# BOMP: Bin-Optimized Motion Planning

arXiv: https://arxiv.org/abs/2411.00221 · HTML: https://mlsanity.com/paper/2411.00221/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 35 references that are in the map.
- First posted on arXiv 31 Oct 2024.

## Builds on (most cited first, 14 of 14)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [Fast Segment Anything](https://mlsanity.com/paper/2306.12156/) (2023)
- [Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics](https://mlsanity.com/paper/1703.09312/) (2017)
- [Learning Sampling Distributions for Robot Motion Planning](https://mlsanity.com/paper/1709.05448/) (2017)
- [Motion Planning Networks](https://mlsanity.com/paper/1806.05767/) (2018)
- [Motion Planning around Obstacles with Convex Optimization](https://mlsanity.com/paper/2205.04422/) (2022)
- [Experience-Based Planning with Sparse Roadmap Spanners](https://mlsanity.com/paper/1410.1950/) (2014)
- [Neural Informed RRT*: Learning-based Path Planning with Point Cloud State Representations under Admissible Ellipsoidal Constraints](https://mlsanity.com/paper/2309.14595/) (2023)
- [Motion Memory: Leveraging Past Experiences to Accelerate Future Motion Planning](https://mlsanity.com/paper/2310.06198/) (2023)
- [Implicit Graph Search for Planning on Graphs of Convex Sets](https://mlsanity.com/paper/2410.08909/) (2024)
- [Learning How to Walk: Warm-starting Optimal Control Solver with Memory of Motion](https://mlsanity.com/paper/2001.11751/) (2020)
- [GOMP: Grasp-Optimized Motion Planning for Bin Picking](https://mlsanity.com/paper/2003.02401/) (2020)
- [Mechanical Search on Shelves using a Novel "Bluction" Tool](https://mlsanity.com/paper/2201.08968/) (2022)

## Built on by (most cited first, 1 of 1)

- [RoboVista: Evaluating Vision Language Models for Diverse Robot Applications](https://mlsanity.com/paper/2607.04610/) (2026)

## Abstract (arXiv)

In logistics, the ability to quickly compute and execute pick-and-place motions from bins is critical to increasing productivity. We present Bin-Optimized Motion Planning (BOMP), a motion planning framework that plans arm motions for a six-axis industrial robot with a long-nosed suction tool to remove boxes from deep bins. BOMP considers robot arm kinematics, actuation limits, the dimensions of a grasped box, and a varying height map of a bin environment to rapidly generate time-optimized, jerk-limited, and collision-free trajectories. The optimization is warm-started using a deep neural network trained offline in simulation with 25,000 scenes and corresponding trajectories. Experiments with 96 simulated and 15 physical environments suggest that BOMP generates collision-free trajectories that are up to 58 % faster than baseline sampling-based planners and up to 36 % faster than an industry-standard Up-Over-Down algorithm, which has an extremely low 15 % success rate in this context. BOMP also generates jerk-limited trajectories while baselines do not. Website: https://sites.google.com/berkeley.edu/bomp.