Robotic Foundation Model (RFM)
How data helps future humanoid robots reborn
Last updated
How data helps future humanoid robots reborn
Last updated
Robotic Foundation Models (RFMs) [1] represent a paradigm shift in how we develop and deploy AI systems for robotics. These models leverage large-scale, multimodal datasets to learn generalized representations that can be fine-tuned for specific robotic tasks. Unlike traditional models tailored for narrow, task-specific domains, RFMs are designed to adapt and generalize across diverse applications, from industrial automation to assistive robotics.
RFMs are at the forefront of integrating advanced AI into robotics, prompting several pivotal research areas:
Multimodal Learning and Integration: Developing methods to effectively combine data from various sources—such as vision, language, and tactile inputs—to enhance robots' comprehensive understanding and interaction with their environments.
Generalization and Transfer Learning: Investigating how RFMs can apply knowledge from one task or domain to novel situations, enabling robots to perform tasks without extensive retraining.
Embodied Intelligence and Physical Interaction: Exploring how RFMs can be integrated with physical robotic systems to improve interaction with the physical world, including manipulation and mobility tasks.
Human-Robot Interaction: Enhancing the ability of robots to understand and respond to human instructions and behaviors, facilitating more natural and effective collaboration.
A prominent example of an RFM is the Vision-Language-Action (VLA) model [3], which provides an end-to-end solution for robotic tasks. The VLA model integrates visual perception, natural language understanding, and action planning into a unified framework. For instance, in a kitchen setting, the model can process a command like "Pick up the red cup from the table and place it on the shelf." Using its multimodal capabilities, the VLA model identifies the red cup (vision), interprets the task requirements (language), and generates the appropriate motor actions (action planning) to complete the task seamlessly.
The VLA model is an end-to-end approach that eliminates the need for separate modules or intermediate translations, enabling smoother task execution and adaptability to real-world environments. By learning from diverse datasets of videos, sensor inputs, and textual descriptions, the VLA model exemplifies how RFMs can generalize across tasks and domains while maintaining robust performance in dynamic settings.
Data Scale and Diversity:
Traditional Robotics: Models are trained on small, task-specific datasets, limiting their adaptability.
RFMs: Leverage large-scale, internet-derived datasets, enabling superior generalization and problem-solving beyond the training scope.
Learning Paradigms:
Traditional Robotics: Focuses on narrowly defined models tailored to specific tasks, often requiring manual tuning.
RFMs: Employ foundation models pre-trained on extensive, multimodal data, demonstrating emergent abilities like zero-shot learning.
Adaptability and Generalization:
Traditional Robotics: Models typically perform well only within the boundaries of their training data.
RFMs: Exhibit remarkable adaptability, enabling their use across a variety of tasks with minimal retraining.
Integration of Multimodal Data:
Traditional Robotics: Relies on specific types of data processed in isolation.
RFMs: Combine multimodal inputs—such as vision, language, and sensory data—into unified representations, enhancing understanding and interaction.
Emergent Capabilities:
Traditional Robotics: Limited to predefined tasks, with challenges in addressing unforeseen scenarios.
RFMs: Capable of tackling novel problems using zero-shot learning and other emergent abilities.
A recent study [2] investigates how the quantity and diversity of training data affect a robot's ability to generalize in robotic manipulation tasks. The authors conducted an extensive empirical study, collecting over 40,000 demonstrations and performing more than 15,000 real-world robot rollouts. Their key findings include:
Power-Law Relationship of Scale and Performance: The generalization performance of robotic policies improves following a power-law trend as the number of training environments and objects increases. This indicates that doubling the diversity of training data leads to consistent, albeit diminishing, performance gains.
Importance of Diversity: The diversity of training environments and objects is very crucial. The RFM performance increases with the sheer number of demonstrations per environment or object. This law further results in the difficulty of data collection in diverse environments.
Efficient Data Collection Strategy: Gathering diverse experiences across various settings helped train policies achieving approximately 90% success rates in novel environments with unseen objects.
These findings suggest that a new scaling law is built in RFM, i.e., the scale and diversity of data and the ability of the RFM. To build a stronger RFM, data is necessary.
Data forms the backbone of RFMs, determining their capacity for generalization and adaptability. Several types of data are critical for building effective RFMs:
Visual Data:
Essential for object detection, scene understanding, and spatial reasoning.
Includes 2D images, videos, and 3D data like point clouds and volumetric representations.
Natural Language Data:
Enables RFMs to interpret and execute high-level commands using natural language.
Includes annotated datasets of human instructions, narratives, and dialogues.
Human Demonstration Data:
Enhances models' ability to replicate human actions via imitation learning.
Combines visual and linguistic annotations to provide contextually rich demonstrations
Synthetic and Simulated Data:
Bridges the gap in real-world data availability by augmenting datasets with realistic simulations.
Useful for tasks like navigation and planning in dynamic or hazardous environments.
[1] Roya Firoozi et al., "Foundation Models in Robotics: Applications, Challenges, and the Future," arXiv:2312.07843
[2] Lin, Fanqi, et al. "Data Scaling Laws in Imitation Learning for Robotic Manipulation." arXiv:2410.18647.
[3] Kim, Moo Jin, et al. "OpenVLA: An Open-Source Vision-Language-Action Model." arXiv preprint arXiv:2406.09246 (2024).
