13 citations · 20 across the 15 of their papers we have counts for
16 papers
Behavior Foundations for Quadruped Robots: ABot-C0 Technical Report
Xufeng Zhao, Fuzhi Yang, Jianhui Chen +17
The motion controller is one of the most fundamental modules in embodied intelligence systems. Driven by large-scale human motion-capture data and the motion-tracking paradigm, hum…
StateVLM: A State-Aware Vision-Language Model for Robotic Affordance Reasoning
Xiaowen Sun, Matthias Kerzel, Mengdi Li +3
Vision-language models have demonstrated strong performance across robotic perception and instruction-following tasks. However, they still struggle with precise spatial reasoning,…
PersRM-R1: Enhance Personalized Reward Modeling with Reinforcement Learning
Mengdi Li, Guanqiao Chen, Xufeng Zhao +3
Reward models (RMs), which are central to existing post-training methods, aim to align LLM outputs with human values by providing feedback signals during fine-tuning. However, exis…
Curriculum-RLAIF: Curriculum Alignment with Reinforcement Learning from AI Feedback
Jiaye Lin, Mengdi Li, Xufeng Zhao +4
Reward models trained through Reinforcement Learning from AI Feedback (RLAIF) methods frequently suffer from limited generalizability, which hinders the alignment performance of po…
LLM+MAP: Bimanual Robot Task Planning using Large Language Models and Planning Domain Definition Language
Kun Chu, Xufeng Zhao, Cornelius Weber +1
Bimanual robotic manipulation provides significant versatility, but also presents an inherent challenge due to the complexity involved in the spatial and temporal coordination betw…
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…
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…
Impact Makes a Sound and Sound Makes an Impact: Sound Guides Representations and Explorations
Xufeng Zhao, Cornelius Weber, Muhammad Burhan Hafez +1
Sound is one of the most informative and abundant modalities in the real world while being robust to sense without contacts by small and cheap sensors that can be placed on mobile…