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SHORE (Stability‑Hybrid On‑Policy Reinforcement Learning) tackles online 3D bin packing by jointly deciding which box to pack, its orientation, and placement while ensuring stack stability. It learns a stability probability via a 3D U‑Net from both executed outcomes and simulated alternatives, and incorporates these predictions into a discrete on‑policy actor‑critic that gates the influence of an untrained model. In Isaac Gym experiments, SHORE achieves 73 % space utilization in 0.4 M steps—twice as fast as a baseline without stability modeling—and outperforms the GOPT baseline on a 60‑box set, albeit with a slightly higher placement‑failure rate. whyItMatters":"The method demonstrates that integrating learned stability predictions into reinforcement learning accelerates learning and improves packing efficiency in realistic, high‑risk bin‑packing scenarios."
arXiv:2609.13234v1 Announce Type: cross Abstract: Industrialized construction imposes stringent precision requirements on robotic assembly of modular components such as prefabricated window units. In...
arXiv:2607. 09866v1 Announce Type: cross Abstract: Offline-to-online reinforcement learning is promising for generalizable robotic manipulation, yet its full-stack complexity obscures reproduction and diagnosis.
The paper introduces RBGA, an algorithm for offline-to-online safe reinforcement learning in dexterous manipulation. RBGA tracks the remaining safety budget during an episode and uses it to arbitrate between offline guidance and an evolving online policy, comparing cost-to-go estimates at each step. The authors provide theoretical analysis of safety implications and demonstrate through experiments that RBGA improves online policy performance while preserving safety.
arXiv:2608. 15680v1 Announce Type: cross Abstract: Vision-language-action (VLA) models improve robotic manipulation but remain vulnerable to compounding errors, scene changes, and off-trajectory states.
Action-conditioned world models offer a scalable alternative to costly real-world interaction for improving robot policies. However, existing world-model-based methods largely treat world models as fo...