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This work presents a hierarchical framework for bipedal locomotion that combines a Reinforcement Learning (RL)-based high-level (HL) planner policy for the online generation of task space commands with a model-based low-level (LL) controller to track the desired task space trajectories. Different from traditional end-to-end learning approaches, our HL policy takes insights from the angular momentum-based linear inverted pendulum (ALIP) to carefully design the observation and action spaces of the Markov Decision Process (MDP). This simple yet effective design creates an insightful mapping between a low-dimensional state that effectively captures the complex dynamics of bipedal locomotion and a set of task space outputs that shape the walking gait of the robot. The HL policy is agnostic to the task space LL controller, which increases the flexibility of the design and generalization of the framework to other bipedal robots. This hierarchical design results in a learning-based framework with improved performance, data efficiency, and robustness compared with the ALIP model-based approach and state-of-the-art learning-based frameworks for bipedal locomotion. The proposed hierarchical controller is tested in three different robots, Rabbit, a five-link underactuated planar biped; Walker2D, a seven-link fully-actuated planar biped; and Digit, a 3D humanoid robot with 20 actuated joints. The trained policy naturally learns human-like locomotion behaviors and is able to effectively track a wide range of walking speeds while preserving the robustness and stability of the walking gait even under adversarial conditions.
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An Energy-Based Framework for Robust Dynamic Bipedal Walking Over Compliant Terrain
Abstract Bipedal locomotion over compliant terrain is an important and largely underexplored problem in the robotics community. Although robot walking has been achieved on some non-rigid surfaces with existing control methodologies, there is a need for a systematic framework applicable to different bipeds that enables stable locomotion over various compliant terrains. In this work, a novel energy-based framework is proposed that allows the dynamic locomotion of bipeds across a wide range of compliant surfaces. The proposed framework utilizes an extended version of the 3D dual spring-loaded inverted pendulum (Dual-SLIP) model that supports compliant terrains, while a bio-inspired controller is employed to regulate expected perturbations of extremely low ground-stiffness levels. An energy-based methodology is introduced for tuning the bio-inspired controller to enable dynamic walking with robustness to a wide range of low ground-stiffness one-step perturbations. The proposed system and controller are shown to mimic the vertical ground reaction force (GRF) responses observed in human walking over compliant terrains. Moreover, they succeed in handling repeated unilateral stiffness perturbations under specific conditions. This work can advance the field of biped locomotion by providing a biomimetic method for generating stable human-like walking trajectories for bipedal robots over various compliant surfaces. Furthermore, the concepts of the proposed framework could be incorporated into the design of controllers for lower-limb prostheses with adjustable stiffness to improve their robustness over compliant surfaces.
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- PAR ID:
- 10515996
- Publisher / Repository:
- American Society of Mechanical Engineers (ASME)
- Date Published:
- Journal Name:
- Journal of Dynamic Systems, Measurement, and Control
- Volume:
- 146
- Issue:
- 2
- ISSN:
- 0022-0434
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1115/1.4064094
