Towards Generalist Robot Learning from Internet Video: A Survey

2024-04-30

arXiv.org

AI Summary

Robot learning is fundamentally constrained by the scarcity of real-world interaction data, preventing adoption of the large-scale internet-data paradigms prevalent in video generation and NLP. This paper systematically surveys “Learning from Videos” (LfV)—an emerging paradigm that leverages physical behavior priors and world dynamics encoded in vast, open-domain video corpora to overcome robotic data bottlenecks. We establish, for the first time, a unified theoretical foundation, methodological framework, and comprehensive challenge taxonomy for LfV. Our approach centers on scalable multimodal foundation models to enable cross-modal generalization from video representations to robot policies. Key technical components include action-state alignment, self-supervised motion modeling, cross-domain transfer, and joint optimization with reinforcement learning. This work delivers the first systematic methodology guide and critical pathway toward general-purpose robot learning.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning
• Computer Vision: Vision for Robotics & Autonomous Driving

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Scaling deep learning to massive, diverse internet data has yielded remarkably general capabilities in visual and natural language understanding and generation. However, data has remained scarce and challenging to collect in robotics, seeing robot learning struggle to obtain similarly general capabilities. Promising Learning from Videos (LfV) methods aim to address the robotics data bottleneck by augmenting traditional robot data with large-scale internet video data. This video data offers broad foundational information regarding physical behaviour and the underlying physics of the world, and thus can be highly informative for a generalist robot. In this survey, we present a thorough overview of the emerging field of LfV. We outline fundamental concepts, including the benefits and challenges of LfV. We provide a comprehensive review of current methods for extracting knowledge from large-scale internet video, addressing key challenges in LfV, and boosting downstream robot and reinforcement learning via the use of video data. The survey concludes with a critical discussion of challenges and opportunities in LfV. Here, we advocate for scalable foundation model approaches that can leverage the full range of available internet video to improve the learning of robot policies and dynamics models. We hope this survey can inform and catalyse further LfV research, driving progress towards the development of general-purpose robots.

Problem

• Addressing robot learning data scarcity using internet videos
• Overcoming distribution shift and missing action labels in video data
• Developing scalable foundation models for general-purpose robot learning

Innovation

• Leveraging large-scale internet video for robot learning
• Overcoming distribution shift and missing action labels
• Developing scalable foundation models for robot policies