Controlling fine-grained forces during manipulation remains a core challenge in robotics. While robot policies learned from robot-collected data or simulation show promise, they struggle to generalize across the diverse range of real-world interactions. Learning directly from humans offers a scalable solution, enabling demonstrators to perform skills in their natural embodiment and in everyday environments. However, visual demonstrations alone lack the information needed to infer precise contact forces. We present FeelTheForce (FTF): a robot learning system that models human tactile behavior to learn force-sensitive manipulation. Using a tactile glove to measure contact forces and a vision-based model to estimate hand pose, we train a closed-loop policy that continuously predicts the forces needed for manipulation. This policy is re-targeted to a Franka Panda robot with tactile gripper sensors using shared visual and action representations. At execution, a PD controller modulates gripper closure to track predicted forces-enabling precise, force-aware control. Our approach grounds robust low-level force control in scalable human supervision, achieving a 77% success rate across 5 force-sensitive manipulation tasks. Code and videos are available atthis https URL.
View on arXiv@article{adeniji2025_2506.01944,
title={ Feel the Force: Contact-Driven Learning from Humans },
author={ Ademi Adeniji and Zhuoran Chen and Vincent Liu and Venkatesh Pattabiraman and Raunaq Bhirangi and Siddhant Haldar and Pieter Abbeel and Lerrel Pinto },
journal={arXiv preprint arXiv:2506.01944},
year={ 2025 }
}