How Firm Should a Grasp Be?
cs.RO, cs.CV
Submitted: 2026-10-08
Updated: 2026-10-08
Comments: CoRL 2026
License: http://creativecommons.org/licenses/by/4.0/
The gist: An ideal robot grasp is firm enough to securely handle an object, yet gentle enough to avoid damaging it.
Terminology
Abstract
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.
Sources
- DefGraspNets: Grasp Planning on 3D Fields with Graph Neural Nets
- 3D-ViTac: Learning Fine-Grained Manipulation with Visuo-Tactile Sensing
- Touch in the Wild: Learning Fine-Grained Manipulation with a Portable Visuo-Tactile Gripper
- Towards synthesizing grasps for 3D deformable objects with physics-based simulation
- TactAlign: Human-to-Robot Policy Transfer via Tactile Alignment
- Tactile-Conditioned Diffusion Policy for Force-Aware Robotic Manipulation
- Benchmarking in Manipulation Research: The YCB Object and Model Set and Benchmarking Protocols
- ShapeNet: An Information-Rich 3D Model Repository
- SOPHY: Learning to Generate Simulation-Ready Objects with Physical Materials
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