13 citations · 20 across the 15 of their papers we have counts for
5 papers · 1 filter
Mental Modeling of Reinforcement Learning Agents by Language Models
Wenhao Lu, Xufeng Zhao, Josua Spisak +2
Can emergent language models faithfully model the intelligence of decision-making agents? Though modern language models exhibit already some reasoning ability, and theoretically ca…
Details Make a Difference: Object State-Sensitive Neurorobotic Task Planning
Xiaowen Sun, Xufeng Zhao, Jae Hee Lee +3
The state of an object reflects its current status or condition and is important for a robot's task planning and manipulation. However, detecting an object's state and generating a…
Agentic Skill Discovery
Xufeng Zhao, Cornelius Weber, Stefan Wermter
Language-conditioned robotic skills make it possible to apply the high-level reasoning of Large Language Models (LLMs) to low-level robotic control. A remaining challenge is to acq…
Large Language Models for Orchestrating Bimanual Robots
Kun Chu, Xufeng Zhao, Cornelius Weber +3
Although there has been rapid progress in endowing robots with the ability to solve complex manipulation tasks, generating control policies for bimanual robots to solve tasks invol…
Causal State Distillation for Explainable Reinforcement Learning
Wenhao Lu, Xufeng Zhao, Thilo Fryen +4
Reinforcement learning (RL) is a powerful technique for training intelligent agents, but understanding why these agents make specific decisions can be quite challenging. This lack…