8 citations · 10 across the 16 of their papers we have counts for
10 papers · 1 filter
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…
Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why
Chenhao Li, Marco Hutter, Andreas Krause
This survey provides a comparative analysis of feature-based and GAN-based approaches to learning from demonstrations, with a focus on the structure of reward functions and their i…
Human2LocoMan: Learning Versatile Quadrupedal Manipulation with Human Pretraining
Yaru Niu, Yunzhe Zhang, Mingyang Yu +11
Quadrupedal robots have demonstrated impressive locomotion capabilities in complex environments, but equipping them with autonomous versatile manipulation skills in a scalable way…
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…
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…
NIL: No-data Imitation Learning by Leveraging Pre-trained Video Diffusion Models
Mert Albaba, Chenhao Li, Markos Diomataris +3
Acquiring physically plausible motor skills across diverse and unconventional morphologies-including humanoid robots, quadrupeds, and animals-is essential for advancing character s…
DFM: Deep Fourier Mimic for Expressive Dance Motion Learning
Ryo Watanabe, Chenhao Li, Marco Hutter
As entertainment robots gain popularity, the demand for natural and expressive motion, particularly in dancing, continues to rise. Traditionally, dancing motions have been manually…
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…
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…
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…