9 papers
H2R-Bench: Benchmarking Human-to-Robot Manipulation Video Generation in World Models
Dingyi Rong, Yue Shi, Chaofan Ma +6
Large-scale manipulation data is essential for robot learning, yet collecting robot demonstrations remains expensive and difficult to scale. Meanwhile, abundant egocentric human ma…
Gold Points Sniper: Self-guided Visual Reasoning in VLM for Fine-grained Action Understanding
Haodi Liu, Xinhang Yang, Kunda Yan +3
Robots operating in everyday environments must understand fine-grained human actions, intentions, and contextual cues from broad views where people occupy only small regions, a cap…
GeoWorld: Geometric World Models
Zeyu Zhang, Danning Li, Ian Reid +1
Energy-based predictive world models provide a powerful approach for multi-step visual planning by reasoning over latent energy landscapes rather than generating pixels. However, e…
FATE: Closed-Loop Feasibility-Aware Task Generation with Active Repair for Physically Grounded Robotic Curricula
Bingchuan Wei, Bingqi Huang, Jingheng Ma +2
Recent breakthroughs in generative simulation have harnessed Large Language Models (LLMs) to generate diverse robotic task curricula, yet these open-loop paradigms frequently produ…
MoRL: Reinforced Reasoning for Unified Motion Understanding and Generation
Hongpeng Wang, Zeyu Zhang, Wenhao Li +1
Human motion understanding and generation are crucial for vision and robotics but remain limited in reasoning capability and test-time planning. We propose MoRL, a unified multimod…
Motion-R1: Enhancing Motion Generation with Decomposed Chain-of-Thought and RL Binding
Runqi Ouyang, Haoyun Li, Zhenyuan Zhang +6
Text-to-Motion generation has become a fundamental task in human-machine interaction, enabling the synthesis of realistic human motions from natural language descriptions. Although…