arXiv — cs.AI preprintsInternational19 October 2026
HydroShear: Hydroelastic Shear Simulation for Tactile Sim-to-Real Reinforcement Learning
This is an official announcement record
Firsthand records what arXiv — cs.AI preprints announced and links to the original. The wording below is theirs, not ours.
arXiv:2603.00446v2 Announce Type: replace-cross Abstract: In this paper, we address the problem of tactile sim-to-real policy transfer for contact-rich tasks. Existing methods primarily focus on vision-based sensors and emphasize image rendering quality while providing overly simplistic models of force and shear. Consequently, these models exhibit a large sim-to-real gap for many dexterous tasks. Here, we present HydroShear, a non-holonomic hydroelastic tactile simulator that advances the state-of-the-art by modeling: a) stick-slip transitions, b) path-dependent force and shear build up, and c
Read the official announcement
Opens arxiv.org
More from arXiv — cs.AI preprints
- Structured Authority Injection: Persistent Human Control for LLM Agents via Typed MCP Overrides19 October 2026
- Rethinking User Encoders: Routing Diffusion States for Scenario-Adaptive User Representation19 October 2026
- From Events to Reasoning: Hierarchical Rule Composition over Explicit States19 October 2026
- ECHOES: Seeing Beyond the Current Image via Recursive History Compression and Privileged On-Policy Distillation19 October 2026
- Sensitivity Analysis of Decision Rules in an Agent-Based Agro-Ecological Model19 October 2026
This content is for informational purposes only and is not professional advice. Specifications, prices, plan tiers, and features change frequently and may differ from what is shown here; verify current details on the manufacturer's or company's official page before purchasing. Ratings are based on analysis of published documentation, not independent lab testing.