『Embodied AI 101』のカバーアート

Embodied AI 101

Embodied AI 101

著者： Shaoqing Tan
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Stay in the loop on research in AI and physical intelligence.© 2026 Shaoqing Tan 科学
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  • The Optimizer Becomes a Policy: Inside RP-1's Learned Planner for Robot World Models

    The Optimizer Becomes a Policy: Inside RP-1's Learned Planner for Robot World Models
    2026/09/03
    Pantheon Industries introduces Reinforced Planning (RP-1), the first fully learned planner that iteratively improves robot action plans from scratch using Reinforcement Learning over a frozen World Model. RP-1 reimagines planning as a learned policy: starting with a candidate action sequence, it uses a frozen World Model to imagine outcomes, a learned critic to evaluate closeness to goal (value-based scoring instead of latent distance), and a neural planner to revise the sequence over multiple iterations. This replaces hand-designed search heuristics (CEM, MPPI, Adam) with learned, reusable plan-improvement rules. Key results across ThreeRoom, OGBench, and Reacher benchmarks: RP-1 beats SOTA latent methods on 48 of 48 head-to-head comparisons, achieves 2.2x higher success rate on hard and long-horizon tasks, is 67x faster at scale (50 robot arms on H200 GPUs), and requires 1000x fewer World Model queries than MPPI/CEM.
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    31 分
  • SAGA: Ground the Affordance, Then Learn the Motion

    SAGA: Ground the Affordance, Then Learn the Motion
    2026/09/03
    SAGA: Open-World Mobile Manipulation via Structured Affordance Grounding
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    33 分
  • REMAC: Turning Robot Failures into Better Team Plans

    REMAC: Turning Robot Failures into Better Team Plans
    2026/09/03
    REMAC: Self-reflective and self-evolving multi-agent collaboration for long-horizon robot manipulation
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    32 分
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