# Stable bin packing of non-convex 3D objects with a robot manipulator

arXiv: https://arxiv.org/abs/1812.04093 · HTML: https://mlsanity.com/paper/1812.04093/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2018.

## Built on by (most cited first, 21 of 21)

- [O2O-Afford: Annotation-Free Large-Scale Object-Object Affordance Learning](https://mlsanity.com/paper/2106.15087/) (2021)
- [Neural Shape Mating: Self-Supervised Object Assembly with Adversarial Shape Priors](https://mlsanity.com/paper/2205.14886/) (2022)
- [Compositional Diffusion-Based Continuous Constraint Solvers](https://mlsanity.com/paper/2309.00966/) (2023)
- [Learning Generalizable Feature Fields for Mobile Manipulation](https://mlsanity.com/paper/2403.07563/) (2024)
- [Learning Physically Realizable Skills for Online Packing of General 3D Shapes](https://mlsanity.com/paper/2212.02094/) (2022)
- [Adjustable Robust Reinforcement Learning for Online 3D Bin Packing](https://mlsanity.com/paper/2310.04323/) (2023)
- [3D Geometric Shape Assembly via Efficient Point Cloud Matching](https://mlsanity.com/paper/2407.10542/) (2024)
- [Planning Irregular Object Packing via Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2211.09382/) (2022)
- [Machine Learning for the Multi-Dimensional Bin Packing Problem: Literature Review and Empirical Evaluation](https://mlsanity.com/paper/2312.08103/) (2023)
- [Bin Packing Optimization via Deep Reinforcement Learning](https://mlsanity.com/paper/2403.12420/) (2024)
- [GOPT: Generalizable Online 3D Bin Packing via Transformer-based Deep Reinforcement Learning](https://mlsanity.com/paper/2409.05344/) (2024)
- [Deliberate Planning of 3D Bin Packing on Packing Configuration Trees](https://mlsanity.com/paper/2504.04421/) (2025)
- [Learning Gradient Fields for Scalable and Generalizable Irregular Packing](https://mlsanity.com/paper/2310.19814/) (2023)
- [Neural Packing: from Visual Sensing to Reinforcement Learning](https://mlsanity.com/paper/2311.09233/) (2023)
- [Efficient Reinforcement Learning of Task Planners for Robotic Palletization through Iterative Action Masking Learning](https://mlsanity.com/paper/2404.04772/) (2024)
- [Convolutional Occupancy Models for Dense Packing of Complex, Novel Objects](https://mlsanity.com/paper/2308.00091/) (2023)
- [Robotic Learning the Sequence of Packing Irregular Objects from Human Demonstrations](https://mlsanity.com/paper/2210.01645/) (2022)
- [Low Resolution Next Best View for Robot Packing](https://mlsanity.com/paper/2505.04228/) (2025)
- [OPA-Pack: Object-Property-Aware Robotic Bin Packing](https://mlsanity.com/paper/2505.13339/) (2025)
- [Effective Online 3D Bin Packing with Lookahead Parcels Using Monte Carlo Tree Search](https://mlsanity.com/paper/2601.02649/) (2026)
- [Diffusion Reinforcement Learning Based Online 3D Bin Packing Spatial Strategy Optimization](https://mlsanity.com/paper/2604.10953/) (2026)

## Abstract (arXiv)

Recent progress in the field of robotic manipulation has generated interest in fully automatic object packing in warehouses. This paper proposes a formulation of the packing problem that is tailored to the automated warehousing domain. Besides minimizing waste space inside a container, the problem requires stability of the object pile during packing and the feasibility of the robot motion executing the placement plans. To address this problem, a set of constraints are formulated, and a constructive packing pipeline is proposed to solve for these constraints. The pipeline is able to pack geometrically complex, non-convex objects with stability while satisfying robot constraints. In particular, a new 3D positioning heuristic called Heightmap-Minimization heuristic is proposed, and heightmaps are used to speed up the search. Experimental evaluation of the method is conducted with a realistic physical simulator on a dataset of scanned real-world items, demonstrating stable and high-quality packing plans compared with other 3D packing methods.