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Dynamic Mobile Manipulation via Whole-Body Bilateral Teleoperation of a Wheeled Humanoid

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arxiv 2307.01350 v1 pith:ZWNUTU7Q submitted 2023-07-03 cs.RO

Dynamic Mobile Manipulation via Whole-Body Bilateral Teleoperation of a Wheeled Humanoid

classification cs.RO
keywords humanoidmanipulationtasksdynamicteleoperationwhole-bodyboxesframework
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Humanoid robots have the potential to help human workers by realizing physically demanding manipulation tasks such as moving large boxes within warehouses. We define such tasks as Dynamic Mobile Manipulation (DMM). This paper presents a framework for DMM via whole-body teleoperation, built upon three key contributions: Firstly, a teleoperation framework employing a Human Machine Interface (HMI) and a bi-wheeled humanoid, SATYRR, is proposed. Secondly, the study introduces a dynamic locomotion mapping, utilizing human-robot reduced order models, and a kinematic retargeting strategy for manipulation tasks. Additionally, the paper discusses the role of whole-body haptic feedback for wheeled humanoid control. Finally, the system's effectiveness and mappings for DMM are validated through locomanipulation experiments and heavy box pushing tasks. Here we show two forms of DMM: grasping a target moving at an average speed of 0.4 m/s, and pushing boxes weighing up to 105\% of the robot's weight. By simultaneously adjusting their pitch and using their arms, the pilot adjusts the robot pose to apply larger contact forces and move a heavy box at a constant velocity of 0.2 m/s.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. MDIR: A Task-Manifold Impedance Retargeting Method for Contact-Rich Teleoperation

    cs.RO 2026-07 conditional novelty 6.0

    MDIR reparameterizes a single fixed Cartesian impedance demonstration into a task-channel variable-impedance controller that passes all 15 closed-loop task checks while reducing force peaks, impulse, force variability...

  2. Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation

    cs.RO 2024-01 conditional novelty 6.0

    A low-cost whole-body teleoperation system enables effective imitation learning for complex bimanual mobile manipulation by co-training on mobile and static demonstration datasets.