Towards Generalist Robot Learning from Internet Video: A Survey
(2404.19664)Abstract
This survey presents an overview of methods for learning from video (LfV) in the context of reinforcement learning (RL) and robotics. We focus on methods capable of scaling to large internet video datasets and, in the process, extracting foundational knowledge about the world's dynamics and physical human behaviour. Such methods hold great promise for developing general-purpose robots. We open with an overview of fundamental concepts relevant to the LfV-for-robotics setting. This includes a discussion of the exciting benefits LfV methods can offer (e.g., improved generalization beyond the available robot data) and commentary on key LfV challenges (e.g., challenges related to missing information in video and LfV distribution shifts). Our literature review begins with an analysis of video foundation model techniques that can extract knowledge from large, heterogeneous video datasets. Next, we review methods that specifically leverage video data for robot learning. Here, we categorise work according to which RL knowledge modality benefits from the use of video data. We additionally highlight techniques for mitigating LfV challenges, including reviewing action representations that address the issue of missing action labels in video. Finally, we examine LfV datasets and benchmarks, before concluding the survey by discussing challenges and opportunities in LfV. Here, we advocate for scalable approaches that can leverage the full range of available data and that target the key benefits of LfV. Overall, we hope this survey will serve as a comprehensive reference for the emerging field of LfV, catalysing further research in the area, and ultimately facilitating progress towards obtaining general-purpose robots.
Overview
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Robotics and reinforcement learning are extensively growing fields, greatly enhanced by integrating video data from the internet, particularly because of the rich, dynamic interactions and human behaviors that can be analyzed and learned.
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Learning from Video (LfV) assists robots in generalizing tasks, improving data efficiency, and developing new capabilities by observing and processing videos displaying a range of human activities and physical interactions.
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Despite its potential, LfV faces challenges like the high dimensionality of videos, lack of detailed low-level information essential for robotic operations, and the absence of specific action and reward annotations in most internet videos.
Understanding Learning from Video for Robotics
Reinforcement learning (RL) and robotics have been making strides thanks to advances in machine learning techniques and the burgeoning availability of diverse data sources. In this complex interplay, a particularly interesting development is the integration of video data into robotics, often referred to as Learning from Video (LfV). The rationale behind LfV is both intuitive and compelling: videos, especially those broadly available on the internet, are rich in demonstrations of dynamic interactions and human behaviors, making them a valuable resource for teaching robots.
Unpacking the Potential of Learning from Video (LfV)
Videos carry a wealth of information about the physical world and how entities within it interact dynamically over time. For robots, which operate in the physical space, this information is gold. Imagine a robot learning nuanced physical tasks like picking up delicate items, navigating through clutter, or interacting socially by observing videos of humans performing similar tasks.
There are three main angles from which LfV proves beneficial:
- Generalization: Videos can help robots learn to generalize beyond the tasks and environments they were explicitly trained on by demonstrating a broad spectrum of scenarios.
- Improved Data Efficiency: Robots can learn from fewer examples by leveraging the rich, pre-existing datasets of videos.
- Emergence of New Capabilities: By learning from diverse human behaviors, robots can potentially develop new skills that were not explicitly taught.
Challenges on the Road
Despite the high potential, learning from videos is not without its challenges:
- High Dimensionality: Videos are inherently high-dimensional, making them computationally expensive to process and learn from.
- Missing Low-Level Details: Videos often lack detailed information required for robot operation, like exact object weight or material properties, which can be crucial for tasks involving manipulation.
- Action and Reward Annotation: Most internet videos do not come with annotations about the actions taken or the rewards obtained, crucial elements for many learning algorithms.
Practical Approaches and Techniques
To leverage video data effectively, robots can use a variety of techniques:
- Video as a learning platform: By observing videos, robots can extract patterns and rules about physical interactions and dynamics.
- Simulators and virtual environments: Implementing knowledge learned from videos into simulators can provide an interactive medium for robots to practice and hone their skills.
- Combining real and synthetic data: Mixing lessons learned from both real-world interactions and video observations can balance out the data needs and provide a more rounded learning experience.
Looking Forward
The integration of learning from video into robotics is still in its early days but holds a promise of significant advancements. As algorithms become more sophisticated and datasets richer, the ability of robots to learn from human-recorded video data will likely become a standard part of their education. The future might see robots that can not only perform complex physical tasks with ease but also understand social cues and interact fluidly in human environments, all thanks to the diverse scenarios and interactions they observed in videos.
In conclusion, as we push the boundaries of what robots can learn from videos, we're shaping a future where robotic systems can be more adaptable, efficient, and insightful. The journey from video-watching to action-taking encapsulates the fusion of sensory perceptions with mechanical precision, a hallmark of advanced robotic systems. As this field evolves, the potential applications are bound to expand, possibly revolutionizing how robots are integrated into everyday life and industrial operations.
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