Lecture on Low-Level Embodied Intelligence with Foundation Models

Introduction

• Speaker: Aishia, senior research scientist at Google DeepMind, working on the robotics team.
• Background: PhD from Stanford, focusing on intelligent embodied agents to interact with unstructured environments.
• Mission: Build intelligent robots with applications in home robotics.

Embodied Intelligence

• Definition: Integral part of AI, crucial for achieving artificial general intelligence.
• Use Cases:
  • Home robots for cleaning, cooking, and caring for aging family members.
• Current Limitations: AI primarily functions in virtual environments, struggles with messy, complex real-world tasks.

Real-World Challenges for Robots

• Example 1: Robot mistakenly opens a Coke can while attempting to place it in the sink, demonstrating misunderstanding of physical interaction.
• Example 2: Robot's programmed behavior leads to spillage from a can due to incorrect arm positioning.
• Key Insight: Robots require an understanding of physical laws, spatial relationships, and the consequences of their actions.

Learning Through Interaction

• Approach: Create interactive environments for robots to explore and learn through play, similar to human childhood experiences.
• Gibson Environment: A simulation environment that accurately represents visual and physical worlds, allowing agents to learn navigation and manipulation.
• Interactive Gibson: An evolved version that includes more complex interactions and modeling of physical laws.

Shift in Computer Vision Field

• Paradigm Shift: Transition from internet AI to embodied AI, focusing on action-oriented learning.
• Importance of Data: Large amounts of data still critical for learning intelligent behavior, whether from static datasets or simulations.

Foundation Models and Robotics

• Foundation Models: Use of semantic prior from foundation models to enhance robotic decision-making and action generation.
• Palm Sayan Algorithm: High-level planning algorithm that uses language models to parse commands and generate actionable tasks.
  • Affordance scoring helps ensure actions are feasible.
• Low-Level Policies: Robotics Transformer 1 (RT1) manages low-level actions based on extensive data from human demonstrations.

Recent Progress and Future Work

• Scaling Up: Emphasis on collecting large-scale, diverse datasets and training models to leverage foundation models in robotics.
• New Interfaces: Developing interfaces between language models and robotic control for improved low-level action execution.
• Reward Functions: Propose using reward functions as a bridge between language models and robotic actions.

Conclusion

• Future Potential: Combining foundation models with robotics can enhance the development and capabilities of embodied intelligent systems.
• Ongoing Challenges: Addressing data limitations and ensuring safety and alignment in robotic actions.
• Open Questions: Exploration of scaling robotics data collection and optimizing interfaces for robot training.

Q&A Session Insights

• Data Collection: Emphasis on the challenges in collecting meaningful robotic action data.
• Safety Considerations: Importance of ensuring robotic actions do not cause harm; developing ethical safety protocols.