Opt2VLA is presented, a force-aware VLA framework that introduces explicit force commands at the VLA-to-control interface for humanoid whole-body manipulation and shows that explicit force conditioning enables more accurate and consistent force regulation than motion-only control, while physically grounded torque super...
Fu-Kang Liu, Yipu Chen, Jaehwi Jang et al.· 0 citations
Similar to humans, robots benefit from multiple sensing modalities when performing complex manipulation tasks. Current behavior cloning (BC) policies typically fuse learned observation embeddings from multimodal inputs before decoding them into actions. This approach suffers from two key limitations: 1) it requires all...
Vaibhav Saxena, Yun-Hao Luo, Yotto Koga et al.· IEEE Robotics and Automation...· 0 citations
Robots operating in open environments act under partial observability, physical constraints, and dynamic task contexts. Beyond mapping observations and language instructions to actions, they must anticipate how candidate actions may affect future states and task-relevant outcomes. Recent advances in world models, video...
Zu-Xing Lu, Hong-Jia Zhai, Guan-Zhi Wang et al.· 1 citation
EgoWAM is introduced, a controlled human-robot co-training framework that fixes the policy backbone, action head, and data mixture while varying only the world prediction target, comparing Pixel, DINO, and 3D motion flow.
Baoyu Li, Xi Yin, Mengying Lin et al.· 5 citations
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