Ego Guidance: Enhancing Robot Policy Learning with Action-Free Human Egocentric Videos
Ego Guidance: Enhancing Robot Policy Learning with Action-Free Human Egocentric Videos
This paper investigates the use of human egocentric videos without action supervision to improve robot policy learning. The authors propose Shared Prediction on Action and Representation (SPAR), an architecture that uses future visual state prediction alongside action prediction within a pretrained Vision-Language Model framework. Results indicate that representations learned from these videos enhance performance on out-of-distribution robot tasks, with improvements scaling with video data volume. The study also analyzes the impact of objective choices and data curation strategies for leveraging action-free videos in scalable robot learning.
The authors propose SPAR, a multi-task architecture leveraging action-free human egocentric videos to improve robot policy generalization on out-of-distribution tasks, with performance gains attributed to scaling video data and specific objective choices.
Source: arXiv Robotics — research abstracts · Read original article ↗
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Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
- Data origin
- Human egocentric dataOpen source S1
- paper: not_reported
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Ego Guidance: Enhancing Robot Policy Learning with Action-Free Human Egocentric Videos
Open source S1
that representations learned from egocentric videos improve downstream performance on out-of-distribution robot tasks, with further gains as video data scales. We also study the effects of objective choices and data curation, providing insights into how to leverage action-free videos for scalable robot learning.
Open source S3
arXiv:2611.13685v1 Announce Type: new Abstract: In this paper, we investigate how human egocentric videos can benefit robot policy learning without action supervision. Building on a pretrained VLM, we propose Shared Prediction on Action and Representation (SPAR), an MoT architecture in which future visual state prediction can provide independent and complementary learning signals alongside action prediction. We find
Open source S2
Implications for data suppliers
RoboSignal interpretation and collection questions, not statements of buyer demand.
- Check whether the intended buyer needs human observations, hand or object annotations, or aligned robot actions. Human video alone does not provide robot commands.
- Validate demand and acceptance criteria with a buyer before scaling. Publication, popularity and a research result do not establish a purchase commitment.
Source：arXiv Robotics — research abstracts · arxiv.org