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HEFT enables full-size humanoids to perform complex movements with heavy payloads, overcoming significant challenges in motion tracking and stability.
Forget expensive real-world robot training: Hi-WM lets humans directly edit a robot's simulated reality, turning world models into powerful, reusable playgrounds for failure recovery.
Frontier video models like Veo-3 can generate surprisingly good task-level plans for robot manipulation, but still need help with the fine details.
LLM agents get stuck in error feedback loops, but ProCeedRL's process-level critic and reflection-based demonstrations can actively break these cycles and substantially improve exploration.
Closing the reality gap: iteratively refining a world model with real-world robot data yields a significant boost in vision-language-action policy performance.
