OpenViTac: Learning and Benchmarking Visuo-Tactile Policies in a Unified Sim-and-Real Framework
To address this gap, we introduce OpenViTac, a visuo-tactile manipulation benchmark for evaluating robot policies across simulation and the real world.
ProofPaper ↗
Key points
- Tactile feedback provides embodied agents with physical information beyond visual observations, enabling more reliable interaction with the real world.
- However, despite the rapid progress of vision-tactile-language-action (VTLA) policies, there remains a lack of unified benchmarks for evaluating tactile-enabled robot manipulation across simulation and the real world.
- OpenViTac organizes contact-rich manipulation into four tactile-relevant capability dimensions and provides paired simulation-real-world settings for consistent evaluation of VLA, WAM, and VTLA policies.
- Correspondingly, we introduce OpenVTLA, a tactile augmentation framework that combines the best-performing representation and integration strategy.
Sources (1)
- [1]OpenViTac: Learning and Benchmarking Visuo-Tactile Policies in a Unified Sim-and-Real FrameworkarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 04:43 PM
To address this gap, we introduce OpenViTac, a visuo-tactile manipulation benchmark for evaluating robot policies across simulation and the real world.
Tactile feedback provides embodied agents with physical information beyond visual observations, enabling more reliable interaction with the real world.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 7, 2026Q-Learning with Scalar Adjoint Matching
- Oct 7, 2026NavGPT-3: Harnessing Context in a Hierarchical Navigation Runtime
- Oct 6, 2026CARE: Certifying Acceleration for Vision-Language-Action Inference
- Oct 6, 2026PAIR: Bridging Perception and Action in Vision-Language-Action Models
- Oct 5, 2026SimForcing: Distilling Simulation Motion Priors into Real-Domain Robot World Models
- Oct 2, 2026FastOPD: On-Policy Distillation for Lightweight VLA Deployment