# Leveraging Contact Forces for Learning to Grasp

arXiv: https://arxiv.org/abs/1809.07004 · HTML: https://mlsanity.com/paper/1809.07004/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2018.

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

- [DexVIP: Learning Dexterous Grasping with Human Hand Pose Priors from Video](https://mlsanity.com/paper/2202.00164/) (2022)
- [Improved Learning of Robot Manipulation Tasks via Tactile Intrinsic Motivation](https://mlsanity.com/paper/2102.11051/) (2021)
- [Learning Dexterous Manipulation Using Contact Wrench Guidance From Human Demonstration](https://mlsanity.com/paper/2607.00033/) (2026)
- [Simulation-based Bayesian inference for multi-fingered robotic grasping](https://mlsanity.com/paper/2109.14275/) (2021)
- [Enhancing Tactile-based Reinforcement Learning for Robotic Control](https://mlsanity.com/paper/2510.21609/) (2025)
- [Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation](https://mlsanity.com/paper/2602.08557/) (2026)
- [DASH: Modularized Human Manipulation Simulation with Vision and Language for Embodied AI](https://mlsanity.com/paper/2108.12536/) (2021)
- [The Role of Tactile Sensing in Learning and Deploying Grasp Refinement Algorithms](https://mlsanity.com/paper/2109.11234/) (2021)
- [Planning Visual-Tactile Precision Grasps via Complementary Use of Vision and Touch](https://mlsanity.com/paper/2212.08604/) (2022)
- [Contact Energy Based Hindsight Experience Prioritization](https://mlsanity.com/paper/2312.02677/) (2023)
- [Towards Transferring Tactile-based Continuous Force Control Policies from Simulation to Robot](https://mlsanity.com/paper/2311.07245/) (2023)
- [Modular Neural Network Policies for Learning In-Flight Object Catching with a Robot Hand-Arm System](https://mlsanity.com/paper/2312.13987/) (2023)
- [A model-free approach to fingertip slip and disturbance detection for grasp stability inference](https://mlsanity.com/paper/2311.13245/) (2023)

## Abstract (arXiv)

Grasping objects under uncertainty remains an open problem in robotics research. This uncertainty is often due to noisy or partial observations of the object pose or shape. To enable a robot to react appropriately to unforeseen effects, it is crucial that it continuously takes sensor feedback into account. While visual feedback is important for inferring a grasp pose and reaching for an object, contact feedback offers valuable information during manipulation and grasp acquisition. In this paper, we use model-free deep reinforcement learning to synthesize control policies that exploit contact sensing to generate robust grasping under uncertainty. We demonstrate our approach on a multi-fingered hand that exhibits more complex finger coordination than the commonly used two-fingered grippers. We conduct extensive experiments in order to assess the performance of the learned policies, with and without contact sensing. While it is possible to learn grasping policies without contact sensing, our results suggest that contact feedback allows for a significant improvement of grasping robustness under object pose uncertainty and for objects with a complex shape.