# TAP-Net: Transport-and-Pack using Reinforcement Learning

arXiv: https://arxiv.org/abs/2009.01469 · HTML: https://mlsanity.com/paper/2009.01469/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2020.

## Built on by (most cited first, 12 of 12)

- [A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs](https://mlsanity.com/paper/2106.04927/) (2021)
- [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)
- [Planning Irregular Object Packing via Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2211.09382/) (2022)
- [Learning based 2D Irregular Shape Packing](https://mlsanity.com/paper/2309.10329/) (2023)
- [Machine Learning for the Multi-Dimensional Bin Packing Problem: Literature Review and Empirical Evaluation](https://mlsanity.com/paper/2312.08103/) (2023)
- [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)
- [RASP: Revisiting 3D Anamorphic Art for Shadow-Guided Packing of Irregular Objects](https://mlsanity.com/paper/2504.02465/) (2025)
- [Robotic Learning the Sequence of Packing Irregular Objects from Human Demonstrations](https://mlsanity.com/paper/2210.01645/) (2022)
- [Effective Online 3D Bin Packing with Lookahead Parcels Using Monte Carlo Tree Search](https://mlsanity.com/paper/2601.02649/) (2026)

## Abstract (arXiv)

We introduce the transport-and-pack(TAP) problem, a frequently encountered instance of real-world packing, and develop a neural optimization solution based on reinforcement learning. Given an initial spatial configuration of boxes, we seek an efficient method to iteratively transport and pack the boxes compactly into a target container. Due to obstruction and accessibility constraints, our problem has to add a new search dimension, i.e., finding an optimal transport sequence, to the already immense search space for packing alone. Using a learning-based approach, a trained network can learn and encode solution patterns to guide the solution of new problem instances instead of executing an expensive online search. In our work, we represent the transport constraints using a precedence graph and train a neural network, coined TAP-Net, using reinforcement learning to reward efficient and stable packing. The network is built on an encoder-decoder architecture, where the encoder employs convolution layers to encode the box geometry and precedence graph and the decoder is a recurrent neural network (RNN) which inputs the current encoder output, as well as the current box packing state of the target container, and outputs the next box to pack, as well as its orientation. We train our network on randomly generated initial box configurations, without supervision, via policy gradients to learn optimal TAP policies to maximize packing efficiency and stability. We demonstrate the performance of TAP-Net on a variety of examples, evaluating the network through ablation studies and comparisons to baselines and alternative network designs. We also show that our network generalizes well to larger problem instances, when trained on small-sized inputs.