SOURCE-LINKED INTELLIGENCE
FIERCE: From Generalist Robot Policies to Fast Specialists via Progress-Failure Feedback
Generalist robot policies offer useful initialization, but refining compact specialists through limited physical interaction requires informative learning feedback. We present FIERCE, a generalist-initialized reinforcement learning framework centered on a unified, task-adaptive progress-failure evaluator. Its architecture shares an observation-language representation between an observed-progress head and an action-conditioned latent predictor whose past and current predictions feed a causal sequence head for task-failure estimation. Joint supervision from progress and preference labels, synchr
Read original source ↗ Open in workspace
- recordType
- paper
- region
- Global
Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-16T13:34:08.000Z
First collected: 2026-09-20T08:01:03.945Z. This is not the publication date.