DexJoCo-X: Benchmarking Action Representations for Multi-Hand Dexterous Manipulation
cs.RO
Submitted: 2026-10-02
Updated: 2026-10-12
Terminology
Sources
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
- RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation
- Being-H0.5: Scaling Human-Centric Robot Learning for Cross-Embodiment Generalization
- Ego-Pi: VLA Fine-Tuning for Ego-Centric Human and Robot Data
- One-Policy-Fits-All: Geometry-Aware Action Latents for Cross-Embodiment Manipulation
- Cross-Hand Latent Representation for Vision-Language-Action Models
- Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning
- DexJoCo: A Benchmark and Toolkit for Task-Oriented Dexterous Manipulation on MuJoCo
- DexVerse: A Modular Benchmark for Multi-Task, Multi-Embodiment Dexterous Manipulation
- robosuite: A Modular Simulation Framework and Benchmark for Robot Learning
- Robotic Telekinesis: Learning a Robotic Hand Imitator by Watching Humans on Youtube
- R3DP: Real-Time 3D-Aware Policy for Embodied Manipulation
- CEDex: Cross-Embodiment Dexterous Grasp Generation at Scale from Human-like Contact Representations
- AnyDexGrasp: General Dexterous Grasping for Different Hands with Human-level Learning Efficiency
- MachaGrasp: Morphology-Aware Cross-Embodiment Dexterous Hand Articulation Generation for Grasping
- DexGrasp-Zero: A Morphology-Aligned Policy for Zero-Shot Cross-Embodiment Dexterous Grasping
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