# Learning compliant grasping and manipulation by teleoperation with adaptive force control

arXiv: https://arxiv.org/abs/2107.08996 · HTML: https://mlsanity.com/paper/2107.08996/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2021.

## Built on by (most cited first, 5 of 5)

- [REBOOT: Reuse Data for Bootstrapping Efficient Real-World Dexterous Manipulation](https://mlsanity.com/paper/2309.03322/) (2023)
- [MOMA-Force: Visual-Force Imitation for Real-World Mobile Manipulation](https://mlsanity.com/paper/2308.03624/) (2023)
- [QDTraj: Exploration of Diverse Trajectory Primitives for Articulated Objects Robotic Manipulation](https://mlsanity.com/paper/2604.22551/) (2026)
- [A model-free approach to fingertip slip and disturbance detection for grasp stability inference](https://mlsanity.com/paper/2311.13245/) (2023)
- [Force-Modulated Dexterity: Unified Compliance for Contact-Rich Manipulation](https://mlsanity.com/paper/2610.17161/) (2026)

## Abstract (arXiv)

In this work, we focus on improving the robot's dexterous capability by exploiting visual sensing and adaptive force control. TeachNet, a vision-based teleoperation learning framework, is exploited to map human hand postures to a multi-fingered robot hand. We augment TeachNet, which is originally based on an imprecise kinematic mapping and position-only servoing, with a biomimetic learning-based compliance control algorithm for dexterous manipulation tasks. This compliance controller takes the mapped robotic joint angles from TeachNet as the desired goal, computes the desired joint torques. It is derived from a computational model of the biomimetic control strategy in human motor learning, which allows adapting the control variables (impedance and feedforward force) online during the execution of the reference joint angle trajectories. The simultaneous adaptation of the impedance and feedforward profiles enables the robot to interact with the environment in a compliant manner. Our approach has been verified in multiple tasks in physics simulation, i.e., grasping, opening-a-door, turning-a-cap, and touching-a-mouse, and has shown more reliable performances than the existing position control and the fixed-gain-based force control approaches.