Krishna, L and Castillo, GA and Mishra, UA and Hereid, A and Kolathaya, S (2022) Linear Policies are Sufficient to Realize Robust Bipedal Walking on Challenging Terrains. In: IEEE Robotics and Automation Letters, 7 (2). pp. 2047-2054.
|
PDF
IEEE_rob_aut_let_7-2_2047-2054_2022.pdf - Published Version Download (3MB) | Preview |
Abstract
In this work, we demonstrate robust walking in the bipedal robot Digit on uneven terrains by just learning a single linear policy. In particular, we propose a new control pipeline, wherein the high-level trajectory modulator shapes the end-foot ellipsoidal trajectories, and the low-level gait controller regulates the torso and ankle orientation. The foot-trajectory modulator uses a linear policy and the regulator uses a linear PD control law. As opposed to neural network based policies, the proposed linear policy has only 13 learnable parameters, thereby not only guaranteeing sample efficient learning but also enabling simplicity and interpretability of the policy. This is achieved with no loss of performance on challenging terrains like slopes, stairs and outdoor landscapes. We first demonstrate robust walking in the custom simulation environment, MuJoCo, and then directly transfer to hardware with no modification of the control pipeline. We subject the biped to a series of pushes and terrain height changes, both indoors and outdoors, thereby validating the presented work. 2377-3766 © 2022 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.
| Item Type: | Journal Article |
|---|---|
| Publication: | IEEE Robotics and Automation Letters |
| Publisher: | Institute of Electrical and Electronics Engineers Inc. |
| Additional Information: | The copyright for this article belongs to the Authors. |
| Keywords: | Biped locomotion; Intelligent robots; Pipelines; Reinforcement learning; Robotics, Bipedal-locomotion; Foot; Hip; Humanoid and bipedal locomotion; Legged locomotion; Legged robots; Machine learning for robot control; Machine-learning; Reinforcement learnings; Robot kinematics; Robots control; Torso, Trajectories |
| Department/Centre: | Division of Electrical Sciences > Computer Science & Automation |
| Date Deposited: | 21 Jun 2022 05:10 |
| Last Modified: | 21 Jun 2022 05:10 |
| URI: | https://eprints.iisc.ac.in/id/eprint/73722 |
Actions (login required)
 |
View Item |
