# Occlusion-Aware Search for Object Retrieval in Clutter

arXiv: https://arxiv.org/abs/2011.03334 · HTML: https://mlsanity.com/paper/2011.03334/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2020.

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

- [ILIAS: Instance-Level Image retrieval At Scale](https://mlsanity.com/paper/2502.11748/) (2025)
- [Play it by Ear: Learning Skills amidst Occlusion through Audio-Visual Imitation Learning](https://mlsanity.com/paper/2205.14850/) (2022)
- [Tracking through Containers and Occluders in the Wild](https://mlsanity.com/paper/2305.03052/) (2023)
- [A System for Generalized 3D Multi-Object Search](https://mlsanity.com/paper/2303.03178/) (2023)
- [Efficient and High-quality Prehensile Rearrangement in Cluttered and Confined Spaces](https://mlsanity.com/paper/2110.02814/) (2021)
- [Multi-Object Grasping in the Plane](https://mlsanity.com/paper/2206.00229/) (2022)
- [Smart Explorer: Recognizing Objects in Dense Clutter via Interactive Exploration](https://mlsanity.com/paper/2208.03496/) (2022)
- [Learning to Efficiently Plan Robust Frictional Multi-Object Grasps](https://mlsanity.com/paper/2210.07420/) (2022)
- [Grasp, See, and Place: Efficient Unknown Object Rearrangement with Policy Structure Prior](https://mlsanity.com/paper/2402.15402/) (2024)
- [Mechanical Search on Shelves using a Novel "Bluction" Tool](https://mlsanity.com/paper/2201.08968/) (2022)
- [Learning Dual-Arm Push and Grasp Synergy in Dense Clutter](https://mlsanity.com/paper/2412.04052/) (2024)
- [Gentle Object Retraction in Dense Clutter Using Multimodal Force Sensing and Imitation Learning](https://mlsanity.com/paper/2508.19476/) (2025)

## Abstract (arXiv)

We address the manipulation task of retrieving a target object from a cluttered shelf. When the target object is hidden, the robot must search through the clutter for retrieving it. Solving this task requires reasoning over the likely locations of the target object. It also requires physics reasoning over multi-object interactions and future occlusions. In this work, we present a data-driven hybrid planner for generating occlusion-aware actions in closed-loop. The hybrid planner explores likely locations of the occluded target object as predicted by a learned distribution from the observation stream. The search is guided by a heuristic trained with reinforcement learning to act on observations with occlusions. We evaluate our approach in different simulation and real-world settings (video available on https://youtu.be/dY7YQ3LUVQg). The results validate that our approach can search and retrieve a target object in near real time in the real world while only being trained in simulation.