What Foundation Models can Bring for Robot Learning in Manipulation : A Survey (2404.18201v3)

Abstract: The realization of universal robots is an ultimate goal of researchers. However, a key hurdle in achieving this goal lies in the robots' ability to manipulate objects in their unstructured surrounding environments according to different tasks. The learning-based approach is considered an effective way to address generalization. The impressive performance of foundation models in the fields of computer vision and natural language suggests the potential of embedding foundation models into manipulation tasks as a viable path toward achieving general manipulation capability. However, we believe achieving general manipulation capability requires an overarching framework akin to auto driving. This framework should encompass multiple functional modules, with different foundation models assuming distinct roles in facilitating general manipulation capability. This survey focuses on the contributions of foundation models to robot learning for manipulation. We propose a comprehensive framework and detail how foundation models can address challenges in each module of the framework. What's more, we examine current approaches, outline challenges, suggest future research directions, and identify potential risks associated with integrating foundation models into this domain.

Exploring the Impact of Foundation Models on Robot Manipulation Learning

Introduction to Foundation Models in Robotics

The application of foundation models in robotics, particularly in the field of manipulation, heralds a promising avenue towards achieving robots that can operate universally in assorted environments and tasks. Historically, this area has pivoted on learning-based methods with embedded models like deep learning, reinforcement learning, and imitation learning playing pivotal roles. The surge in performance accrued through models pre-trained on diverse, large-scale datasets has armed researchers with new tools to push the boundaries in this domain further, especially integrating models like BERT and GPT-3 into robotic tasks.

Types of Foundation Models Used in Robotics

Foundation models come in various forms, each offering distinct benefits to the field of robotic manipulation:

1. LLMs such as BERT and GPT-3, invaluable for their prowess in text understanding and generation, now support direct policy coding and naturalistic interaction simulations.
2. Vision Foundation Models (VFMs) enhance perception capabilities, pivotal for robots operating in dynamic or visually complex settings.
3. Vision LLMs (VLMs) excel in understanding and generating responses by integrating visual and textual data, an asset for tasks requiring multimodal insights.
4. Visual Content Generation Models (VGMs), which are crucial for simulating realistic 3D environments that robots might need to interact with during training phases.
5. Large Multimodal Models (LMMs) transcend traditional modal boundaries, offering a holistic approach to understanding environments by integrating haptic feedback, sound, and more.
6. Robot Foundation Models (RFMs), like RT-X, represent a cutting-edge fusion of multiple data types aimed at refining the policy models that drive robot actions from direct observations.

General Contributions and Challenges

Foundation models contribute significantly by enhancing interaction capabilities, boosting perceptual accuracy, and refining the granularity of robotic responses to environmental stimuli and task requirements. Specific benefits include generating complex action sequences, enhancing skill learning, and elevating interaction naturalism. However, challenges persist, particularly in ensuring safety and stability in autonomous operations, setting a barrier to achieving the broad deployment of such advanced systems in unpredictable real-world settings.

Future Directions and Considerations

Looking ahead, researchers are poised to explore developing overarching frameworks that could seamlessly integrate these diverse models to construct robots with truly generalized manipulation abilities. Drawing parallels with the progression seen in autonomous driving technologies, a similar multi-aspect approach might pave the path toward more adaptable and safer robotic systems.

Moreover, the ongoing innovation in dataset generation and model training paradigms promises to gradually bridge the gap between simulation-based learning environments and real-world applicability, ensuring not only functionality but also higher safety. The speculative development of more context-aware robots capable of learning and adapting in situ underscores the transformative potential of foundation models in robot learning.

Conclusion

In essence, while foundation models are spearheading a revolution in robotic manipulation, achieving a level of generalized capability analogous to human-like manipulation remains a complex target layered with technical, safety, and ethical considerations. Nonetheless, current advancements suggest promising approaches to crafting more intelligent, perceptive, and adaptable robotic systems in the near future, marking a significant stride toward the realization of robots as ubiquitous and versatile partners in various aspects of human activity.