ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

Force control for Robust Quadruped Locomotion: A Linear Policy Approach

Shirwatkar, A and Kurva, VK and Vinoda, D and Singh, A and Sagi, A and Lodha, H and Goswami, BG and Sood, S and Nehete, K and Kolathaya, S (2023) Force control for Robust Quadruped Locomotion: A Linear Policy Approach. In: 2023 IEEE International Conference on Robotics and Automation, ICRA 2023, 29 May - 2 June 2023, London, pp. 5113-5119.

[img] PDF
IEEE-ICRA-2023_2023_5113-5119_2023.pdf - Published Version
Restricted to Registered users only

Download (3MB) | Request a copy
Official URL: https://doi.org/10.1109/ICRA48891.2023.10161080


This work presents a simple linear policy for direct force control for quadrupedal robot locomotion. The motivation is that force control is essential for highly dynamic and agile motions. We learn a linear policy to generate end-foot trajectory parameters and a centroidal wrench, which is then distributed among the legs based on the foot contact information using a quadratic program (QP) to get the desired ground reaction forces. Unlike the majority of the existing works that use complex nonlinear function approximators to represent the RL policy or model predictive control (MPC) methods with many optimization variables in the order of hundred, our controller uses a simple linear function approximator to represent policy along with only a twelve variable QP for the force distribution. A centroidal dynamics-based MPC method is used to generate reference trajectory data, and then the linear policy is trained using imitation learning to minimize the deviations from the reference trajectory. We demonstrate this compute-efficient controller on our robot Stoch3 in simulation and real-world experiments on indoor and outdoor terrains with push recovery. © 2023 IEEE.

Item Type: Conference Paper
Publication: Proceedings - IEEE International Conference on Robotics and Automation
Publisher: Institute of Electrical and Electronics Engineers Inc.
Additional Information: The copyright for this article belongs to the Institute of Electrical and Electronics Engineers Inc.
Keywords: Biped locomotion; Controllers; Force control; Navigation; Predictive control systems; Quadratic programming; Reinforcement learning; Robot programming; Trajectories, Function approximators; Linear policy; Model predictive control; Model-predictive control; Predictive control methods; Quadratic programs; Quadruped Robots; Reference trajectories; Reinforcement learnings; Simplest linear, Model predictive control
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Division of Interdisciplinary Sciences > Robert Bosch Centre for Cyber Physical Systems
Date Deposited: 05 Nov 2023 10:19
Last Modified: 05 Nov 2023 10:19
URI: https://eprints.iisc.ac.in/id/eprint/83092

Actions (login required)

View Item View Item