DeepAI AI Chat
Log In Sign Up

Data Efficient Reinforcement Learning for Legged Robots

07/08/2019
by   Yuxiang Yang, et al.
Google
6

We present a model-based framework for robot locomotion that achieves walking based on only 4.5 minutes (45,000 control steps) of data collected on a quadruped robot. To accurately model the robot's dynamics over a long horizon, we introduce a loss function that tracks the model's prediction over multiple timesteps. We adapt model predictive control to account for planning latency, which allows the learned model to be used for real time control. Additionally, to ensure safe exploration during model learning, we embed prior knowledge of leg trajectories into the action space. The resulting system achieves fast and robust locomotion. Unlike model-free methods, which optimize for a particular task, our planner can use the same learned dynamics for various tasks, simply by changing the reward function. To the best of our knowledge, our approach is more than an order of magnitude more sample efficient than current model-free methods.

READ FULL TEXT
03/05/2022

Safe Reinforcement Learning for Legged Locomotion

Designing control policies for legged locomotion is complex due to the u...
02/17/2020

A Robust Model-Based Biped Locomotion Framework Based on Three-Mass Model: From Planning to Control

Biped robots are inherently unstable because of their complex kinematics...
02/28/2023

Model-Free and Learning-Free Proprioceptive Humanoid Movement Control

This paper presents a novel model-free method for humanoid-robot quasi-s...
10/07/2021

Evaluating model-based planning and planner amortization for continuous control

There is a widespread intuition that model-based control methods should ...
03/15/2020

Robot Playing Kendama with Model-Based and Model-Free Reinforcement Learning

Several model-based and model-free methods have been proposed for the ro...
09/27/2018

Adaptive Tensegrity Locomotion on Rough Terrain via Reinforcement Learning

The dynamical properties of tensegrity robots give them appealing rugged...
03/19/2020

Learning to Fly via Deep Model-Based Reinforcement Learning

Learning to control robots without requiring models has been a long-term...