Leveraging Internet Video Data to Develop Generalist Robots

แนวคิดหลัก

Leveraging large-scale internet video data can help overcome the data bottleneck in robotics and facilitate the development of generalist robots capable of performing a diverse range of physical tasks in unstructured real-world environments.

บทคัดย่อ

This survey presents an overview of methods for learning from video (LfV) in the context of reinforcement learning (RL) and robotics. The key focus is on developing techniques that can scale to large internet video datasets and extract foundational knowledge about the world's dynamics and physical human behavior, which can then be leveraged to train generalist robots.
The survey begins by discussing the potential benefits of LfV, including improved generalization beyond the available robot data and the possibility of obtaining emergent capabilities not attainable from robot data alone. It then outlines the key challenges in LfV, such as missing action labels in video, distribution shifts between video and robot domains, and the lack of low-level information in video data.
The main body of the survey reviews the literature on video foundation models, which can extract knowledge from large, heterogeneous video datasets, as well as methods that specifically leverage video data for robot learning. The latter are categorized based on which RL knowledge modality (representations, policies, dynamics models, reward functions, or value functions) benefits from the use of video data. Techniques for mitigating LfV challenges, such as action representations to address missing action labels, are also discussed.
The survey further examines LfV datasets and benchmarks, before concluding with a discussion of the key challenges and opportunities in this emerging field. It advocates for scalable approaches that can leverage the full range of available data and target the key benefits of LfV, ultimately facilitating progress towards obtaining general-purpose robots.

สถิติ

"There are huge quantities of video data freely available on the internet (e.g., YouTube alone contains over ten thousand years of footage [Sj¨oberg, 2023])."
"The largest open-source robot dataset [Padalkar et al., 2023] pales in comparison (see Figure 9), both in terms of quantity and diversity of the data."

คำพูด

"Promisingly, these deep learning methods are transferable to robotics [Brohan et al., 2022, Team et al., 2023b]."
"Crucially, internet video has excellent coverage over many behaviours and tasks relevant to a generalist robot. For example, there are many videos of humans performing household chores, which would be relevant to a general-purpose household robot."

ข้อมูลเชิงลึกที่สำคัญจาก

Towards Generalist Robot Learning from Internet Video: A Survey

by Robert McCar... ที่ arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.19664.pdf

สอบถามเพิ่มเติม

How can video foundation models be further improved to better extract relevant knowledge from internet video data for robotics applications?

Video foundation models can be enhanced in several ways to better extract relevant knowledge from internet video data for robotics applications:

Improved Video Encoders: Developing more advanced video encoders that can effectively capture the dynamics and physical behaviors present in the video data. This can involve exploring novel architectures or incorporating attention mechanisms to focus on relevant parts of the video.

Enhanced Video Prediction Models: Enhancing video prediction models to better anticipate future frames or actions in a video sequence. This can involve incorporating uncertainty estimation to handle the stochastic nature of real-world environments.

Multi-Modal Fusion: Integrating multiple modalities such as text, audio, and sensor data with video information to provide a more comprehensive understanding of the environment. This can help in capturing a broader range of information relevant to robotics tasks.

Self-Supervised Learning: Leveraging self-supervised learning techniques to learn representations from unlabeled video data. This can help in capturing underlying structures and patterns in the data without the need for explicit labels.

Transfer Learning: Utilizing transfer learning approaches to adapt pre-trained video foundation models to specific robotic tasks. This can help in transferring knowledge learned from large-scale internet video datasets to downstream robotic applications.

Attention to Action Representation: Focusing on developing robust action representations that can address the challenge of missing action labels in video data. This can involve learning representations that capture the essence of actions without explicit labels.

By incorporating these strategies, video foundation models can be enhanced to better extract relevant knowledge from internet video data, ultimately improving their utility for robotics applications.

How can the potential limitations of scaling to internet video data alone be addressed, and how can other data sources or techniques be combined to overcome these limitations?

Scaling to internet video data alone may have limitations that can be addressed through the integration of other data sources and techniques:

Complementary Data Sources: Incorporating robot-specific datasets that provide low-level information such as tactile sensing, proprioception, and depth sensing. By combining internet video data with robot-specific data, the model can have a more comprehensive understanding of the environment.

Simulated Data: Augmenting internet video data with simulated data to create a more diverse and controlled training dataset. Simulated data can help in addressing specific scenarios or tasks that may be underrepresented in internet video data.

Human Demonstrations: Utilizing human demonstrations or expert knowledge to provide additional insights and guidance for the learning process. Human demonstrations can offer valuable information on complex tasks or behaviors that may not be fully captured in internet video data.

Multi-Modal Fusion: Integrating data from multiple modalities such as text, images, and sensor data to provide a more holistic view of the environment. This fusion can help in capturing different aspects of the task and improving the model's overall performance.

Domain Adaptation Techniques: Employing domain adaptation methods to bridge the gap between internet video data and the robot domain. Techniques such as adversarial training or domain-specific fine-tuning can help in aligning the distributions of different datasets.

By combining these data sources and techniques, the limitations of scaling to internet video data alone can be mitigated, leading to more robust and effective models for robotics applications.

How can the insights and knowledge gained from internet video be effectively integrated with the low-level skills and information available in robot-specific datasets to create truly capable generalist robots?

To integrate the insights from internet video with the low-level skills from robot-specific datasets for creating generalist robots, the following strategies can be employed:

Hierarchical Learning: Implementing a hierarchical learning approach where high-level knowledge from internet video data guides the learning of low-level skills from robot-specific datasets. This hierarchical structure allows for the integration of different levels of information seamlessly.

Representation Learning: Learning shared representations that capture both the high-level concepts from internet video and the low-level skills from robot-specific data. By learning representations that encode relevant information from both sources, the model can effectively leverage the strengths of each dataset.

Transfer Learning: Utilizing transfer learning techniques to adapt knowledge from internet video to the robot domain. Pre-trained models from internet video data can be fine-tuned on robot-specific tasks to transfer the acquired knowledge effectively.

Multi-Task Learning: Training the model on a diverse set of tasks that encompass both high-level understanding from internet video and low-level skills from robot-specific data. Multi-task learning can help in jointly learning different aspects of the task and improving the model's overall performance.

Continuous Learning: Implementing a continuous learning framework where the model can adapt and update its knowledge over time based on new experiences and data. This allows the model to continuously improve and refine its capabilities in both high-level understanding and low-level skills.

By employing these strategies, the insights and knowledge gained from internet video can be seamlessly integrated with the low-level skills and information available in robot-specific datasets, leading to the development of truly capable generalist robots.