# Stable Object Placement Planning From Contact Point Robustness

arXiv: https://arxiv.org/abs/2410.12483 · HTML: https://mlsanity.com/paper/2410.12483/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 51 references that are in the map.
- First posted on arXiv 16 Oct 2024.

## Builds on (most cited first, 10 of 10)

- [Hierarchical Planning for Long-Horizon Manipulation with Geometric and Symbolic Scene Graphs](https://mlsanity.com/paper/2012.07277/) (2020)
- [Regression Planning Networks](https://mlsanity.com/paper/1909.13072/) (2019)
- [Object Placement Planning and Optimization for Robot Manipulators](https://mlsanity.com/paper/1907.02555/) (2019)
- [Learning to Efficiently Plan Robust Frictional Multi-Object Grasps](https://mlsanity.com/paper/2210.07420/) (2022)
- [In-Hand Object-Dynamics Inference using Tactile Fingertips](https://mlsanity.com/paper/2003.13165/) (2020)
- [The Busboy Problem: Efficient Tableware Decluttering Using Consolidation and Multi-Object Grasps](https://mlsanity.com/paper/2307.03882/) (2023)
- [Object Reconfiguration with Simulation-Derived Feasible Actions](https://mlsanity.com/paper/2302.14161/) (2023)
- [A Probabilistic Model for Planar Sliding of Objects with Unknown Material Properties: Identification and Robust Planning](https://mlsanity.com/paper/2008.01921/) (2020)
- [The Sum of Its Parts: Visual Part Segmentation for Inertial Parameter Identification of Manipulated Objects](https://mlsanity.com/paper/2302.06685/) (2023)
- [A Standard Rigid Transformation Notation Convention for Robotics Research](https://mlsanity.com/paper/2405.07351/) (2024)

## Built on by (most cited first, 2 of 2)

- [GeoAlign: Beyond Semantics with State-Guided Spatial Alignment in VLA Models](https://mlsanity.com/paper/2606.03240/) (2026)
- [Generating Stable Placements via Physics-guided Diffusion Models](https://mlsanity.com/paper/2509.21664/) (2025)

## Abstract (arXiv)

We introduce a planner designed to guide robot manipulators in stably placing objects within intricate scenes. Our proposed method reverses the traditional approach to object placement: our planner selects contact points first and then determines a placement pose that solicits the selected points. This is instead of sampling poses, identifying contact points, and evaluating pose quality. Our algorithm facilitates stability-aware object placement planning, imposing no restrictions on object shape, convexity, or mass density homogeneity, while avoiding combinatorial computational complexity. Our proposed stability heuristic enables our planner to find a solution about 20 times faster when compared to the same algorithm not making use of the heuristic and eight times faster than a state-of-the-art method that uses the traditional sample-and-evaluate approach. Our proposed planner is also more successful in finding stable placements than the five other benchmarked algorithms. Derived from first principles and validated in ten real robot experiments, our planner offers a general and scalable method to tackle the problem of object placement planning with rigid objects.