Repository
Benchmarks / Research
VTDexManip · LQTS
This is a official code for the benchmark of the paper "VTDexManip: A Dataset and Benchmark for Visual-tactile Pretraining and Dexterous Manipulation with Reinforcement Learning" (ICLR 2025)
Overview
This is a official code for the benchmark of the paper "VTDexManip: A Dataset and Benchmark for Visual-tactile Pretraining and Dexterous Manipulation with Reinforcement Learning" (ICLR 2025)
Publisher purpose: This is a official code for the benchmark of the paper "VTDexManip: A Dataset and Benchmark for Visual-tactile Pretraining and Dexterous Manipulation with Reinforcement Learning" (ICLR 2025)
Compare tasks, protocols, dataset/environment versions and hardware before interpreting reported results. A benchmark repository is not a certification of a robot system.
Project information
- Maintainer
- LQTS
- Primary language
- Python
- Repository code license
- MIT
- Model-weight license
- Not recorded
- Dataset license
- Not recorded
- Hardware design license
- Not recorded
- Maintenance
- Not archived in the last recorded metadata
- Last observed repository push
- 2025-07-07
Code licenses do not automatically cover model weights, datasets or hardware designs. Unrecorded license details require confirmation with the publisher.
Documentation sections
- VTDexManip: A Dataset and Benchmark for Visual-tactile Pretraining and Dexterous Manipulation with Reinforcement Learning
- **Dependencies**
- **VTDexManip** Benchmark
- Model list
- Downstream tasks
- Manipulation policy
Browse this resource’s categories
Explore related collections
Browse Benchmarks →More in Benchmarks
Repository
Repository
Repository
Repository
Repository
Research
actionshift · Archerkattri
ActionShift evaluates adaptation to hidden robot action-interface contracts on ManiSkill tasks.
Manipulation
Repository
Research
RMBench · RoboTwin-Platform
Memory-Dependent Manipulation Benchmark based on RoboTwin
Manipulation