Muzhi Han

LLM3:Large Language Model-based Task and Motion Planning with Motion Failure Reasoning

2024 · arXiv (Cornell University)

Conventional Task and Motion Planning (TAMP) approaches rely on manually crafted interfaces connecting symbolic task planning with continuous motion generation. These domain-specific and labor-intensive modules are limited in addressing emerging tasks in real-world settings. Here, …

INTERPRET: Interactive Predicate Learning from Language Feedback for Generalizable Task Planning

2024

Learning abstract state representations and knowledge is crucial for long-horizon robot planning.We present Inter-PreT, an Large Language Model (LLM)-powered framework for robots to learn symbolic predicates from language feedback of human non-experts during embodied interaction.The …