6 papers
Multi-Terrain Mastery: A Comprehensive Controller for Bipedal Locomotion
Oluwami Dosunmu-Ogunbi, Aayushi Shrivastava
Advancing bipedal robots to navigate diverse terrains remains a significant challenge in robotics. Traditional locomotion controllers excel on specific surfaces but struggle across…
Residual Denoising Enables Sample-Efficient Multi-Agent Coordination on Demand
Dayi Dong, Maulik Bhatt, Aayushi Shrivastava +2
Pretrained robot policies offer strong manipulation skills but are typically limited to single-agent settings, where a robot acts in isolation. In this work, we study how to adapt…
SAMBAR: Selective Anchoring via Method of Multipliers for Balanced Knowledge Acquisition and Retention in Vision-Language-Action Models
Aayushi Shrivastava, Xunlan Zhou, Hongrui Zhao +2
Vision-Language-Action (VLA) models leverage large-scale pretraining to ultimately achieve generalist manipulation. Deployed VLA policies must support continual learning to acquire…
HuGo: LLMs as Whole-Body Policy Code Designers for Humanoid Loco-Manipulation
Seoyeon Choi, Shizhao Ye, Nicholas Bui +5
For humanoids to be useful in everyday environments, they must perform a wide range of tasks that couple locomotion and manipulation. Existing approaches commonly acquire a loco-ma…
Learning Control Policies to Provably Satisfy Hard Affine Constraints for Black-Box Hybrid Dynamical Systems
Aayushi Shrivastava, Kartik Nagpal, Sairam Jinkala +2
Ensuring safety for black-box hybrid dynamical systems presents significant challenges due to their instantaneous state jumps and unknown explicit nonlinear dynamics. Existing solu…
Demonstrating a Robust Walking Algorithm for Underactuated Bipedal Robots in Non-flat, Non-stationary Environments
Oluwami Dosunmu-Ogunbi, Aayushi Shrivastava, Jessy W Grizzle
This work explores an innovative algorithm designed to enhance the mobility of underactuated bipedal robots across challenging terrains, especially when navigating through spaces w…