Effective Online 3D Bin Packing with Lookahead Parcels Using Monte Carlo Tree Search
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866914300218572800 |
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| author | Fang, Jiangyi Zhou, Bowen Wang, Haotian Zhu, Xin Wang, Leye |
| author_facet | Fang, Jiangyi Zhou, Bowen Wang, Haotian Zhu, Xin Wang, Leye |
| contents | Online 3D Bin Packing (3D-BP) with robotic arms is crucial for reducing transportation and labor costs in modern logistics. While Deep Reinforcement Learning (DRL) has shown strong performance, it often fails to adapt to real-world short-term distribution shifts, which arise as different batches of goods arrive sequentially, causing performance drops. We argue that the short-term lookahead information available in modern logistics systems is key to mitigating this issue, especially during distribution shifts. We formulate online 3D-BP with lookahead parcels as a Model Predictive Control (MPC) problem and adapt the Monte Carlo Tree Search (MCTS) framework to solve it. Our framework employs a dynamic exploration prior that automatically balances a learned RL policy and a robust random policy based on the lookahead characteristics. Additionally, we design an auxiliary reward to penalize long-term spatial waste from individual placements. Extensive experiments on real-world datasets show that our method consistently outperforms state-of-the-art baselines, achieving over 10\% gains under distributional shifts, 4\% average improvement in online deployment, and up to more than 8\% in the best case--demonstrating the effectiveness of our framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_02649 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Effective Online 3D Bin Packing with Lookahead Parcels Using Monte Carlo Tree Search Fang, Jiangyi Zhou, Bowen Wang, Haotian Zhu, Xin Wang, Leye Robotics Artificial Intelligence Online 3D Bin Packing (3D-BP) with robotic arms is crucial for reducing transportation and labor costs in modern logistics. While Deep Reinforcement Learning (DRL) has shown strong performance, it often fails to adapt to real-world short-term distribution shifts, which arise as different batches of goods arrive sequentially, causing performance drops. We argue that the short-term lookahead information available in modern logistics systems is key to mitigating this issue, especially during distribution shifts. We formulate online 3D-BP with lookahead parcels as a Model Predictive Control (MPC) problem and adapt the Monte Carlo Tree Search (MCTS) framework to solve it. Our framework employs a dynamic exploration prior that automatically balances a learned RL policy and a robust random policy based on the lookahead characteristics. Additionally, we design an auxiliary reward to penalize long-term spatial waste from individual placements. Extensive experiments on real-world datasets show that our method consistently outperforms state-of-the-art baselines, achieving over 10\% gains under distributional shifts, 4\% average improvement in online deployment, and up to more than 8\% in the best case--demonstrating the effectiveness of our framework. |
| title | Effective Online 3D Bin Packing with Lookahead Parcels Using Monte Carlo Tree Search |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2601.02649 |