Ego-Pi: VLA Fine-Tuning for Ego-Centric Human and Robot Data

• URL: http://arxiv.org/abs/2606.08107v1
• Date: Sat, 06 Jun 2026 11:14:21 GMT
• Title: Ego-Pi: VLA Fine-Tuning for Ego-Centric Human and Robot Data
• Abstract summary: We investigate key design choices for learning across human and humanoid embodiments equipped with dexterous five-finger hands.<n>Our results show that human data enables robots to learn new task semantics and compose existing skills into novel behaviors without corresponding robot data.
• Score: 47.128323261147045
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Robotics faces a fundamental challenge of data scarcity. Unlike language or vision research, there is no internet-scale dataset for robotic manipulation. A promising path forward is to leverage egocentric human data, which can be collected more easily, with greater breadth, and at a larger scale. Towards this end, we investigate key design choices for learning across human and humanoid embodiments equipped with dexterous five-finger hands, using the $π_{0.5}$ model as a foundation. Our results show that human data enables robots to learn new task semantics and compose existing skills into novel behaviors without corresponding robot data. The paper website is here: https://egopipaper.github.io/

Related papers

• Ego2Robot: Scalable Robot Data Synthesis from Egocentric Human Data [60.94418315274988]
  Ego2Robot supports both curated and in-the-wild videos, producing 18,561 hours of robot training data spanning 15 robot morphologies.<n>Joint pretraining on Ego2Robot-synthesized and robot data consistently improves out-of-distribution generalization across multiple types.
  arXiv  Detail & Related papers  (2026-08-03T17:52:26Z)
• HumanEgo: Zero-Shot Robot Learning from Minutes of Human Egocentric Videos [58.9564236347451]
  HumanEgo is a framework that bridges the embodiment gap between human and robot.<n>It lifts each human demonstration to an entity-level representation of hand-object interaction.<n>HumanEgo is robot-data-free, hardware-agnostic, data-efficient, and zero-shot human-to-robot transferable.
  arXiv  Detail & Related papers  (2026-05-24T08:26:41Z)
• EgoVerse: An Egocentric Human Dataset for Robot Learning from Around the World [63.2070298441355]
  EgoVerse is a collaborative platform for human data-driven robot learning.<n>We release 1,362 hours (80k episodes) of human demonstrations spanning 1,965 tasks, 240 scenes, and 2,087 unique demonstrators.<n>We conduct a large-scale study of human-to-robot transfer with experiments replicated across multiple labs, tasks, and robot embodiments.
  arXiv  Detail & Related papers  (2026-04-08T21:27:06Z)
• EgoVLA: Learning Vision-Language-Action Models from Egocentric Human Videos [49.820119587446655]
  In this paper, we explore training Vision-Language-Action (VLA) models using egocentric human videos.<n>With a VLA trained on human video that predicts human wrist and hand actions, we can perform Inverse Kinematics and convert the human actions to robot actions.<n>We propose a simulation benchmark called Ego Humanoid Manipulation Benchmark, where we design diverse bimanual manipulation tasks with demonstrations.
  arXiv  Detail & Related papers  (2025-07-16T17:27:44Z)
• EgoZero: Robot Learning from Smart Glasses [54.6168258133554]
  EgoZero learns robust manipulation policies from human demonstrations captured with Project Aria smart glasses.<n>We deploy EgoZero policies on a Franka Panda robot and demonstrate zero-shot transfer with 70% success rate over 7 manipulation tasks.<n>Our results suggest that in-the-wild human data can serve as a scalable foundation for real-world robot learning.
  arXiv  Detail & Related papers  (2025-05-26T17:59:17Z)
• Humanoid Policy ~ Human Policy [41.34186233320398]
  We train a human-humanoid behavior policy, which we term Human Action Transformer (HAT)<n>The state-action space of HAT is unified for both humans and humanoid robots and can be differentiably retargeted to robot actions.<n>We show that human data improves both generalization and robustness of HAT with significantly better data collection efficiency.
  arXiv  Detail & Related papers  (2025-03-17T17:59:09Z)
• InteRACT: Transformer Models for Human Intent Prediction Conditioned on Robot Actions [7.574421886354134]
  InteRACT architecture pre-trains a conditional intent prediction model on large human-human datasets and fine-tunes on a small human-robot dataset. We evaluate on a set of real-world collaborative human-robot manipulation tasks and show that our conditional model improves over various marginal baselines.
  arXiv  Detail & Related papers  (2023-11-21T19:15:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.