# In-the-Wild Compliant Manipulation with UMI-FT

arXiv: https://arxiv.org/abs/2601.09988 · HTML: https://mlsanity.com/paper/2601.09988/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2026.

## Built on by (most cited first, 13 of 13)

- [Humanoid Manipulation Interface: Humanoid Whole-Body Manipulation from Robot-Free Demonstrations](https://mlsanity.com/paper/2602.06643/) (2026)
- [UMI-3D: Extending Universal Manipulation Interface from Vision-Limited to 3D Spatial Perception](https://mlsanity.com/paper/2604.14089/) (2026)
- [TacCoRL: Integrating Tactile Feedback into VLA via Simulation](https://mlsanity.com/paper/2606.11743/) (2026)
- [Tactile-based Multimodal Fusion in Embodied Intelligence: A Survey of Vision, Language, and Contact-Driven Paradigms](https://mlsanity.com/paper/2605.17336/) (2026)
- [CR-VLA-Force: Learning Control-aware Compliance VLA Model for Robust Contact-rich Robotic Manipulation](https://mlsanity.com/paper/2609.05832/) (2026)
- [UMI-Underwater: Learning Underwater Manipulation without Underwater Teleoperation](https://mlsanity.com/paper/2603.27012/) (2026)
- [Active Stereo-Camera Outperforms Multi-Sensor Setup in ACT Imitation Learning for Humanoid Manipulation](https://mlsanity.com/paper/2603.28422/) (2026)
- [IMPACT: Learning Internal-Model Predictive Control for Forceful Robotic Manipulation](https://mlsanity.com/paper/2606.10818/) (2026)
- [ForceTwin: Physics-informed Digital Twins for Robotic Manipulation from Instrumented Human Interaction](https://mlsanity.com/paper/2609.21751/) (2026)
- [Beyond State-as-Action: Exploiting Command-State Discrepancy for Robot Imitation Learning](https://mlsanity.com/paper/2609.33145/) (2026)
- [Robot Learning with Visual Predicted Force](https://mlsanity.com/paper/2610.04741/) (2026)
- [Chronos Policy: Learning Robot Policies on Physical Time](https://mlsanity.com/paper/2610.13929/) (2026)
- [Force-Modulated Dexterity: Unified Compliance for Contact-Rich Manipulation](https://mlsanity.com/paper/2610.17161/) (2026)

## Abstract (arXiv)

Many manipulation tasks require careful force modulation. With insufficient force the task may fail, while excessive force could cause damage. The high cost, bulky size and fragility of commercial force/torque (F/T) sensors have limited large-scale, force-aware policy learning. We introduce UMI-FT, a handheld data-collection platform that mounts compact, six-axis force/torque sensors on each finger, enabling finger-level wrench measurements alongside RGB, depth, and pose. Using the multimodal data collected from this device, we train an adaptive compliance policy that predicts position targets, grasp force, and stiffness for execution on standard compliance controllers. In evaluations on three contact-rich, force-sensitive tasks (whiteboard wiping, skewering zucchini, and lightbulb insertion), UMI-FT enables policies that reliably regulate external contact forces and internal grasp forces, outperforming baselines that lack compliance or force sensing. UMI-FT offers a scalable path to learning compliant manipulation from in-the-wild demonstrations. We open-source the hardware and software to facilitate broader adoption at:https://umi-ft.github.io/.