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intelligence_src_f28777e1849bec7cf0
observed · 2026-10-12 16:59:03 UTC
This White Paper gives robotics researchers and engineers an overview of a new large-scale motion capture dataset built to close the data gap limiting humanoid robot learning. It also shows how policies trained on the dataset transfer to a real humanoid robot.What you will learn about: Why humanoid robot learning, a central problem in embodied AI and Physical AI, needs data that internet video and existing motion capture datasets cannot provide. How FrameNet, a linguistic framework for human action, can guide motion capture collection to systematically cover a broad range of whole-body motion. Why synchronized object trajectories and meshes make human-object interaction data useful for teaching robots real-world tasks such as carrying, pushing, and pulling. How reinforcement learning policies trained on this motion capture data improve with scale, and how sim-to-real transfer carries them onto a physical humanoid robot.Download this free whitepaper now!
Fingerprint: bf1308cee0e62481404241d09cca6400eebbd5575fe2edf04c0340c2d50e54e9
intelligence_src_f28777e1849bec7cf0
observed · 2026-10-12 16:59:03 UTC
available
Fingerprint: 6ab66bf670ba3f01aa469a19a61354be9b2f1c174649ee36eccd41ac913c914b