| CARVIEW |
Extracting Force-informed Actions from Kinesthetic Demonstrations
for Dexterous Manipulation
Claire Chen, Zhongchun Yu, Hojung Choi, Mark Cutkosky, Jeannette Bohg
Stanford University
RA-L June 2025
DexForce leverages contact forces, measured during kinesthetic demonstrations, to compute force-informed actions.
We use DexForce demonstrations to learn policies for6 tasksand evaluate each rollout according to the following criteria:
slide cube
success
failure
reorient smiley
success
partial success
failure
open AirPods
success
partial success
failure
grasp battery
success
partial success
failure
unscrew nut
success
partial success
failure
flip box
success
partial success
failure
We show that DexForce's force-informed actions are critical for learning capable policies.
Policies trained on our force-informed actions achieve an average76%success rate across all 6 tasks.
Baseline policies trained on actions thatdo notaccount for contact forces havenear zerosuccess rates.
To view policy rollouts trainedwithandwithoutforce-informed actions for each task, view on larger screen.
Explore policies trainedwithandwithoutforce-informed actions for each task:
with force-informed actions
without force-informed actions
with force-informed actions
without force-informed actions
with force-informed actions
without force-informed actions
with force-informed actions
without force-informed actions
with force-informed actions
without force-informed actions
with force-informed actions
without force-informed actions
Hardware
We instrument an Allegro hand with a wrist-mounted Intel RealSense D435 camera (+ fisheye lens attachment) and two CoinFT six-axis force-torque sensors.
To re-create our setup:
Fisheye RealSense: We have open-sourced our camera and fisheye lens mounting parts.
Get the instructions and partshereand
pleasecite usif you find these useful!
CoinFT: The developers of CoinFT plan to release the sensor design soon!
Supplementary video
BibTeX
@ARTICLE{chen2025dexforce,
author={Chen, Claire and Yu, Zhongchun and Choi, Hojung and Cutkosky, Mark and Bohg, Jeannette},
journal={IEEE Robotics and Automation Letters},
title={DexForce: Extracting Force-Informed Actions From Kinesthetic Demonstrations for Dexterous Manipulation},
year={2025},
volume={10},
number={6},
pages={6416-6423},
keywords={Robots;Force;Robot kinematics;Robot sensing systems;Hands;Force measurement;Sensors;Impedance;Imitation learning;Position measurement;Dexterous manipulation;force and tactile sensing;imitation learning},
doi={10.1109/LRA.2025.3568318}
}