DexJoCo: A Benchmark and Toolkit for Task-Oriented Dexterous Manipulation on MuJoCo

arXiv:2605.16257 · cs.RO · Submitted 2026-05-15 · Read on arXiv

cs.RO

Submitted: 2026-05-15

Updated: 2026-09-22

Code: https://github.com/oogle-deepmind/mujoco_menagerie

Project page: https://dexjoco.github.io

Terminology

• Benchmark — 50× in this paper · explained in 2 episodes
• Generalization — 5× in this paper · explained in 13 episodes
• Robustness — 5× in this paper · explained in 5 episodes
• Reinforcement Learning — 4× in this paper · explained in 4 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Foundation Model — 3× in this paper · explained in 3 episodes
• Action Space — 2× in this paper · explained in 1 episode
• Benchmarking — 2× in this paper · explained in 3 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Standardized Benchmarks — 2× in this paper · explained in 2 episodes
• VLA Models — 2× in this paper · explained in 3 episodes
• Action Bias — 1× in this paper · explained in 1 episode
• Action Chunking — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Computer Vision and Pattern Recognition Conference — 1× in this paper
• Core — 1× in this paper · explained in 2 episodes
• Embodied AI — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• IEEE International Conference on Robotics and Automation — 1× in this paper
• In Conference on Robot Learning — 1× in this paper
• Sim-to-Real Gap — 1× in this paper · explained in 6 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes
• Zero-Shot Transfer — 1× in this paper · explained in 4 episodes

Sources

• EgoScale: Scaling Dexterous Manipulation with Diverse Egocentric Human Data
• What Matters in Learning from Offline Human Demonstrations for Robot Manipulation
• FACTR: Force-Attending Curriculum Training for Contact-Rich Policy Learning
• UniDex: A Robot Foundation Suite for Universal Dexterous Hand Control from Egocentric Human Videos
• DOGlove: Dexterous Manipulation with a Low-Cost Open-Source Haptic Force Feedback Glove
• DexUMI: Using Human Hand as the Universal Manipulation Interface for Dexterous Manipulation
• GR-Dexter Technical Report
• DexCap: Scalable and Portable Mocap Data Collection System for Dexterous Manipulation
• Glovity: Learning Dexterous Contact-Rich Manipulation via Spatial Wrench Feedback Teleoperation System
• DEXOP: A Device for Robotic Transfer of Dexterous Human Manipulation
• Learning Dexterous Manipulation with Quantized Hand State
• robosuite: A Modular Simulation Framework and Benchmark for Robot Learning
• Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware
• SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics

Related papers

• FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
• MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
• RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
• HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
• APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
• Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving