# Mechanical Search on Shelves using a Novel "Bluction" Tool

arXiv: https://arxiv.org/abs/2201.08968 · HTML: https://mlsanity.com/paper/2201.08968/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 29 references that are in the map.
- First posted on arXiv 22 Jan 2022.

## Builds on (most cited first, 17 of 17)

- [Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning](https://mlsanity.com/paper/1803.09956/) (2018)
- [Multi-view Self-supervised Deep Learning for 6D Pose Estimation in the Amazon Picking Challenge](https://mlsanity.com/paper/1609.09475/) (2016)
- [SuctionNet-1Billion: A Large-Scale Benchmark for Suction Grasping](https://mlsanity.com/paper/2103.12311/) (2021)
- [Mechanical Search: Multi-Step Retrieval of a Target Object Occluded by Clutter](https://mlsanity.com/paper/1903.01588/) (2019)
- [Cartman: The low-cost Cartesian Manipulator that won the Amazon Robotics Challenge](https://mlsanity.com/paper/1709.06283/) (2017)
- [A Deep Learning Approach to Grasping the Invisible](https://mlsanity.com/paper/1909.04840/) (2019)
- [Object Finding in Cluttered Scenes Using Interactive Perception](https://mlsanity.com/paper/1911.07482/) (2019)
- [Visuomotor Mechanical Search: Learning to Retrieve Target Objects in Clutter](https://mlsanity.com/paper/2008.06073/) (2020)
- [Occlusion-Aware Search for Object Retrieval in Clutter](https://mlsanity.com/paper/2011.03334/) (2020)
- [Team Delft's Robot Winner of the Amazon Picking Challenge 2016](https://mlsanity.com/paper/1610.05514/) (2016)
- [X-Ray: Mechanical Search for an Occluded Object by Minimizing Support of Learned Occupancy Distributions](https://mlsanity.com/paper/2004.09039/) (2020)
- [Efficient and High-quality Prehensile Rearrangement in Cluttered and Confined Spaces](https://mlsanity.com/paper/2110.02814/) (2021)
- [Mechanical Search on Shelves using Lateral Access X-RAY](https://mlsanity.com/paper/2011.11696/) (2020)
- [Inferring Occluded Geometry Improves Performance when Retrieving an Object from Dense Clutter](https://mlsanity.com/paper/1907.08770/) (2019)
- [A Summary of Team MIT's Approach to the Amazon Picking Challenge 2015](https://mlsanity.com/paper/1604.03639/) (2016)
- [Bimanual Shelf Picking Planner Based on Collapse Prediction](https://mlsanity.com/paper/2105.14764/) (2021)
- [Efficient Computation of Visibility Polygons](https://mlsanity.com/paper/1403.3905/) (2014)

## Built on by (most cited first, 3 of 3)

- [All You Need is LUV: Unsupervised Collection of Labeled Images using Invisible UV Fluorescent Indicators](https://mlsanity.com/paper/2203.04566/) (2022)
- [BOMP: Bin-Optimized Motion Planning](https://mlsanity.com/paper/2411.00221/) (2024)
- [Gentle Object Retraction in Dense Clutter Using Multimodal Force Sensing and Imitation Learning](https://mlsanity.com/paper/2508.19476/) (2025)

## Abstract (arXiv)

Shelves are common in homes, warehouses, and commercial settings due to their storage efficiency. However, this efficiency comes at the cost of reduced visibility and accessibility. When looking from a side (lateral) view of a shelf, most objects will be fully occluded, resulting in a constrained lateral-access mechanical search problem. To address this problem, we introduce: (1) a novel bluction tool, which combines a thin pushing blade and suction cup gripper, (2) an improved LAX-RAY simulation pipeline and perception model that combines ray-casting with 2D Minkowski sums to efficiently generate target occupancy distributions, and (3) a novel SLAX-RAY search policy, which optimally reduces target object distribution support area using the bluction tool. Experimental data from 2000 simulated shelf trials and 18 trials with a physical Fetch robot equipped with the bluction tool suggest that using suction grasping actions improves the success rate over the highest performing push-only policy by 26% in simulation and 67% in physical environments.