# MOMA-Force: Visual-Force Imitation for Real-World Mobile Manipulation

arXiv: https://arxiv.org/abs/2308.03624 · HTML: https://mlsanity.com/paper/2308.03624/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 35 references that are in the map.
- First posted on arXiv 7 Aug 2023.

## Builds on (most cited first, 24 of 24)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [RT-1: Robotics Transformer for Real-World Control at Scale](https://mlsanity.com/paper/2212.06817/) (2022)
- [Ego4D: Around the World in 3,000 Hours of Egocentric Video](https://mlsanity.com/paper/2110.07058/) (2021)
- [iBOT: Image BERT Pre-Training with Online Tokenizer](https://mlsanity.com/paper/2111.07832/) (2021)
- [R3M: A Universal Visual Representation for Robot Manipulation](https://mlsanity.com/paper/2203.12601/) (2022)
- [Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation](https://mlsanity.com/paper/2209.05451/) (2022)
- [BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning](https://mlsanity.com/paper/2202.02005/) (2022)
- [Behavioral Cloning from Observation](https://mlsanity.com/paper/1805.01954/) (2018)
- [Implicit Behavioral Cloning](https://mlsanity.com/paper/2109.00137/) (2021)
- [Masked Visual Pre-training for Motor Control](https://mlsanity.com/paper/2203.06173/) (2022)
- [Real-World Robot Learning with Masked Visual Pre-training](https://mlsanity.com/paper/2210.03109/) (2022)
- [Language-Driven Representation Learning for Robotics](https://mlsanity.com/paper/2302.12766/) (2023)
- [The Surprising Effectiveness of Representation Learning for Visual Imitation](https://mlsanity.com/paper/2112.01511/) (2021)
- [Deep Whole-Body Control: Learning a Unified Policy for Manipulation and Locomotion](https://mlsanity.com/paper/2210.10044/) (2022)
- [Habitat-Web: Learning Embodied Object-Search Strategies from Human Demonstrations at Scale](https://mlsanity.com/paper/2204.03514/) (2022)
- [MaskViT: Masked Visual Pre-Training for Video Prediction](https://mlsanity.com/paper/2206.11894/) (2022)
- [Visual Imitation Made Easy](https://mlsanity.com/paper/2008.04899/) (2020)
- [Error-Aware Imitation Learning from Teleoperation Data for Mobile Manipulation](https://mlsanity.com/paper/2112.05251/) (2021)
- [Articulated Object Interaction in Unknown Scenes with Whole-Body Mobile Manipulation](https://mlsanity.com/paper/2103.10534/) (2021)
- [A Holistic Approach to Reactive Mobile Manipulation](https://mlsanity.com/paper/2109.04749/) (2021)
- [Whole-Body MPC for a Dynamically Stable Mobile Manipulator](https://mlsanity.com/paper/1902.10415/) (2019)
- [Learning compliant grasping and manipulation by teleoperation with adaptive force control](https://mlsanity.com/paper/2107.08996/) (2021)
- [Constraint Handling in Continuous-Time DDP-Based Model Predictive Control](https://mlsanity.com/paper/2101.06067/) (2021)
- [NEO: A Novel Expeditious Optimisation Algorithm for Reactive Motion Control of Manipulators](https://mlsanity.com/paper/2010.08686/) (2020)

## Built on by (most cited first, 9 of 9)

- [Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation](https://mlsanity.com/paper/2401.02117/) (2024)
- [GR-2: A Generative Video-Language-Action Model with Web-Scale Knowledge for Robot Manipulation](https://mlsanity.com/paper/2410.06158/) (2024)
- [Visual Whole-Body Control for Legged Loco-Manipulation](https://mlsanity.com/paper/2403.16967/) (2024)
- [ForceMimic: Force-Centric Imitation Learning with Force-Motion Capture System for Contact-Rich Manipulation](https://mlsanity.com/paper/2410.07554/) (2024)
- [TeleMoMa: A Modular and Versatile Teleoperation System for Mobile Manipulation](https://mlsanity.com/paper/2403.07869/) (2024)
- [Whole-Body Teleoperation for Mobile Manipulation at Zero Added Cost](https://mlsanity.com/paper/2409.15095/) (2024)
- [Object-Centric Mobile Manipulation through SAM2-Guided Perception and Imitation Learning](https://mlsanity.com/paper/2507.10899/) (2025)
- [Scalable Trajectory Generation for Whole-Body Mobile Manipulation](https://mlsanity.com/paper/2604.12565/) (2026)
- [Spacetime Optimal-Transport Attention for Visuo-Haptic Imitation Learning of Contact-Rich Manipulation](https://mlsanity.com/paper/2605.20433/) (2026)

## Abstract (arXiv)

In this paper, we present a novel method for mobile manipulators to perform multiple contact-rich manipulation tasks. While learning-based methods have the potential to generate actions in an end-to-end manner, they often suffer from insufficient action accuracy and robustness against noise. On the other hand, classical control-based methods can enhance system robustness, but at the cost of extensive parameter tuning. To address these challenges, we present MOMA-Force, a visual-force imitation method that seamlessly combines representation learning for perception, imitation learning for complex motion generation, and admittance whole-body control for system robustness and controllability. MOMA-Force enables a mobile manipulator to learn multiple complex contact-rich tasks with high success rates and small contact forces. In a real household setting, our method outperforms baseline methods in terms of task success rates. Moreover, our method achieves smaller contact forces and smaller force variances compared to baseline methods without force imitation. Overall, we offer a promising approach for efficient and robust mobile manipulation in the real world. Videos and more details can be found on urlhttps://visual-force-imitation.github.io