arXiv (All)
open access
Tactile Sensing-based Secure and Gentle Grasping of Unknown Objects
Reliable robotic manipulation requires maintaining a firm grasp while strictly avoiding both contact slip and structural damage to the object. Satisfying these inherent \textit{secure-and-gentle} grasping constraints is challenging for unknown objects. We present a novel online data-driven predictiv
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Learning From a Steady Hand: A Weakly Supervised Agent for Robot Assistance under Microscopy#1354844
- Vision-Based Tactile Sensing for the Perception of the Object's Compliance and Hardness#925824
- Tracing Energy Flow: Learning Tactile-based Grasping Force Control to Reduce Slippage in Dynamic Object Interaction#987536
- ContactWorld: What Representations Matter in Vision-Tactile World Models for Contact-Rich Manipulation#985130
- PhysCoRe: Physics-Corrected Residual World Models for Material-Aware Deformable Dynamics#1013629
Record · ID 1296797
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive