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From Pretraining to Proficiency: Real-World Subtask RL for Long-Horizon Manipulation with Minimal Human Intervention
The paper presents PARTS, a framework that augments a frozen pretrained robot policy with RL‑learned residuals on selected bottleneck subtasks, using local success rewards and minimal human resets. In real‑world bimanual and single‑arm tasks, PARTS more than doubles success rates with only minutes of robot rollouts, outperforming prior fine‑tuning methods.
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GPT‑Policy is a framework that lets a large vision‑language model (e.g. GPT‑6 Astra) perform in‑context robot learning: a context compiler extracts visual transitions from demos, the VLM proposes tool actions, and a constrained controller verifies and executes them. Real‑robot experiments show that raw video demos improve success rates even without explicit action labels, and that providing align…
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Production AI agents often fail silently due to issues beyond model hallucination, such as state inconsistencies. This talk outlines principles for building reliable agent harnesses, focusing on explicit state ownership, ordered mutations, and verifiable actions, using real-world case studies.
InfoQinfoq.com24 mintalkGAVEL: Graph World Models for Verified and Efficient Long-Horizon LLM Task Planning
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Hugging Face Daily Papersarxiv.org1 minpaperHow to operate shared platforms safely at agent scale
Datadog explains how scaling AI agents turns isolated executions into shared‑platform risk and outlines a systematic approach to model agent trajectories, monitor per‑dependency constraints, and enforce workload‑specific capacity policies. The result is proactive detection of bottlenecks and protection against noisy‑neighbor failures.
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