# Force-Modulated Dexterity: Unified Compliance for Contact-Rich Manipulation

arXiv: https://arxiv.org/abs/2610.17161 · HTML: https://mlsanity.com/paper/2610.17161/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 37 references that are in the map.
- First posted on arXiv 12 Oct 2026.

## Builds on (most cited first, 26 of 26)

- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [AnyTeleop: A General Vision-Based Dexterous Robot Arm-Hand Teleoperation System](https://mlsanity.com/paper/2307.04577/) (2023)
- [DexPilot: Vision Based Teleoperation of Dexterous Robotic Hand-Arm System](https://mlsanity.com/paper/1910.03135/) (2019)
- [From One Hand to Multiple Hands: Imitation Learning for Dexterous Manipulation from Single-Camera Teleoperation](https://mlsanity.com/paper/2204.12490/) (2022)
- [Bunny-VisionPro: Real-Time Bimanual Dexterous Teleoperation for Imitation Learning](https://mlsanity.com/paper/2407.03162/) (2024)
- [DexUMI: Using Human Hand as the Universal Manipulation Interface for Dexterous Manipulation](https://mlsanity.com/paper/2505.21864/) (2025)
- [Adaptive Compliance Policy: Learning Approximate Compliance for Diffusion Guided Control](https://mlsanity.com/paper/2410.09309/) (2024)
- [Compliant Residual DAgger: Improving Real-World Contact-Rich Manipulation with Human Corrections](https://mlsanity.com/paper/2506.16685/) (2025)
- [Bimanual Dexterity for Complex Tasks](https://mlsanity.com/paper/2411.13677/) (2024)
- [DexForce: Extracting Force-informed Actions from Kinesthetic Demonstrations for Dexterous Manipulation](https://mlsanity.com/paper/2501.10356/) (2025)
- [DOGlove: Dexterous Manipulation with a Low-Cost Open-Source Haptic Force Feedback Glove](https://mlsanity.com/paper/2502.07730/) (2025)
- [Variable impedance control and learning -- A review](https://mlsanity.com/paper/2010.06246/) (2020)
- [In-the-Wild Compliant Manipulation with UMI-FT](https://mlsanity.com/paper/2601.09988/) (2026)
- [Robotic Compliant Object Prying Using Diffusion Policy Guided by Vision and Force Observations](https://mlsanity.com/paper/2503.03998/) (2025)
- [OSMO: Open-Source Tactile Glove for Human-to-Robot Skill Transfer](https://mlsanity.com/paper/2512.08920/) (2025)
- [Force Policy: Learning Hybrid Force-Position Control Policy under Interaction Frame for Contact-Rich Manipulation](https://mlsanity.com/paper/2602.22088/) (2026)
- [Learning Dexterous Manipulation Skills from Imperfect Simulations](https://mlsanity.com/paper/2512.02011/) (2025)
- [Learning compliant grasping and manipulation by teleoperation with adaptive force control](https://mlsanity.com/paper/2107.08996/) (2021)
- [Contact-Grounded Policy: Dexterous Visuotactile Policy with Generative Contact Grounding](https://mlsanity.com/paper/2603.05687/) (2026)
- [ForceBand: Learning Forceful Manipulation with sEMG](https://mlsanity.com/paper/2606.26093/) (2026)
- [Learning and Generalizing Variable Impedance Manipulation Skills from Human Demonstrations](https://mlsanity.com/paper/2104.01324/) (2021)
- [Minimalist Compliance Control](https://mlsanity.com/paper/2603.00913/) (2026)
- [An Efficient Closed-Form Method for Optimal Hybrid Force-Velocity Control](https://mlsanity.com/paper/2011.04872/) (2020)
- [DexTeleop-0: Force-Aware Bimanual Dexterous Teleoperation with Ego-Centric Perception towards Shared Autonomy](https://mlsanity.com/paper/2606.23431/) (2026)
- [VIDP: Variable Impedance Diffusion Policy for Compliant Robot Manipulation from Diverse Demonstrations](https://mlsanity.com/paper/2608.06210/) (2026)

## Abstract (arXiv)

Teleoperation is widely used to collect high-quality demonstrations for robot learning. Dexterous manipulation, however, requires coordinated control of motion and contact forces. Operators can specify motion, but struggle to reproduce the subtle force adjustments of human manipulation without feeling the robot's contacts. We introduce Force-Modulated Dexterity (FOMO-Dex), a unified compliance formulation for contact-rich manipulation. FOMO-Dex expresses nominal actions in coordinates defined by task geometry and contact structure. It applies force-driven motion corrections along selected directions while leaving the remaining command components unchanged. Across card picking, paper cutting, screwdriving, and nut-and-bolt assembly, FOMO-Dex reduces excessive contact forces compared with stiff control. A user study shows that operators collect more successful demonstration data per hour after brief practice. For policy learning, imitating the corrected actions improves card-picking success over imitating raw operator commands. Policies trained on FOMO-Dex demonstrations also achieve more rotation in nut-and-bolt manipulation and screwdriving than those trained on demonstrations collected with the stiff controller. Videos are shown on https://yingyuan0414.github.io/fomo-dex/ .