# The Role of Embodiment in Intuitive Whole-Body Teleoperation for Mobile Manipulation

arXiv: https://arxiv.org/abs/2509.03222 · HTML: https://mlsanity.com/paper/2509.03222/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 48 references that are in the map.
- First posted on arXiv 3 Sep 2025.

## Builds on (most cited first, 17 of 17)

- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023)
- [Open X-Embodiment: Robotic Learning Datasets and RT-X Models](https://mlsanity.com/paper/2310.08864/) (2023)
- [DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset](https://mlsanity.com/paper/2403.12945/) (2024)
- [BridgeData V2: A Dataset for Robot Learning at Scale](https://mlsanity.com/paper/2308.12952/) (2023)
- [RoboNet: Large-Scale Multi-Robot Learning](https://mlsanity.com/paper/1910.11215/) (2019)
- [Learning Human-to-Humanoid Real-Time Whole-Body Teleoperation](https://mlsanity.com/paper/2403.04436/) (2024)
- [Multiple Interactions Made Easy (MIME): Large Scale Demonstrations Data for Imitation](https://mlsanity.com/paper/1810.07121/) (2018)
- [TeleMoMa: A Modular and Versatile Teleoperation System for Mobile Manipulation](https://mlsanity.com/paper/2403.07869/) (2024)
- [Human to Robot Whole-Body Motion Transfer](https://mlsanity.com/paper/1909.06278/) (2019)
- [NimbRo wins ANA Avatar XPRIZE Immersive Telepresence Competition: Human-Centric Evaluation and Lessons Learned](https://mlsanity.com/paper/2308.12238/) (2023)
- [Audio-based Roughness Sensing and Tactile Feedback for Haptic Perception in Telepresence](https://mlsanity.com/paper/2303.07186/) (2023)
- [NimbRo Rescue: Solving Disaster-Response Tasks through Mobile Manipulation Robot Momaro](https://mlsanity.com/paper/1810.01345/) (2018)
- [Integrating and Evaluating Visuo-tactile Sensing with Haptic Feedback for Teleoperated Robot Manipulation](https://mlsanity.com/paper/2404.19585/) (2024)
- [Low-Cost Teleoperation with Haptic Feedback through Vision-based Tactile Sensors for Rigid and Soft Object Manipulation](https://mlsanity.com/paper/2403.16764/) (2024)
- [Mixed Reality Teleoperation Assistance for Direct Control of Humanoids](https://mlsanity.com/paper/2411.01014/) (2024)
- [Quantitative Physical Ergonomics Assessment of Teleoperation Interfaces](https://mlsanity.com/paper/2105.09809/) (2021)

## Built on by (most cited first, 1 of 1)

- [Whole-Body Mobile Manipulation using Offline Reinforcement Learning on Sub-optimal Controllers](https://mlsanity.com/paper/2604.12509/) (2026)

## Abstract (arXiv)

Intuitive Teleoperation interfaces are essential for mobile manipulation robots to ensure high quality data collection while reducing operator workload. A strong sense of embodiment combined with minimal physical and cognitive demands not only enhances the user experience during large-scale data collection, but also helps maintain data quality over extended periods. This becomes especially crucial for challenging long-horizon mobile manipulation tasks that require whole-body coordination. We compare two distinct robot control paradigms: a coupled embodiment integrating arm manipulation and base navigation functions, and a decoupled embodiment treating these systems as separate control entities. Additionally, we evaluate two visual feedback mechanisms: immersive virtual reality and conventional screen-based visualization of the robot's field of view. These configurations were systematically assessed across a complex, multi-stage task sequence requiring integrated planning and execution. Our results show that the use of VR as a feedback modality increases task completion time, cognitive workload, and perceived effort of the teleoperator. Coupling manipulation and navigation leads to a comparable workload on the user as decoupling the embodiments, while preliminary experiments suggest that data acquired by coupled teleoperation leads to better imitation learning performance. Our holistic view on intuitive teleoperation interfaces provides valuable insight into collecting high-quality, high-dimensional mobile manipulation data at scale with the human operator in mind. Project website:https://sophiamoyen.github.io/role-embodiment-wbc-moma-teleop/