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cs.RO2026
Worlds in One Demo: A Synthetic Data Engine for Learning Open-World Mobile Manipulation
Lingxiao Guo, Huanyu Li, Guanya Shi
Learning open-world mobile manipulation policies requires vast data to achieve spatial generalization, long-horizon robustness, and scene generalization. Current prevailing data co…
cs.RO2026
PCHC: Enabling Preference Conditioned Humanoid Control via Multi-Objective Reinforcement Learning
Huanyu Li, Dewei Wang, Xinmiao Wang +4
Humanoid robots often need to balance competing objectives, such as maximizing speed while minimizing energy consumption. While current reinforcement learning (RL) methods can mast…
cs.RO2026
Failure-Aware RL: Reliable Offline-to-Online Reinforcement Learning with Self-Recovery for Real-World Manipulation
Huanyu Li, Kun Lei, Sheng Zang +5
Post-training algorithms based on deep reinforcement learning can push the limits of robotic models for specific objectives, such as generalizability, accuracy, and robustness. How…