# How Firm Should a Grasp Be?

arXiv: https://arxiv.org/abs/2610.11221 · HTML: https://mlsanity.com/paper/2610.11221/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 87 references that are in the map.
- First posted on arXiv 8 Oct 2026.

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

- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [Benchmarking in Manipulation Research: The YCB Object and Model Set and Benchmarking Protocols](https://mlsanity.com/paper/1502.03143/) (2015)
- [3D-ViTac: Learning Fine-Grained Manipulation with Visuo-Tactile Sensing](https://mlsanity.com/paper/2410.24091/) (2024)
- [Touch in the Wild: Learning Fine-Grained Manipulation with a Portable Visuo-Tactile Gripper](https://mlsanity.com/paper/2507.15062/) (2025)
- [DefGraspNets: Grasp Planning on 3D Fields with Graph Neural Nets](https://mlsanity.com/paper/2303.16138/) (2023)
- [Tactile-Conditioned Diffusion Policy for Force-Aware Robotic Manipulation](https://mlsanity.com/paper/2510.13324/) (2025)
- [TactAlign: Human-to-Robot Policy Transfer via Tactile Alignment](https://mlsanity.com/paper/2602.13579/) (2026)
- [SOPHY: Learning to Generate Simulation-Ready Objects with Physical Materials](https://mlsanity.com/paper/2504.12684/) (2025)
- [Towards synthesizing grasps for 3D deformable objects with physics-based simulation](https://mlsanity.com/paper/2107.08898/) (2021)

## Abstract (arXiv)

An ideal robot grasp is firm enough to securely handle an object, yet gentle enough to avoid damaging it. Achieving this balance requires knowledge of the object's material properties, such as its mass, elasticity, and surface friction. These properties, however, are seldom precisely known a priori. In this work, we propose a visuotactile approach to estimating material properties in real time, during the process of grasping. Our method uses these estimated properties to determine the minimum grasp force required to handle the object. We contribute a new dataset of real-world objects (fruits and vegetables) with measured physical properties (shape, mass, elasticity, and friction), which we use to construct our force estimation model via simulations. We experimentally validate our approach to grasp force control using a robot with a parallel-jaw gripper. We demonstrate our system's ability to gently grasp a wide variety of objects, in each case adapting to their unique physical properties.