13 papers
Language Movement Primitives: Grounding Language Models in Robot Motion
Yinlong Dai, Benjamin A. Christie, Daniel J. Evans +2
Enabling robots to perform novel manipulation tasks from natural language instructions remains a fundamental challenge in robotics, despite significant progress in generalized prob…
Theory of Mind Guided Strategy Adaptation for Zero-Shot Coordination
Andrew Ni, Simon Stepputtis, Stefanos Nikolaidis +3
A central challenge in multi-agent reinforcement learning is enabling agents to adapt to previously unseen teammates in a zero-shot fashion. Prior work in zero-shot coordination of…
Adaptively Coordinating with Novel Partners via Learned Latent Strategies
Benjamin Li, Shuyang Shi, Lucia Romero +7
Adaptation is the cornerstone of effective collaboration among heterogeneous team members. In human-agent teams, artificial agents need to adapt to their human partners in real tim…
Self-Correcting Decoding with Generative Feedback for Mitigating Hallucinations in Large Vision-Language Models
Ce Zhang, Zifu Wan, Zhehan Kan +7
While recent Large Vision-Language Models (LVLMs) have shown remarkable performance in multi-modal tasks, they are prone to generating hallucinatory text responses that do not alig…
Model-Agnostic Policy Explanations with Large Language Models
Zhang Xi-Jia, Yue Guo, Shufei Chen +4
Intelligent agents, such as robots, are increasingly deployed in real-world, human-centric environments. To foster appropriate human trust and meet legal and ethical standards, the…
Modeling Latent Partner Strategies for Adaptive Zero-Shot Human-Agent Collaboration
Benjamin Li, Shuyang Shi, Lucia Romero +7
In collaborative tasks, being able to adapt to your teammates is a necessary requirement for success. When teammates are heterogeneous, such as in human-agent teams, agents need to…