SHORE: Stability-Hybrid On-Policy Reinforcement Learning for Online Bin Packing
SHORE: Stability-Hybrid On-Policy Reinforcement Learning for Online Bin Packing
This research presents SHORE, a reinforcement learning framework designed for online 3D bin packing tasks that must maintain stack stability under uncertain future arrivals. The method combines a learned stability probability with on-policy return estimates using a 3D U-Net and an actor-critic architecture. Evaluated in Isaac Gym, SHORE achieves 73% space utilization by 0.4M environment steps, outperforming the GOPT baseline (0.588 utilization) on a 60-box set, though with a higher placement-failure rate of 1.03% versus 0.63%. The approach is also demonstrated on a real robot to confirm its integration feasibility.
Source: arXiv Robotics — research abstracts · Read original article ↗
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60
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ions in both its action values and policy prior, with gates that limit the effect of an untrained or changing model. In deployment, SHORE does not require additional search or simulation overhead. In Isaac Gym, SHORE reaches 73\% space utilization by 0.4M environment steps, while the same learner without stability modeling reaches that level at about 1M. On the expanded 60-box set, SHORE achieves 0.734 space utilizat
Open source S4
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ions in both its action values and policy prior, with gates that limit the effect of an untrained or changing model. In deployment, SHORE does not require additional search or simulation overhead. In Isaac Gym, SHORE reaches 73\% space utilization by 0.4M environment steps, while the same learner without stability modeling reaches that level at about 1M. On the expanded 60-box set, SHORE achieves 0.734 space utilizat
Open source S4
ion versus 0.588 for the GOPT state-of-the-art baseline, with a higher placement-failure frequency (1.03\% vs. 0.63\%), trading more risk for greater packed volume. The results show faster learning and higher utilization on the more demanding realistic box arrival set. Ablations suggest that simulated alternatives provide useful early supervision, while executed outcomes alone sustain later performance. We also demon
Open source S5
strate the method on a real robot to verify the simplicity of integrating the method to real bin packing pipelines.
Open source S6
Source：arXiv Robotics — research abstracts · arxiv.org