DexRoam: Learning Mobile Bimanual Dexterous Manipulation from Egocentric Whole-Body Human Demonstrations

arXiv:2609.35761 · cs.RO · Submitted 2026-09-28 · Read on arXiv

cs.RO

Submitted: 2026-09-28

Updated: 2026-09-28

Code: https://github.com/vuer-ai/vuer

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

Terminology

• Alignment — 51× in this paper · explained in 1 episode
• Action Space — 18× in this paper · explained in 1 episode
• Task Completion Score — 9× in this paper
• Ablation — 5× in this paper · explained in 1 episode
• Affordance — 4× in this paper · explained in 1 episode
• Distribution Shift — 4× in this paper · explained in 5 episodes
• Generalization — 4× in this paper · explained in 13 episodes
• Sliced Wasserstein Distance — 4× in this paper
• Wasserstein Distance — 4× in this paper · explained in 4 episodes
• Foundation Model — 3× in this paper · explained in 3 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Semantic Alignment — 3× in this paper · explained in 3 episodes
• World Model — 3× in this paper · explained in 7 episodes
• Active Vision — 2× in this paper · explained in 1 episode
• Fidelity — 2× in this paper · explained in 2 episodes
• Maximum Mean Discrepancy — 2× in this paper
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• 3D Gaussian Splatting — 1× in this paper · explained in 2 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Action Decoder — 1× in this paper · explained in 1 episode
• Active Perception — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Cross-Embodiment Transfer — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Gaussian Splatting — 1× in this paper · explained in 4 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Transferability — 1× in this paper · explained in 4 episodes

Sources

• The Design of Stretch: A Compact, Lightweight Mobile Manipulator for Indoor Human Environments
• Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation
• HOMIE: Humanoid Loco-Manipulation with Isomorphic Exoskeleton Cockpit
• HumanoidExo: Scalable Whole-Body Humanoid Manipulation via Wearable Exoskeleton
• NuExo: A Wearable Exoskeleton Covering all Upper Limb ROM for Outdoor Data Collection and Teleoperation of Humanoid Robots
• Ego4D: Around the World in 3,000 Hours of Egocentric Video
• The EPIC-KITCHENS Dataset: Collection, Challenges and Baselines
• HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object Interaction
• Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots
• ARCap: Collecting High-quality Human Demonstrations for Robot Learning with Augmented Reality Feedback
• Open-TeleVision: Teleoperation with Immersive Active Visual Feedback
• MotionTrans: Human VR Data Enable Motion-Level Learning for Robotic Manipulation Policies
• EgoScale: Scaling Dexterous Manipulation with Diverse Egocentric Human Data
• DexWild: Dexterous Human Interactions for In-the-Wild Robot Policies
• H-RDT: Human Manipulation Enhanced Bimanual Robotic Manipulation
• EgoVLA: Learning Vision-Language-Action Models from Egocentric Human Videos
• EgoHumanoid: Unlocking In-the-Wild Loco-Manipulation with Robot-Free Egocentric Demonstration
• EMMA: Scaling Mobile Manipulation via Egocentric Human Data
• HoMMI: Learning Whole-Body Mobile Manipulation from Human Demonstrations
• HALOMI: Learning Humanoid Loco-Manipulation with Active Perception from Human Demonstrations

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