# FFRob: Leveraging Symbolic Planning for Efficient Task and Motion Planning

arXiv: https://arxiv.org/abs/1608.01335 · HTML: https://mlsanity.com/paper/1608.01335/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2016.

## Built on by (most cited first, 30 of 34)

- [AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkers](https://mlsanity.com/paper/2306.06531/) (2023)
- [Visual Room Rearrangement](https://mlsanity.com/paper/2103.16544/) (2021)
- [Error-Aware Imitation Learning from Teleoperation Data for Mobile Manipulation](https://mlsanity.com/paper/2112.05251/) (2021)
- [Interactive Task Planning with Language Models](https://mlsanity.com/paper/2310.10645/) (2023)
- [From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence](https://mlsanity.com/paper/2110.15245/) (2021)
- [Sequential Manipulation Planning on Scene Graph](https://mlsanity.com/paper/2207.04364/) (2022)
- [N2M2: Learning Navigation for Arbitrary Mobile Manipulation Motions in Unseen and Dynamic Environments](https://mlsanity.com/paper/2206.08737/) (2022)
- [Extended Tree Search for Robot Task and Motion Planning](https://mlsanity.com/paper/2103.05456/) (2021)
- [Efficient Task Planning for Mobile Manipulation: a Virtual Kinematic Chain Perspective](https://mlsanity.com/paper/2108.01259/) (2021)
- [Representation, learning, and planning algorithms for geometric task and motion planning](https://mlsanity.com/paper/2203.04605/) (2022)
- [Optimal task and motion planning and execution for human-robot multi-agent systems in dynamic environments](https://mlsanity.com/paper/2303.14874/) (2023)
- [MPTP: Motion-Planning-aware Task Planning for Navigation in Belief Space](https://mlsanity.com/paper/2104.04696/) (2021)
- [Task Allocation for Multi-Robot Task and Motion Planning: a case for Object Picking in Cluttered Workspaces](https://mlsanity.com/paper/2110.04089/) (2021)
- [Object Manipulation via Visual Target Localization](https://mlsanity.com/paper/2203.08141/) (2022)
- [Computational Tradeoff in Minimum Obstacle Displacement Planning for Robot Navigation](https://mlsanity.com/paper/2302.07114/) (2023)
- [Minimum Displacement Motion Planning for Movable Obstacles](https://mlsanity.com/paper/2204.12740/) (2022)
- [STAMP: Differentiable Task and Motion Planning via Stein Variational Gradient Descent](https://mlsanity.com/paper/2310.01775/) (2023)
- [A Task-Motion Planning Framework Using Iteratively Deepened AND/OR Graph Networks](https://mlsanity.com/paper/2104.01549/) (2021)
- [6-DoF Stability Field via Diffusion Models](https://mlsanity.com/paper/2310.17649/) (2023)
- [A Task and Motion Planning Framework Using Iteratively Deepened AND/OR Graph Networks](https://mlsanity.com/paper/2503.07700/) (2025)
- [RobKiNet: Robotic Kinematics Informed Neural Network for Optimal Robot Configuration Prediction](https://mlsanity.com/paper/2402.16281/) (2024)
- [A Meta-Engine Framework for Interleaved Task and Motion Planning using Topological Refinements](https://mlsanity.com/paper/2408.05795/) (2024)
- [Locally Optimal Solutions to Constraint Displacement Problems via Path-Obstacle Overlaps](https://mlsanity.com/paper/2511.12203/) (2025)
- [Abstract Interpretation for Generalized Heuristic Search in Model-Based Planning](https://mlsanity.com/paper/2208.02938/) (2022)
- [Long-HOT: A Modular Hierarchical Approach for Long-Horizon Object Transport](https://mlsanity.com/paper/2210.15908/) (2022)
- [Interpretable Responsibility Sharing as a Heuristic for Task and Motion Planning](https://mlsanity.com/paper/2409.05586/) (2024)
- [A Framework for Task and Motion Planning based on Expanding AND/OR Graphs](https://mlsanity.com/paper/2509.00317/) (2025)
- [Placeit! A Framework for Learning Robot Object Placement Skills](https://mlsanity.com/paper/2510.09267/) (2025)
- [Interleaving Scheduling and Motion Planning with Incremental Learning of Symbolic Space-Time Motion Abstractions](https://mlsanity.com/paper/2603.10651/) (2026)
- [Virtues of Ordered Chaos: Planning with Topple Actions in Tabletop Stack Rearrangement](https://mlsanity.com/paper/2605.17815/) (2026)

## Abstract (arXiv)

Mobile manipulation problems involving many objects are challenging to solve due to the high dimensionality and multi-modality of their hybrid configuration spaces. Planners that perform a purely geometric search are prohibitively slow for solving these problems because they are unable to factor the configuration space. Symbolic task planners can efficiently construct plans involving many variables but cannot represent the geometric and kinematic constraints required in manipulation. We present the FFRob algorithm for solving task and motion planning problems. First, we introduce Extended Action Specification (EAS) as a general purpose planning representation that supports arbitrary predicates as conditions. We adapt existing heuristic search ideas for solving procstrips planning problems, particularly delete-relaxations, to solve EAS problem instances. We then apply the EAS representation and planners to manipulation problems resulting in FFRob. FFRob iteratively discretizes task and motion planning problems using batch sampling of manipulation primitives and a multi-query roadmap structure that can be conditionalized to evaluate reachability under different placements of movable objects. This structure enables the EAS planner to efficiently compute heuristics that incorporate geometric and kinematic planning constraints to give a tight estimate of the distance to the goal. Additionally, we show FFRob is probabilistically complete and has finite expected runtime. Finally, we empirically demonstrate FFRob's effectiveness on complex and diverse task and motion planning tasks including rearrangement planning and navigation among movable objects.