SARM2 + SPIRAL: Multi-Task Reward Models and RL Refinement for Long-Horizon Dexterous Manipulation

from Embodied AI 101 · host Shaoqing Tan

Combines scalable autonomous reward modeling with RL-based refinement to improve vision-language-action policies on long-horizon dexterous manipulation tasks via autonomous rollouts. Demonstrates significant gains over imitation learning baselines.

Episode metadata supplied by the publisher feed · Published Jun 25, 2026

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SARM2 + SPIRAL: Multi-Task Reward Models and RL Refinement for Long-Horizon Dexterous Manipulation

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