13 citations · 20 across the 15 of their papers we have counts for
5 papers · 1 filter
Accelerating Reinforcement Learning of Robotic Manipulations via Feedback from Large Language Models
Kun Chu, Xufeng Zhao, Cornelius Weber +2
Reinforcement Learning (RL) plays an important role in the robotic manipulation domain since it allows self-learning from trial-and-error interactions with the environment. Still,…
Enhancing Zero-Shot Chain-of-Thought Reasoning in Large Language Models through Logic
Xufeng Zhao, Mengdi Li, Wenhao Lu +4
Recent advancements in large language models have showcased their remarkable generalizability across various domains. However, their reasoning abilities still have significant room…
A Closer Look at Reward Decomposition for High-Level Robotic Explanations
Wenhao Lu, Xufeng Zhao, Sven Magg +3
Explaining the behaviour of intelligent agents learned by reinforcement learning (RL) to humans is challenging yet crucial due to their incomprehensible proprioceptive states, vari…
Chat with the Environment: Interactive Multimodal Perception Using Large Language Models
Xufeng Zhao, Mengdi Li, Cornelius Weber +2
Programming robot behavior in a complex world faces challenges on multiple levels, from dextrous low-level skills to high-level planning and reasoning. Recent pre-trained Large Lan…
Internally Rewarded Reinforcement Learning
Mengdi Li, Xufeng Zhao, Jae Hee Lee +2
We study a class of reinforcement learning problems where the reward signals for policy learning are generated by an internal reward model that is dependent on and jointly optimize…