Blog
The latest news from Google AI
Exploring Nature-Inspired Robot Agility
Friday, April 3, 2020
Posted by Xue Bin (Jason) Peng, Student Researcher and Sehoon Ha, Research Scientist, Robotics at Google
Whether it’s a dog chasing after a ball or a horse jumping over obstacles, animals can effortlessly perform an incredibly rich repertoire of agile skills. Developing robots that are able to replicate these agile behaviors can open opportunities to deploy robots for sophisticated tasks in the real world. But designing controllers that enable legged robots to perform these agile behaviors can be a very challenging task. While
reinforcement learning
(RL) is an approach often used for automating development of robotic skills, a number of technical hurdles remain and, in practice, there is still substantial manual overhead. Designing reward functions that lead to effective skills can itself require a great deal of expert insight, and often involves a lengthy reward tuning process for each desired skill. Furthermore, applying RL to legged robots requires not only efficient algorithms, but also mechanisms to enable the robots to remain safe and recover after falling, without frequent human assistance.
In this post, we will discuss two of our recent projects aimed at addressing these challenges. First, we describe how robots can learn agile behaviors by imitating motions from real animals, producing fast and fluent movements like trotting and hopping. Then, we discuss a system for automating the training of locomotion skills in the real world, which allows robots to learn to walk on their own, with minimal human assistance.
Learning Agile Robotic Locomotion Skills by Imitating Animals
In “
Learning Agile Robotic Locomotion Skills by Imitating Animals
”, we present a framework that takes a reference motion clip recorded from an animal (a dog, in this case) and uses RL to train a control policy that enables a robot to imitate the motion in the real world. By providing the system with different reference motions, we are able to train a quadruped robot to perform a diverse set of agile behaviors, ranging from fast walking gaits to dynamic hops and turns. The policies are trained primarily in simulation, and then transferred to the real world using a latent space adaptation technique that can efficiently adapt a policy using only a few minutes of data from the real robot.
Motion Imitation
We start by collecting motion capture clips of a real dog performing various locomotion skills. Then, we use RL to train a control policy to imitate the dog’s motions. The policies are trained in a physics simulation to track the pose of the reference motion at each timestep. Then, by using different reference motions in the reward function, we can train a simulated robot to imitate a variety of different skills.
Reinforcement learning is used to train a simulated robot to imitate the reference motions from a dog. All simulations are performed using
PyBullet
.
However, since simulators generally provide only a coarse approximation of the real world, policies trained in simulation often perform poorly when deployed on a real robot. Therefore, we use a sample-efficient latent space adaptation technique to transfer a policy trained in simulation to the real world.
First, to encourage the policy to learn behaviors that are robust to variations in the dynamics, we randomize the dynamics of the simulation by varying physical quantities, such as the robot’s mass and friction. Since we have access to the values of these parameters during training in simulation, we can also map them to a low-dimensional representation using a learned encoder. This encoding is then passed as an additional input to the policy during training. Since the physical parameters of the real robot are not known
a priori
, when deploying the policy to a real robot, we remove the encoder and directly search for a set of parameters in the latent space that enables the robot to successfully execute the desired skills in the real world. This technique is often able to adapt a policy to the real world using less than 8 minutes of real-world data.
Comparison of policies before and after adaptation on the real robot. Before adaptation, the robot is prone to falling. But after adaptation, the policies are able to more consistently execute the desired skills.
Results
Using this approach, the robot learns to imitate various locomotion skills from a dog, including different walking gaits, such as pacing and trotting, as well as an agile spinning motion.
Robot imitating various skills from a dog.
In addition to imitating motions from real dogs, it is also possible to imitate artist-animated keyframe motions, including a dynamic hop-turn:
Skills learned by imitating artist-animated keyframe motions: side-steps, turn, and hop-turn.
More details are available in the following video:
Learning to Walk in the Real World with Minimal Human Effort
The above approach is able to train policies in simulation and then adapt them to the real world. However, when the task involves complex and diverse physical phenomena, it is also necessary to directly learn from real-world experience. Although learning on real robots has achieved state-of-the-art performance for manipulation tasks (e.g.,
QT-Opt
), applying the same methods to legged robots is difficult since the robot may fall and damage itself, or leave the training area, which can then require human intervention.
An automated learning system for legged robots must resolve safety and automation challenges.
In “
Learning to Walk in the Real World with Minimal Human Effort
”, we developed an automated learning system with software and hardware components, using a multi-task learning procedure, a safety-constrained learner, and several carefully designed hardware and software components. Multi-task learning prevents the robot from leaving the training area by generating a learning schedule that drives the robot towards the center of the workspace. We also reduce the number of falls by designing a safety constraint, which we solve with dual gradient descent.
For each roll-out, the scheduler selects a task in which the desired walking direction is pointing towards the center. For instance, assuming we have two tasks, forward and backward walking, the scheduler will select the forward task if the robot is at the back of the workspace, and vice-versa for the backward task. In the middle of the episode, the learner takes dual gradient descent steps to iteratively optimize both the task objective and safety constraints, rather than treating them as a single goal. If the robot has fallen, we invoke an automated get-up controller and proceed to the next episode.
We solve automation and safety challenges with multi-task learning, a safety-constrained SAC algorithm, and an automatic reset controller.
Results
This framework successfully trains policies from scratch to walk in different directions without
any human intervention
.
Snapshots of the training process on the flat surface with zero human resets.
Once trained, it is possible to steer the robot with a remote controller. Notice how it's possible to command the robot to turn in place using the controller. This action would be difficult to manually design due to the planar leg structure of the robot, but is discovered automatically using our automated multi-instance learner.
We train locomotion policies to walk in four directions, which allow us to interactively control the robot with a game controller.
The system also enables the robot to navigate more challenging surfaces, such as a memory foam mattress and a doormat with crevices.
Learned locomotion gaits on challenging terrains.
More details can be found in the following video:
Conclusion
In these two papers, we present methods to reproduce a diverse corpus of behaviors with quadruped robots. Extending this line of work to learn
skills from videos
would also be an exciting direction, which can substantially increase the volume of data from which robots can learn. We are also interested in applying the automated training system to more complex real-world environments and tasks.
Acknowledgments
We would like to thank our coauthors, Erwin Coumans, Tingnan Zhang, Tsang-Wei Lee, Jie Tan, Sergey Levine, Peng Xu and Zhenyu Tan. We would also like to thank Julian Ibarz, Byron David, Thinh Nguyen, Gus Kouretas, Krista Reymann, and Bonny Ho for their support and contributions to this work.
Labels
accessibility
ACL
ACM
Acoustic Modeling
Adaptive Data Analysis
ads
adsense
adwords
Africa
AI
AI for Social Good
Algorithms
Android
Android Wear
API
App Engine
App Inventor
April Fools
Art
Audio
Augmented Reality
Australia
Automatic Speech Recognition
AutoML
Awards
BigQuery
Cantonese
Chemistry
China
Chrome
Cloud Computing
Collaboration
Compression
Computational Imaging
Computational Photography
Computer Science
Computer Vision
conference
conferences
Conservation
correlate
Course Builder
crowd-sourcing
CVPR
Data Center
Data Discovery
data science
datasets
Deep Learning
DeepDream
DeepMind
distributed systems
Diversity
Earth Engine
economics
Education
Electronic Commerce and Algorithms
electronics
EMEA
EMNLP
Encryption
entities
Entity Salience
Environment
Europe
Exacycle
Expander
Faculty Institute
Faculty Summit
Flu Trends
Fusion Tables
gamification
Gboard
Gmail
Google Accelerated Science
Google Books
Google Brain
Google Cloud Platform
Google Docs
Google Drive
Google Genomics
Google Maps
Google Photos
Google Play Apps
Google Science Fair
Google Sheets
Google Translate
Google Trips
Google Voice Search
Google+
Government
grants
Graph
Graph Mining
Hardware
HCI
Health
High Dynamic Range Imaging
ICCV
ICLR
ICML
ICSE
Image Annotation
Image Classification
Image Processing
Inbox
India
Information Retrieval
internationalization
Internet of Things
Interspeech
IPython
Journalism
jsm
jsm2011
K-12
Kaggle
KDD
Keyboard Input
Klingon
Korean
Labs
Linear Optimization
localization
Low-Light Photography
Machine Hearing
Machine Intelligence
Machine Learning
Machine Perception
Machine Translation
Magenta
MapReduce
market algorithms
Market Research
Mixed Reality
ML
ML Fairness
MOOC
Moore's Law
Multimodal Learning
NAACL
Natural Language Processing
Natural Language Understanding
Network Management
Networks
Neural Networks
NeurIPS
Nexus
Ngram
NIPS
NLP
On-device Learning
open source
operating systems
Optical Character Recognition
optimization
osdi
osdi10
patents
Peer Review
ph.d. fellowship
PhD Fellowship
PhotoScan
Physics
PiLab
Pixel
Policy
Professional Development
Proposals
Public Data Explorer
publication
Publications
Quantum AI
Quantum Computing
Recommender Systems
Reinforcement Learning
renewable energy
Research
Research Awards
resource optimization
Robotics
schema.org
Search
search ads
Security and Privacy
Self-Supervised Learning
Semantic Models
Semi-supervised Learning
SIGCOMM
SIGMOD
Site Reliability Engineering
Social Networks
Software
Sound Search
Speech
Speech Recognition
statistics
Structured Data
Style Transfer
Supervised Learning
Systems
TensorBoard
TensorFlow
TPU
Translate
trends
TTS
TV
UI
University Relations
UNIX
Unsupervised Learning
User Experience
video
Video Analysis
Virtual Reality
Vision Research
Visiting Faculty
Visualization
VLDB
Voice Search
Wiki
wikipedia
WWW
Year in Review
YouTube
Archive
2021
Jun
May
Apr
Mar
Feb
Jan
2020
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2019
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2018
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2017
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2016
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2015
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2014
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2013
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2012
Dec
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2011
Dec
Nov
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2010
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2009
Dec
Nov
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2008
Dec
Nov
Oct
Sep
Jul
May
Apr
Mar
Feb
2007
Oct
Sep
Aug
Jul
Jun
Feb
2006
Dec
Nov
Sep
Aug
Jul
Jun
Apr
Mar
Feb
Feed
Follow @googleai
Give us feedback in our
Product Forums
.
