arXiv AI
EigenDEXplore: Structured Exploration for Dexterous Manipulation with Human Priors
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arXiv Computer Vision
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Dex-One2Many: Learning Dexterous Manipulation from a Single Human Demonstration
Dex-One2Many introduces a real-to-sim-to-real framework that learns a generalizable dexterous manipulation policy from a single human video. By abstracting the video into sequential scene graphs, the method guides reinforcement learning with dense rewards and diverse reset states, enabling efficient exploration while preserving broad generalizability. Trained entirely in simulation, Dex-One2Many achieves zero‑shot transfer to a real multi‑fingered hand and outperforms baselines by 6.5% on seen configurations and 71% on unseen scenarios across five tool‑use and manipulation tasks.
arXiv AI
Jun 26
Play2Perfect: What Matters in Dexterous Play Pretraining for Precise Assembly?
arXiv:2606. 26428v1 Announce Type: cross Abstract: Multi-fingered robots promise the speed and dexterity of human hands, yet challenging problems such as precise assembly have remained out of reach.
arXiv AI
Jun 11
Bridging the Morphology Gap: Adapting VLA Models to Dexterous Manipulation via Intent-Conditioned Fine-Tuning
arXiv:2606. 12109v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have demonstrated remarkable zero-shot generalization in robotic manipulation, yet the vast majority of pre-trained pipelines remain strictly confined to low-DoF parallel grippers.
arXiv AI
Jul 29
InDex: Empowering VLA Models with Intent-Conditioned Arm-Hand Coordination for Dexterous Manipulation
arXiv:2606. 12109v2 Announce Type: replace-cross Abstract: Pre-trained Vision-Language-Action (VLA) models provide useful semantic and spatial priors, yet their parallel-gripper action interfaces do not specify how those priors should be realized by a dexterous hand.
Hugging Face Trending Papers
Jun 22
CoorDex: Coordinating Body and Hand Priors for Continuous Dexterous Humanoid Loco-Manipulation
Humanoid loco-manipulation is often simplified into a stop-and-go process: walking to an object, stopping to manipulate it, and then resuming locomotion. It also commonly relies on low degree-of-freedom (DoF) end effectors that behave like an open-close grasp primitive.
arXiv Machine Learning
Sep 22
Connectivity-Aware Exploration of Robotic Grasp Spaces
The paper investigates the multiscale connectivity structure of successful robotic grasps in SE(3) and finds that these sets exhibit heterogeneous yet reproducible connectivity across objects. It proposes a connectivity‑aware sampling strategy that prioritizes bridges, frontiers, boundary extensions, and geometric novelty, which recovers grasp connectivity more efficiently than random or farthest‑point sampling. Experiments also show that connectivity information can improve subsequent grasp discovery and transfer to unseen objects, indicating that the spatial organization of viable actions offers valuable guidance for exploration.