December 06, 2020
The current dominant paradigm in sensorimotor control, whether imitation or reinforcement learning, is to train policies directly in raw action spaces such as torque, joint angle, or end-effector position. This forces the agent to make decision at each point in training, and hence, limit the scalability to continuous, high-dimensional, and long-horizon tasks. In contrast, research in classical robotics has, for a long time, exploited dynamical systems as a policy representation to learn robot behaviors via demonstrations. These techniques, however, lack the flexibility and generalizability provided by deep learning or deep reinforcement learning and have remained under-explored in such settings. In this work, we begin to close this gap and embed dynamics structure into deep neural network-based policies by reparameterizing action spaces with differential equations. We propose Neural Dynamic Policies (NDPs) that make predictions in trajectory distribution space as opposed to prior policy learning methods where action represents the raw control space. The embedded structure allow us to perform end-to-end policy learning under both reinforcement and imitation learning setups. We show that NDPs achieve better or comparable performance to state-of-the-art approaches on many robotic control tasks using both reward-based training and demonstrations. Project video and code are available at: https://shikharbahl.github.io/ neural-dynamic-policies/
Written by
Deepak Pathak
Abhinav Gupta
Mustafa Mukadam
Shikhar Bahl
Publisher
NeurIPS
Research Topics
January 06, 2024
Geng Ji, Wentao Jiang, Jiang Li, Fahmid Morshed Fahid, Zhengxing Chen, Yinghua Li, Jun Xiao, Chongxi Bao, Zheqing (Bill) Zhu
January 06, 2024
December 11, 2023
Dishank Bansal, Ricky Chen, Mustafa Mukadam, Brandon Amos
December 11, 2023
October 26, 2023
Daniel Jiang
October 26, 2023
October 12, 2023
Christopher Paxton, Jay Vakil, Priyam Parashar, Sam Powers, Xiaohan Zhang, Yonatan Bisk, Vidhi Jain
October 12, 2023
Product experiences
Foundational models
Product experiences
Latest news
Foundational models