9 papers · 1 filter
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…
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…
Learning More With Less: Sample Efficient Model-Based RL for Loco-Manipulation
Benjamin Hoffman, Jin Cheng, Chenhao Li +1
By combining the agility of legged locomotion with the capabilities of manipulation, loco-manipulation platforms have the potential to perform complex tasks in real-world applicati…
Constrained Style Learning from Imperfect Demonstrations under Task Optimality
Kehan Wen, Chenhao Li, Junzhe He +1
Learning from demonstration has proven effective in robotics for acquiring natural behaviors, such as stylistic motions and lifelike agility, particularly when explicitly defining…