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Learning Terrain Aware Bipedal Locomotion via Reduced Dimensional Perceptual Representations
Guillermo A. Castillo, Himanshu Lodha, Ayonga Hereid
This work introduces a hierarchical strategy for terrain-aware bipedal locomotion that integrates reduced-dimensional perceptual representations to enhance reinforcement learning (…
Rethink Repeatable Measures of Robot Performance with Statistical Query
Bowen Weng, Linda Capito, Guillermo A. Castillo +1
For a general standardized testing algorithm designed to evaluate a specific aspect of a robot's performance, several key expectations are commonly imposed. Beyond accuracy (i.e.,…
Real-Time Safe Bipedal Robot Navigation using Linear Discrete Control Barrier Functions
Chengyang Peng, Victor Paredes, Guillermo A. Castillo +1
Safe navigation in real-time is an essential task for humanoid robots in real-world deployment. Since humanoid robots are inherently underactuated thanks to unilateral ground conta…
Repeatable and Reliable Efforts of Accelerated Risk Assessment in Robot Testing
Linda Capito, Guillermo A. Castillo, Bowen Weng
Risk assessment of a robot in controlled environments, such as laboratories and proving grounds, is a common means to assess, certify, validate, verify, and characterize the robots…
Adaptive Step Duration for Accurate Foot Placement: Achieving Robust Bipedal Locomotion on Terrains with Restricted Footholds
Zhaoyang Xiang, Victor Paredes, Guillermo A. Castillo +1
Traditional one-step preview planning algorithms for bipedal locomotion struggle to generate viable gaits when walking across terrains with restricted footholds, such as stepping s…
Data-Driven Latent Space Representation for Robust Bipedal Locomotion Learning
Guillermo A. Castillo, Bowen Weng, Wei Zhang +1
This paper presents a novel framework for learning robust bipedal walking by combining a data-driven state representation with a Reinforcement Learning (RL) based locomotion policy…