# Object Placement Planning and Optimization for Robot Manipulators

arXiv: https://arxiv.org/abs/1907.02555 · HTML: https://mlsanity.com/paper/1907.02555/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2019.

## Built on by (most cited first, 9 of 9)

- [O2O-Afford: Annotation-Free Large-Scale Object-Object Affordance Learning](https://mlsanity.com/paper/2106.15087/) (2021)
- [Composing Pick-and-Place Tasks By Grounding Language](https://mlsanity.com/paper/2102.08094/) (2021)
- [Learning to Regrasp by Learning to Place](https://mlsanity.com/paper/2109.08817/) (2021)
- [Slot-Level Robotic Placement via Visual Imitation from Single Human Video](https://mlsanity.com/paper/2504.01959/) (2025)
- [Stable Object Placement Planning From Contact Point Robustness](https://mlsanity.com/paper/2410.12483/) (2024)
- [SPOTS: Stable Placement of Objects with Reasoning in Semi-Autonomous Teleoperation Systems](https://mlsanity.com/paper/2309.13937/) (2023)
- [Working Backwards: Learning to Place by Picking](https://mlsanity.com/paper/2312.02352/) (2023)
- [Generating Stable Placements via Physics-guided Diffusion Models](https://mlsanity.com/paper/2509.21664/) (2025)
- [Placeit! A Framework for Learning Robot Object Placement Skills](https://mlsanity.com/paper/2510.09267/) (2025)

## Abstract (arXiv)

We address the problem of motion planning for a robotic manipulator with the task to place a grasped object in a cluttered environment. In this task, we need to locate a collision-free pose for the object that a) facilitates the stable placement of the object, b) is reachable by the robot manipulator and c) optimizes a user-given placement objective. Because of the placement objective, this problem is more challenging than classical motion planning where the target pose is defined from the start. To solve this task, we propose an anytime algorithm that integrates sampling-based motion planning for the robot manipulator with a novel hierarchical search for suitable placement poses. We evaluate our approach on a dual-arm robot for two different placement objectives, and observe its effectiveness even in challenging scenarios.