5 papers
Using large language models for embodied planning introduces systematic safety risks
Tao Zhang, Kaixian Qu, Zhibin Li +4
Large language models are increasingly used as planners for robotic systems, yet how safely they plan remains an open question. To evaluate safe planning systematically, we introdu…
RoboBallet: Planning for Multi-Robot Reaching with Graph Neural Networks and Reinforcement Learning
Matthew Lai, Keegan Go, Zhibin Li +4
Modern robotic manufacturing requires collision-free coordination of multiple robots to complete numerous tasks in shared, obstacle-rich workspaces. Although individual tasks may b…
Towards Generalist Robot Learning from Internet Video: A Survey
Robert McCarthy, Daniel C. H. Tan, Dominik Schmidt +5
Scaling deep learning to massive and diverse internet data has driven remarkable breakthroughs in domains such as video generation and natural language processing. Robot learning,…
Efficient Learning of A Unified Policy For Whole-body Manipulation and Locomotion Skills
Dianyong Hou, Chengrui Zhu, Zhen Zhang +3
Equipping quadruped robots with manipulators provides unique loco-manipulation capabilities, enabling diverse practical applications. This integration creates a more complex system…
Are Large Language Models Strategic Decision Makers? A Study of Performance and Bias in Two-Player Non-Zero-Sum Games
Nathan Herr, Fernando Acero, Roberta Raileanu +2
Large Language Models (LLMs) have been increasingly used in real-world settings, yet their strategic decision-making abilities remain largely unexplored. To fully benefit from the…