mlsanityData to 14 Oct 2026

Consolidating Kinematic Models to Promote Coordinated Mobile Manipulations

arXiv 2108.01264 ↗First posted 3 Aug 2021cs.RO cs.AIcited by 8

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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.

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

6 of 44 references.

References · 6

  1. 20196-DOF GraspNet: Variational Grasp Generation for Object Manipulationcited by 242
  2. 2018PointNetGPD: Detecting Grasp Configurations from Point Setscited by 80
  3. 2020Where to relocate?: Object rearrangement inside cluttered and confined environments for robotic manipulationcited by 16
  4. 2021Reconstructing Interactive 3D Scenes by Panoptic Mapping and CAD Model Alignmentscited by 15
  5. 2019Walking Posture Adaptation for Legged Robot Navigation in Confined Spacescited by 2
  6. 2020Towards Mobile Multi-Task Manipulation in a Confined and Integrated Environment with Irregular Objectscited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 3; 2023: 0; 2024: 3; 2025: 1; 2026: 1.

8 papers.