Intrinsic Language-Guided Exploration for Complex Long-Horizon Robotic Manipulation Tasks
Eleftherios Triantafyllidis,Filippos Christianos,Zhibin Li,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. In this work, we propose the Intrinsically Guided Exploration from Large Language Models (IGE-LLMs) framework. By leveraging LLMs as an assistive intrinsic reward, IGE-LLMs guides the exploratory process in reinforcement...
