box estimated in each frame. To mitigate this, we adopt the detection+tracking framework recently released in our
2D object detection and tracking
solution. This framework mitigates the need to run the network on every frame, allowing the use of heavier and therefore more accurate models, while keeping the pipeline real-time on mobile devices. It also retains object identity across frames and ensures that the prediction is temporally consistent, reducing the jitter.
For further efficiency in our mobile pipeline, we run our model inference only once every few frames. Next, we take the prediction and track it over time using the approach described in our previous blogs for
instant motion tracking
and
Motion Stills
. When a new prediction is made, we consolidate the detection result with the tracking result based on the area of overlap.
To encourage researchers and developers to experiment and prototype based on our pipeline, we are releasing our on-device ML pipeline in
MediaPipe
, including an
end-to-end demo mobile application
and our trained models for two categories:
shoes
and
chairs
. We hope that sharing our solution with the wide research and development community will stimulate new use cases, new applications, and new research efforts. In the future, we plan to scale our model to many more categories, and further improve our on-device performance.
Examples of our 3D object detection in the wild.
Acknowledgements
The research described in this post was done by Adel Ahmadyan, Tingbo Hou, Jianing Wei, Matthias Grundmann, Liangkai Zhang, Jiuqiang Tang, Chris McClanahan, Tyler Mullen, Buck Bourdon, Esha Uboweja, Mogan Shieh, Siarhei Kazakou, Ming Guang Yong, Chuo-Ling Chang, and James Bruce. We thank Aliaksandr Shyrokau and the annotation team for their diligence to high quality annotations.
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