# Learning Gentle Grasping from Human-Free Force Control Demonstration

arXiv: https://arxiv.org/abs/2409.10371 · HTML: https://mlsanity.com/paper/2409.10371/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 35 references that are in the map.
- First posted on arXiv 16 Sep 2024.

## Builds on (most cited first, 9 of 9)

- [Maintaining Grasps within Slipping Bound by Monitoring Incipient Slip](https://mlsanity.com/paper/1810.13381/) (2018)
- [Soft and Rigid Object Grasping With Cross-Structure Hand Using Bilateral Control-Based Imitation Learning](https://mlsanity.com/paper/2311.09555/) (2023)
- [Zero-shot sim-to-real transfer of tactile control policies for aggressive swing-up manipulation](https://mlsanity.com/paper/2101.02680/) (2021)
- [Learning Generalizable Vision-Tactile Robotic Grasping Strategy for Deformable Objects via Transformer](https://mlsanity.com/paper/2112.06374/) (2021)
- [Tac3D: A Novel Vision-based Tactile Sensor for Measuring Forces Distribution and Estimating Friction Coefficient Distribution](https://mlsanity.com/paper/2202.06211/) (2022)
- [GelLink: A Compact Multi-phalanx Finger with Vision-based Tactile Sensing and Proprioception](https://mlsanity.com/paper/2403.14887/) (2024)
- [EasyCalib: Simple and Low-Cost In-Situ Calibration for Force Reconstruction with Vision-Based Tactile Sensors](https://mlsanity.com/paper/2403.10256/) (2024)
- [Leveraging distributed contact force measurements for slip detection: a physics-based approach enabled by a data-driven tactile sensor](https://mlsanity.com/paper/2109.11504/) (2021)
- [Incipient Slip-Based Rotation Measurement via Visuotactile Sensing During In-Hand Object Pivoting](https://mlsanity.com/paper/2309.05366/) (2023)

## Built on by (most cited first, 1 of 1)

- [Pelican-VL 1.0: A Foundation Brain Model for Embodied Intelligence](https://mlsanity.com/paper/2511.00108/) (2025)

## Abstract (arXiv)

Humans can steadily and gently grasp unfamiliar objects based on tactile perception. Robots still face challenges in achieving similar performance due to the difficulty of learning accurate grasp-force predictions and force control strategies that can be generalized from limited data. In this article, we propose an approach for learning grasping from ideal force control demonstrations, to achieve similar performance of human hands with limited data size. Our approach utilizes objects with known contact characteristics to automatically generate reference force curves without human demonstrations. In addition, we design the dual convolutional neural networks (Dual-CNN) architecture which incorporats a physics-based mechanics module for learning target grasping force predictions from demonstrations. The described method can be effectively applied in vision-based tactile sensors and enables gentle and stable grasping of objects from the ground. The described prediction model and grasping strategy were validated in offline evaluations and online experiments, and the accuracy and generalizability were demonstrated.