Hugging Face Trending Papers
EgoLAP: Learning from Egocentric Human Data through Language-Action Reasoning
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arXiv AI
Oct 7
EgoLAP: Learning from Egocentric Human Data through Language-Action Reasoning
EgoLAP is a VLA pre‑training framework that learns from both human and robot trajectories by expressing motion intent as temporally abstracted language actions linked to motion‑level reasoning grounded in scene geometry, physics, and object affordances. It jointly trains on a shared language‑based action chain‑of‑thought, enabling the transfer of human experience to robot control. In extensive real‑world and simulated experiments, EgoLAP achieves 80.1% mean real‑world task progress, a 2.3× improvement over alternative action representations, and outperforms composite reasoning formats.
arXiv AI
Sep 21
AtomEgo: Exploring Ego-Robot Integration for Embodied Foundation Model Pretraining
AtomEgo investigates how to integrate large-scale egocentric human interaction data into embodied foundation model pre‑training. The study uses a curated 2,659‑hour corpus and a scalable data pipeline to evaluate three co‑training paradigms across vision‑language‑action and world‑action architectures. Results show that the benefit of egocentric data depends on both its scale and the quality of alignment with robotic embodiment, offering practical guidance for scalable ego‑robot pre‑training.
arXiv AI
Jun 2
Qwen-VLA: Unifying Vision-Language-Action Modeling across Tasks, Environments, and Robot Embodiments
arXiv:2605. 30280v2 Announce Type: replace-cross Abstract: Embodied intelligence is often studied through specialized models for individual tasks such as manipulation or navigation, resulting in fragmented capabilities and limited generalization across tasks, environments, and robot embodiments.
arXiv Computer Vision
Oct 1
Ego4WAM: What Matters When Scaling Egocentric Human Data for Robot Learning?
Ego4WAM investigates how various properties of egocentric human data—such as human‑robot alignment, data duration, task diversity, and supervision type—affect robot learning. The study, conducted under a unified world‑action model framework, shows that aligned demonstrations improve out‑of‑distribution generalization and lower the amount of target‑task robot data needed. It also finds that video‑only supervision remains effective, and that data duration and task diversity influence downstream capabilities in distinct ways, as validated on real robots and RoboDojo.
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 2
Semantic-Geometric Task Representations for Bimanual Manipulation from Human Demonstrations to Robot Action Planning
arXiv:2601. 11460v2 Announce Type: replace-cross Abstract: Learning structured task representations from human demonstrations is essential for bimanual manipulation, where action ordering, object involvement, and interaction geometry vary significantly across executions.