15 citations · 28 across the 7 of their papers we have counts for
8 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…
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…
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,…
Distilling Reinforcement Learning Policies for Interpretable Robot Locomotion: Gradient Boosting Machines and Symbolic Regression
Fernando Acero, Zhibin Li
Recent advancements in reinforcement learning (RL) have led to remarkable achievements in robot locomotion capabilities. However, the complexity and ``black-box'' nature of neural…
Modular Neural Network Policies for Learning In-Flight Object Catching with a Robot Hand-Arm System
Wenbin Hu, Fernando Acero, Eleftherios Triantafyllidis +2
We present a modular framework designed to enable a robot hand-arm system to learn how to catch flying objects, a task that requires fast, reactive, and accurately-timed robot moti…
Intrinsic Language-Guided Exploration for Complex Long-Horizon Robotic Manipulation Tasks
Eleftherios Triantafyllidis, Filippos Christianos, Zhibin Li
Current reinforcement learning algorithms struggle in sparse and complex environments, most notably in long-horizon manipulation tasks entailing a plethora of different sequences.…