# Unified Task and Motion Planning using Object-centric Abstractions of Motion Constraints

arXiv: https://arxiv.org/abs/2312.17605 · HTML: https://mlsanity.com/paper/2312.17605/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 29 Dec 2023.

## Builds on (most cited first, 8 of 8)

- [Integrated Task and Motion Planning](https://mlsanity.com/paper/2010.01083/) (2020)
- [PDDLStream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planning](https://mlsanity.com/paper/1802.08705/) (2018)
- [A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms](https://mlsanity.com/paper/1907.03146/) (2019)
- [Entity Abstraction in Visual Model-Based Reinforcement Learning](https://mlsanity.com/paper/1910.12827/) (2019)
- [Deep Object-Centric Representations for Generalizable Robot Learning](https://mlsanity.com/paper/1708.04225/) (2017)
- [Receding Horizon Task and Motion Planning in Changing Environments](https://mlsanity.com/paper/2009.03139/) (2020)
- [HATP: An HTN Planner for Robotics](https://mlsanity.com/paper/1405.5345/) (2014)
- [Efficient State Abstraction using Object-centered Predicates for Manipulation Planning](https://mlsanity.com/paper/2007.08251/) (2020)

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

- [A Survey of Optimization-based Task and Motion Planning: From Classical To Learning Approaches](https://mlsanity.com/paper/2404.02817/) (2024)
- [Leveraging Inter-object Affordances for Efficient Planning in Contact-rich Tasks](https://mlsanity.com/paper/2608.25641/) (2026)

## Abstract (arXiv)

In task and motion planning (TAMP), the ambiguity and underdetermination of abstract descriptions used by task planning methods make it difficult to characterize physical constraints needed to successfully execute a task. The usual approach is to overlook such constraints at task planning level and to implement expensive sub-symbolic geometric reasoning techniques that perform multiple calls on unfeasible actions, plan corrections, and re-planning until a feasible solution is found. We propose an alternative TAMP approach that unifies task and motion planning into a single heuristic search. Our approach is based on an object-centric abstraction of motion constraints that permits leveraging the computational efficiency of off-the-shelf AI heuristic search to yield physically feasible plans. These plans can be directly transformed into object and motion parameters for task execution without the need of intensive sub-symbolic geometric reasoning.