13 papers
Enhancing VLM Reward Models Through Structure-Aware Fine-Tuning
Pyrros Koussios, Chenhao Li, Xin Chen +1
Designing effective reward functions remains a major bottleneck in Reinforcement Learning (RL). Recent work uses large foundation Vision-Language Models (VLMs) as reward models, co…
PAINT: Partner-Agnostic Intent-Aware Cooperative Transport with Legged Robots
Zhihao Cao, Tianxu An, Chenhao Li +2
Collaborative transport requires robots to infer partner intent through physical interaction while maintaining stable loco-manipulation. This becomes particularly challenging in co…
Symmetry-Guided Memory Augmentation for Efficient Locomotion Learning
Kaixi Bao, Chenhao Li, Yarden As +2
Training reinforcement learning (RL) policies for legged locomotion often requires extensive environment interactions, which are costly and time-consuming. We propose Symmetry-Guid…
Uncertainty-Aware Robotic World Model Makes Offline Model-Based Reinforcement Learning Work on Real Robots
Chenhao Li, Andreas Krause, Marco Hutter
Reinforcement Learning (RL) has achieved impressive results in robotics, yet high-performing pipelines remain highly task-specific, with little reuse of prior data. Offline Model-b…
Robotic World Model: A Neural Network Simulator for Robust Policy Optimization in Robotics
Chenhao Li, Andreas Krause, Marco Hutter
Learning robust and generalizable world models is crucial for enabling efficient and scalable robotic control in real-world environments. In this work, we introduce a novel framewo…
Learning Soft Robotic Dynamics with Active Exploration
Hehui Zheng, Bhavya Sukhija, Chenhao Li +3
Soft robots offer unmatched adaptability and safety in unstructured environments, yet their compliant, high-dimensional, and nonlinear dynamics make modeling for control notoriousl…