Advanced Foundation Models for Next-Generation AI Robotics
This research initiative aims to push the boundaries of AI robotics, leveraging the power of foundation models to create more versatile, intelligent, and adaptable robotic systems capable of operating effectively in complex, real-world environment
Background and Rationale
Recent advancements in foundation models present a transformative opportunity to significantly enhance key components of AI robot autonomy, including cognition, decision-making, and control. Traditional deep learning-based AI models, trained on limited task-specific datasets, have shown constraints in adaptability across diverse applications. In contrast, foundation models pre-trained on extensive internet-scale data have demonstrated remarkable generalization capabilities across a wide spectrum of problems, sometimes exhibiting zero-shot solutions to novel challenges.
Large language models have shown potential in programming procedures and providing common-sense reasoning for robotic task execution, while vision-language models enable open-ended recognition of non-predefined stylistic features. However, the practical implementation of these foundation models in robotics faces several challenges, including:
- Multidimensional and integrated perception of space-time and sound
- Scarcity of robot-specific training data
- Ensuring safety and quantifying uncertainty
- Achieving real-time execution capabilities
Research Objectives
Our primary goal is to develop a versatile foundation model for next-generation AI robots. Specifically, we aim to:
- 1. Construct a universal foundation model applicable in real-world scenarios and adaptable to various forms of AI robotics.
- 2. Acquire enabling technologies for context-aware and autonomous AI robots in cognition, decision-making, and control.
Research and Development Phases
Phase 1: Initial Foundation Model Development and Basic Robot Control Technology
- 1. Multimodal Perception Foundation Models:
- Develop open vocabulary object detection and segmentation
- Research interactive understanding and synthesis models integrating sound and speech
- Develop multi-sensor and modality fusion interface technologies
- 2. General-Purpose Robot Control:
- Develop robot motion generation and retargeting techniques
- Create a reinforcement learning-based multimodal decision-making foundation model
- 3. Embodied AI Foundation Model Technology:
- Develop multimodal common sense and ethical reasoning capabilities
- Create long-term memory and domain knowledge augmentation techniques
- Develop world learning techniques for multimodal foundation models
Phase 2: Model Refinement, Real-World Application, and Performance Optimization
- 1. Advanced Multimodal Perception:
- Enhance open-vocabulary object detection and segmentation
- Advance interactive understanding and synthesis models integrating sound and speech
- Improve multi-sensor and modality fusion interface technologies
- 2. Enhanced General-Purpose Robot Control:
- Advance robot motion generation and retargeting techniques
- Apply reinforcement learning-based multimodal decision models in real-world scenarios
- Research model adaptation and quantization for efficient robot behavior control
- 3. Advanced Embodied AI:
- Apply common sense and ethical reasoning models in real-world scenarios
- Enhance long-term memory and domain knowledge augmentation techniques
- Evolve world learning technologies
- Develop adaptive and evolving robotic foundation models through complementary learning
