6 papers
EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning
Shuoqin Zhang, Tongtong Cheng, Xiru Gao +7
Human-in-the-loop reinforcement learning (HIL-RL) enables robots to learn contact-rich manipulation from limited real-world interaction, but deployment exposes three coupled limita…
WAM-TTT: Steering World-Action Models by Watching Human Play at Test Time
Yusen Feng, Bingchen Han, Jiangran Lyu +13
Steering robot foundation models (RFMs) toward new task variants or user-preferred behaviors remains challenging, often requiring additional robot demonstrations, task-specific fin…
LDA-1B: Scaling Latent Dynamics Action Model via Universal Embodied Data Ingestion
Jiangran Lyu, Kai Liu, Xuheng Zhang +20
Recent robot foundation models largely rely on large-scale behavior cloning, which imitates expert actions but discards transferable dynamics knowledge embedded in heterogeneous em…
RoHIL: Robust Human-in-the-Loop Robotic Reinforcement Learning Against Illumination Variations
Shuoqin Zhang, Yixin Xiong, Xiru Gao +4
Human-in-the-loop reinforcement learning systems achieve near-perfect success on the workstation where they are trained, but collapse when the same robot is moved to a workstation…
World4RL: Diffusion World Models for Policy Refinement with Reinforcement Learning for Robotic Manipulation
Zhennan Jiang, Kai Liu, Yuxin Qin +6
Robotic manipulation policies are commonly initialized through imitation learning, but their performance is limited by the scarcity and narrow coverage of expert data. Reinforcemen…
Survey of Vision-Language-Action Models for Embodied Manipulation
Haoran Li, Yuhui Chen, Wenbo Cui +5
Embodied intelligence systems, which enhance agent capabilities through continuous environment interactions, have garnered significant attention from both academia and industry. Vi…