# Efficient Task Planning for Mobile Manipulation: a Virtual Kinematic Chain Perspective

arXiv: https://arxiv.org/abs/2108.01259 · HTML: https://mlsanity.com/paper/2108.01259/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 34 references that are in the map.
- First posted on arXiv 3 Aug 2021.

## Builds on (most cited first, 3 of 3)

- [Integrated Task and Motion Planning](https://mlsanity.com/paper/2010.01083/) (2020)
- [FFRob: Leveraging Symbolic Planning for Efficient Task and Motion Planning](https://mlsanity.com/paper/1608.01335/) (2016)
- [Learning to guide task and motion planning using score-space representation](https://mlsanity.com/paper/1807.09962/) (2018)

## Built on by (most cited first, 11 of 11)

- [LLM3:Large Language Model-based Task and Motion Planning with Motion Failure Reasoning](https://mlsanity.com/paper/2403.11552/) (2024)
- [Closed-Loop Open-Vocabulary Mobile Manipulation with GPT-4V](https://mlsanity.com/paper/2404.10220/) (2024)
- [M2Diffuser: Diffusion-based Trajectory Optimization for Mobile Manipulation in 3D Scenes](https://mlsanity.com/paper/2410.11402/) (2024)
- [Sequential Manipulation Planning on Scene Graph](https://mlsanity.com/paper/2207.04364/) (2022)
- [On the Learning Mechanisms in Physical Reasoning](https://mlsanity.com/paper/2210.02075/) (2022)
- [Understanding Physical Effects for Effective Tool-use](https://mlsanity.com/paper/2206.14998/) (2022)
- [LLM+MAP: Bimanual Robot Task Planning using Large Language Models and Planning Domain Definition Language](https://mlsanity.com/paper/2503.17309/) (2025)
- [M3Bench: Benchmarking Whole-body Motion Generation for Mobile Manipulation in 3D Scenes](https://mlsanity.com/paper/2410.06678/) (2024)
- [Rearrange Indoor Scenes for Human-Robot Co-Activity](https://mlsanity.com/paper/2303.05676/) (2023)
- [IKDiffuser: a Diffusion-based Generative Inverse Kinematics Solver for Kinematic Trees](https://mlsanity.com/paper/2506.13087/) (2025)
- [Scalable Trajectory Generation for Whole-Body Mobile Manipulation](https://mlsanity.com/paper/2604.12565/) (2026)

## Abstract (arXiv)

We present a Virtual Kinematic Chain (VKC) perspective, a simple yet effective method, to improve task planning efficacy for mobile manipulation. By consolidating the kinematics of the mobile base, the arm, and the object being manipulated collectively as a whole, this novel VKC perspective naturally defines abstract actions and eliminates unnecessary predicates in describing intermediate poses. As a result, these advantages simplify the design of the planning domain and significantly reduce the search space and branching factors in solving planning problems. In experiments, we implement a task planner using Planning Domain Definition Language (PDDL) with VKC. Compared with conventional domain definition, our VKC-based domain definition is more efficient in both planning time and memory. In addition, abstract actions perform better in producing feasible motion plans and trajectories. We further scale up the VKC-based task planner in complex mobile manipulation tasks. Taken together, these results demonstrate that task planning using VKC for mobile manipulation is not only natural and effective but also introduces new capabilities.