# Fast and resilient manipulation planning for target retrieval in clutter

arXiv: https://arxiv.org/abs/2003.11420 · HTML: https://mlsanity.com/paper/2003.11420/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2020.

## Built on by (most cited first, 7 of 7)

- [Uniform Object Rearrangement: From Complete Monotone Primitives to Efficient Non-Monotone Informed Search](https://mlsanity.com/paper/2101.12241/) (2021)
- [Object and Relation Centric Representations for Push Effect Prediction](https://mlsanity.com/paper/2102.02100/) (2021)
- [Smart Explorer: Recognizing Objects in Dense Clutter via Interactive Exploration](https://mlsanity.com/paper/2208.03496/) (2022)
- [Semi-Autonomous Teleoperation via Learning Non-Prehensile Manipulation Skills](https://mlsanity.com/paper/2109.13081/) (2021)
- [Planning for Manipulation among Movable Objects: Deciding Which Objects Go Where, in What Order, and How](https://mlsanity.com/paper/2303.13385/) (2023)
- [Learning Dual-arm Object Rearrangement for Cartesian Robots](https://mlsanity.com/paper/2402.13634/) (2024)
- [ClutterNav: Gradient-Guided Search for Efficient 3D Clutter Removal with Learned Costmaps](https://mlsanity.com/paper/2511.12479/) (2025)

## Abstract (arXiv)

This paper presents a task and motion planning (TAMP) framework for a robotic manipulator in order to retrieve a target object from clutter. We consider a configuration of objects in a confined space with a high density so no collision-free path to the target exists. The robot must relocate some objects to retrieve the target without collisions. For fast completion of object rearrangement, the robot aims to optimize the number of pick-and-place actions which often determines the efficiency of a TAMP framework. We propose a task planner incorporating motion planning to generate executable plans which aims to minimize the number of pick-and-place actions. In addition to fully known and static environments, our method can deal with uncertain and dynamic situations incurred by occluded views. Our method is shown to reduce the number of pick-and-place actions compared to baseline methods (e.g., at least 28.0% of reduction in a known static environment with 20 objects).