I. Opportunities and Bottlenecks
Opportunities
- Instruction understanding capability: LLM can achieve zero-shot human instruction interpretation
- Task planning capability: Can decompose complex tasks into long-step sequences
- Environmental adaptability: Flexibly handle open, unstructured environments
Bottlenecks and Challenges
- Safety risks: Lack of formal verification, vulnerability to adversarial examples
- Real-time contradiction: GPT-3 level model inference takes hundreds of milliseconds to seconds, while industrial robots require 10-100ms
- Reliability defects: Lack of physical commonsense, long-range planning deviations
II. Hierarchical Architecture
Architecture Design Philosophy
Organically combine LLM cognitive capabilities with real-time advantages of traditional control systems
Layer Division
| Layer | Name | Working Frequency | Responsibilities |
|---|---|---|---|
| High | LLM Decision Layer | 0.1-1Hz | Natural language understanding, task decomposition, environmental semantic understanding |
| Middle | Transformation Layer | 10-100Hz | Task-action mapping, state monitoring, anomaly detection |
| Low | Control Layer | 100-1000Hz | Real-time motion control, sensor processing, closed-loop regulation |
III. Rise of Language Behavior Models (LBM)
Concept
General-purpose intelligent systems that can simultaneously process advanced cognition and physical behavior
Core Features
- Multi-modal perception fusion
- Behavior output capability
- Cognitive-behavior closed loop
Typical LBM Architecture
- Perception module: Computer vision, speech recognition, multi-sensor fusion
- Cognitive decision module: Task planning, environmental reasoning, behavior strategy generation
- Motion control module: Action parameterization, trajectory planning, real-time control
IV. Safety and Control Guarantees
System-level Safety Monitoring
- Formal verification mechanisms
- Data-driven reachability analysis
Model-level Safety Constraints
- Safety-aware pre-training
- Multi-agent collaborative verification
Layered Protection Architecture
- Upper layer: Intelligent decision layer (LLM + real-time verification module)
- Lower layer: Hard control layer (traditional control theory + physical limit protection + emergency stop function)
V. Core Conclusions
Intelligent Improvements Brought by LLM
- Advanced cognitive capabilities: Understanding complex instructions, contextual reasoning
- Task generalization: Handle unseen task scenarios
Technical Challenges Introduced
- Real-time bottlenecks (hundreds of milliseconds vs 10-100ms)
- Safety concerns (instruction safety, physical feasibility, real-time monitoring)
Solution Trends
- Hierarchical architecture (upper layer LLM planning, lower layer deterministic control)
- Specialized model development (robot behavior large models)
Key Constraints
Multiple safety protection mechanisms must be established: pre-safety screening, in-process feasibility verification, post-event anomaly handling plans