You can access my all of papers at 🔗Google Scholar.
2026
Push-Wiper: Toward General-Purpose Robotic Cleaning across Varied Stains and Surfaces with Segmented Pushing Trajectories
Renhao Lu, Mingxin Wang, Chenyang Cao, Yang Yang, Guoping Pan, Kangkang Dong, Yi Cheng, Houde Liu†(† corresponding author)
IROS 2026 Conference
We reformulate viscous stain cleaning as an aggregation problem, where a sponge progressively gathers the stain through segmented pushing trajectories generated by a diffusion policy, generalizing in a zero-shot manner to unseen stains and curved surfaces.
QPILOTS：Efficient Test-Time Q-Steering for Flow Policies
Yifan Ruan, Chenyang Cao, Andreas Burger, Ali Pesaranghader, Kaveh Kamali, Jaehong Kim, Nandita Vijaykumar, Alan Aspuru-Guzik, Igor Gilitschenski, Nicholas Rhinehart†(† corresponding author)
arXiv 2026 Conference
We introduce a novel algorithm to combine a critic with a pretrained flow-matching policy to achieve strong results in offline-online reinforcement learning (RL) and online VLA steering in simulation.
2025
AM-FMT: Assisting Metric Fast Marching Tree for Motion Planning in Complex Environments
Renhao Lu, Yi Cheng, Chongkun Xia, Chenyang Cao, Lunfei Liang, Xiaojun Zhu, Houde Liu†(† corresponding author)
ROBIO 2025 Conference
We propose an assisting metric for the real-time fast marching tree, which speeds up the tree expansion and avoids the local minima trap in environments with narrow passages and dynamic obstacles.
Residual Reward Models for Preference-based Reinforcement Learning
Chenyang Cao, Miguel Rogel-García, Mohamed Nabail, Xueqian Wang, Nicholas Rhinehart†(† corresponding author)
arXiv 2025 Conference
We propose a residual reward model for reward learning by effectively taking advantage of human prior knowledge.
FOSP： Fine-tuning Offline Safe Policy through World Models
Chenyang Cao, Yuchen Xin, Silang Wu, Longxiang He, Zichen Yan, Junbo Tan, Xueqian Wang†(† corresponding author)
ICLR 2025 Conference
We propose a safe offline-to-online reinforcement learning algorithm by leveraging world models. It ensures the agent safely moves in the environment during online fine-tuning.
2024
A Wristband Haptic Feedback System for Robotic Arm Teleoperation
Silang Wu, Huayue Liang, Chenyang Cao, Chongkun Xia, Xueqian Wang, Houde Liu†(† corresponding author)
ROBIO 2024 Conference
We make a wristband teleoperation system to provide haptic feedback from the end-effector.
Offline Goal-Conditioned Reinforcement Learning for Safety-Critical Tasks with Recovery Policy
Chenyang Cao, Zichen Yan, Renhao Lu, Junbo Tan†, Xueqian Wang†(† corresponding author)
ICRA 2024 Conference
We propose an offline goal-conditioned reinforcement learning algorithm to solve the planning problem in constrained environments without interacting with them. The algorithm combines the advantages of efficient planning and safe obstacle avoidance, and effectively balances the optimization of both aspects.