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ViTacWorld: Scaling Visuo-Tactile World Models for Contact-Rich Robot Manipulation
Yunao Huang, Shiyu Sang, Haotao Lu +5
Contact-rich robot manipulation requires physical interaction cues that are often invisible to cameras, making tactile sensing essential for robust control. However, scaling visuo-…
TactiDex: A Real-World Tactile-Guided Benchmark for Human-Like Dexterous Manipulation
Suting Ni, Hanbing Zhang, Zhenyu Wei +4
Tactile feedback is fundamental to Hand-Object Interaction (HOI), governing contact formation, force regulation, and stable manipulation, making it essential for achieving true hum…
Learning Semantic Atomic Skills for Multi-Task Robotic Manipulation
Yihang Zhu, Weiqing Wang, Shijie Wu +2
Scaling imitation learning to diverse multi-task robot manipulation remains challenging due to suboptimal demonstrations, behavioral multi-modality, and destructive interference ac…
Steering Generative Reinforcement Learning into Stable Robotic Controller
Yixuan Wang, Shutong Ding, Ke Hu +3
Diffusion and flow-based generative policies provide a powerful policy class for reinforcement learning by inducing rich stochastic exploration through iterative action generation.…
DreamPolicy: A Unified World-model Policy for Scalable Humanoid Locomotion
Yahao Fan, Tianxiang Gui, Kaiyang Ji +8
Achieving versatile humanoid locomotion with a single policy presents a critical scalability challenge. Prevailing methods often rely on distilling multiple terrain-specific teache…
Commanding Humanoid by Free-form Language: A Large Language Action Model with Unified Motion Vocabulary
Zhirui Liu, Kaiyang Ji, Ke Yang +4
Enabling humanoid robots to follow free-form natural language commands is a critical step toward seamless human-robot interaction and general-purpose embodied AI. However, existing…