# Consolidating Kinematic Models to Promote Coordinated Mobile Manipulations

arXiv: https://arxiv.org/abs/2108.01264 · HTML: https://mlsanity.com/paper/2108.01264/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 44 references that are in the map.
- First posted on arXiv 3 Aug 2021.

## Builds on (most cited first, 6 of 6)

- [6-DOF GraspNet: Variational Grasp Generation for Object Manipulation](https://mlsanity.com/paper/1905.10520/) (2019)
- [PointNetGPD: Detecting Grasp Configurations from Point Sets](https://mlsanity.com/paper/1809.06267/) (2018)
- [Where to relocate?: Object rearrangement inside cluttered and confined environments for robotic manipulation](https://mlsanity.com/paper/2003.10863/) (2020)
- [Reconstructing Interactive 3D Scenes by Panoptic Mapping and CAD Model Alignments](https://mlsanity.com/paper/2103.16095/) (2021)
- [Walking Posture Adaptation for Legged Robot Navigation in Confined Spaces](https://mlsanity.com/paper/1901.10863/) (2019)
- [Towards Mobile Multi-Task Manipulation in a Confined and Integrated Environment with Irregular Objects](https://mlsanity.com/paper/2003.01776/) (2020)

## Built on by (most cited first, 8 of 8)

- [LLM3:Large Language Model-based Task and Motion Planning with Motion Failure Reasoning](https://mlsanity.com/paper/2403.11552/) (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)
- [M3Bench: Benchmarking Whole-body Motion Generation for Mobile Manipulation in 3D Scenes](https://mlsanity.com/paper/2410.06678/) (2024)
- [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 construct a Virtual Kinematic Chain (VKC) that readily consolidates the kinematics of the mobile base, the arm, and the object to be manipulated in mobile manipulations. Accordingly, a mobile manipulation task is represented by altering the state of the constructed VKC, which can be converted to a motion planning problem, formulated, and solved by trajectory optimization. This new VKC perspective of mobile manipulation allows a service robot to (i) produce well-coordinated motions, suitable for complex household environments, and (ii) perform intricate multi-step tasks while interacting with multiple objects without an explicit definition of intermediate goals. In simulated experiments, we validate these advantages by comparing the VKC-based approach with baselines that solely optimize individual components. The results manifest that VKC-based joint modeling and planning promote task success rates and produce more efficient trajectories.