4 citations · 7 across the 5 of their papers we have counts for
6 papers · 1 filter
Co-Evolving Robot Orchestrators and Policies through Deployment
Xilun Zhang, Maggie Wang, Erik Bauer +4
Vision-language-action (VLA) policies trained on large datasets are capable within their training domains, yet they still fail to generalize to the variety of situations a robot me…
Scaling Verification Can Be More Effective than Scaling Policy Learning for Vision-Language-Action Alignment
Jacky Kwok, Xilun Zhang, Mengdi Xu +4
The long-standing vision of general-purpose robots hinges on their ability to understand and act upon natural language instructions. Vision-Language-Action (VLA) models have made r…
Dynamics as Prompts: In-Context Learning for Sim-to-Real System Identifications
Xilun Zhang, Shiqi Liu, Peide Huang +4
Sim-to-real transfer remains a significant challenge in robotics due to the discrepancies between simulated and real-world dynamics. Traditional methods like Domain Randomization o…
Learning Robust Policies via Interpretable Hamilton-Jacobi Reachability-Guided Disturbances
Hanyang Hu, Xilun Zhang, Xubo Lyu +1
Deep Reinforcement Learning (RL) has shown remarkable success in robotics with complex and heterogeneous dynamics. However, its vulnerability to unknown disturbances and adversaria…
Creative Robot Tool Use with Large Language Models
Mengdi Xu, Peide Huang, Wenhao Yu +7
Tool use is a hallmark of advanced intelligence, exemplified in both animal behavior and robotic capabilities. This paper investigates the feasibility of imbuing robots with the ab…
What Went Wrong? Closing the Sim-to-Real Gap via Differentiable Causal Discovery
Peide Huang, Xilun Zhang, Ziang Cao +6
Training control policies in simulation is more appealing than on real robots directly, as it allows for exploring diverse states in an efficient manner. Yet, robot simulators inev…