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

MELP: Model Embedded Linear Policies for Robust Bipedal Hopping

Soni, R and Castillo, GA and Krishna, L and Hereid, A and Kolathaya, S (2023) MELP: Model Embedded Linear Policies for Robust Bipedal Hopping. In: UNSPECIFIED, pp. 10418-10424.

[img] PDF
IEE_Int_Con_Int_Rob_Sys_2023.pdf - Published Version
Restricted to Registered users only

Download (4MB)
Official URL: https://doi.org/10.1109/IROS55552.2023.10342023


Linear policies are the simplest class of policies that can achieve stable bipedal walking behaviors in both simulation and hardware. However, a significant challenge in deploying them widely is the difficulty in extending them to more dynamic behaviors like hopping and running. Therefore, in this work, we propose a new class of linear policies in which template models can be embedded. In particular, we show how to embed Spring Loaded Inverted Pendulum (SLIP) model in the policy class and realize perpetual hopping in arbitrary directions. The spring constant of the template model is learned in addition to the remaining parameters of the policy. Given this spring constant, the goal is to realize hopping trajectories using the SLIP model, which are then tracked by the bipedal robot using the linear policy. Continuous hopping with adjustable heading direction was achieved across different terrains in simulation with heading and lateral velocities of up to O.5m/ sec and 0.05m/ sec, respectively. The policy was then transferred to the hardware, and preliminary results (> 10 steps) of hopping were achieved. © 2023 IEEE.

Item Type: Conference Paper
Publication: IEEE International Conference on Intelligent Robots and Systems
Publisher: Institute of Electrical and Electronics Engineers Inc.
Additional Information: The copyright for this article belongs to publisher
Keywords: Biped locomotion; Inverted pendulum, Bipedal walking; Bipedal-locomotion; Humanoid and bipedal locomotion; Inverted pendulum model; Rein-forcement learning; Simple++; Spring constants; Spring loaded inverted pendulums; Template models; Walking behavior, Springs (components)
Department/Centre: Others
Date Deposited: 01 Mar 2024 10:27
Last Modified: 01 Mar 2024 10:27
URI: https://eprints.iisc.ac.in/id/eprint/84058

Actions (login required)

View Item View Item