arXiv AI
Jul 31
From Passive Video to Editable Experience: Physically Grounded Experience Synthesis for Embodied Intelligence
arXiv:2607. 26903v1 Announce Type: new Abstract: The key bottleneck in embodied AI is not model architecture but data.
Human2Any: Human-to-Robot Transfer via Constraint-Aware Compositional Planning
arXiv:2606. 28813v1 Announce Type: cross Abstract: Human videos are a scalable source of supervision for robot manipulation, as they are abundant and naturally capture rich object interactions.
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Hugging Face Trending Papers
Jul 29
ContactFlow: A video action conditioning that transfers across embodiments
World models offer a promising route toward robot planning by enabling agents to imagine and verify the consequences of actions before execution. However, current video-based world models often struggle to capture the physical constraints that govern manipulation, particularly contact.
arXiv AI
Jun 2
From Human Videos to Robot Manipulation: A Survey on Scalable Vision-Language-Action Learning with Human-Centric Data
arXiv:2606. 00054v1 Announce Type: cross Abstract: Recent progress in generalizable embodied control has been driven by large-scale pretraining of Vision-Language-Action (VLA) models.
arXiv Machine Learning
Jun 10
Dexterous Point Policy: Learning Point-based Dexterous Hand Policies from Human Demonstrations
arXiv:2606. 10614v1 Announce Type: cross Abstract: Robotic foundation models pre-trained on human demonstration videos have shown promise, but a significant embodiment gap remains when the resulting policies are deployed on real robots.
arXiv AI
Jun 11
LUCID: Learning Embodiment-Agnostic Intent Models from Unstructured Human Videos for Scalable Dexterous Robot Skill Acquisition
arXiv:2606. 11628v1 Announce Type: cross Abstract: The most widely-adopted robot learning pipelines today learn skills from robot demonstrations or structured human data, which are expensive to collect and tied to specific embodiments.
arXiv AI
Jun 2
AffordGen: Generating Diverse Demonstrations for Generalizable Object Manipulation with Afford Correspondence
arXiv:2604. 10579v2 Announce Type: replace-cross Abstract: Despite the recent success of modern imitation learning methods in robot manipulation, their performance is often constrained by geometric variations due to limited data diversity.
arXiv AI
2d ago
AdvDex: Learning Dexterous Manipulation from Human Demonstrations via Joint-Aligned Actions and Adversarial Learning
arXiv:2608. 14028v1 Announce Type: cross Abstract: Dexterous manipulation is a fundamental capability for embodied intelligence, but scaling it remains difficult because robot demonstrations are expensive to collect and action spaces vary across embodiments.
arXiv Machine Learning
Jun 16
DemoDiffusion: One-Shot Human Imitation using pre-trained Diffusion Policy
arXiv:2506. 20668v3 Announce Type: replace-cross Abstract: We propose DemoDiffusion, a simple method for enabling robots to perform manipulation tasks by imitating a single human demonstration, without requiring task-specific training or paired human-robot data.
Hugging Face Trending Papers
Jun 1
RoboDream: Compositional World Models for Scalable Robot Data Synthesis
Scaling robot learning requires large-scale, diverse demonstrations, yet real-world data collection via teleoperation remains prohibitively expensive and time-consuming. While video diffusion models offer a promising avenue for data scaling, existing generative approaches are often limited to superficial visual augmentation, or suffer from embodiment hallucinations that yield physically infeasible motions.
arXiv Machine Learning
Jun 16
Imitating What Works: Simulation-Filtered Modular Policy Learning from Human Videos
arXiv:2602. 13197v2 Announce Type: replace-cross Abstract: The ability to learn manipulation skills by watching videos of humans has the potential to unlock a new source of highly scalable data for robot learning.
arXiv AI
Jun 30
VLK: Learning Humanoid Loco-Manipulation from Synthetic Interactions in Reconstructed Scenes
arXiv:2606. 30645v1 Announce Type: cross Abstract: Perception-based humanoid loco-manipulation requires connecting egocentric observations and task instructions to whole-body motion.
arXiv AI
Jul 2
Learning Dexterous Manipulation Using Contact Wrench Guidance From Human Demonstration
arXiv:2607. 00033v1 Announce Type: cross Abstract: Dexterous robot manipulation can benefit from the abundance of human demonstrations, but transferring such demonstrations to robot policies remains challenging.