robotics
**Robotics and Embodied AI**
**LLMs for Robotics**
LLMs enable robots to understand natural language commands and reason about tasks.
**Key Approaches**
**High-Level Planning**
LLM plans tasks, specialized models execute:
```python
def robot_task_planner(task: str) -> list:
plan = llm.generate(f"""
You are a robot assistant. Break down this task into steps
that map to available robot skills.
Available skills:
- pick_up(object): grasp and lift object
- place(location): put held object at location
- navigate(location): move to location
- scan(): look around for objects
Task: {task}
Step-by-step plan:
""")
return parse_plan(plan)
```
**Vision-Language-Action Models**
End-to-end models that take in images and language, output actions:
```
[Camera Image] + [Language Instruction]
|
v
[VLA Model (RT-2, etc.)]
|
v
[Robot Action (dx, dy, dz, gripper)]
```
**Code as Policies**
LLM generates executable code for robot control:
```python
def code_as_policy(task: str, scene: str) -> str:
code = llm.generate(f"""
Generate Python code using robot API to complete task.
Scene: {scene}
Task: {task}
Robot API:
- robot.move_to(x, y, z)
- robot.grasp()
- robot.release()
- robot.get_object_position(name)
Code:
""")
return code
```
**Simulation Environments**
| Environment | Use Case |
|-------------|----------|
| Isaac Sim | NVIDIA, high fidelity |
| MuJoCo | Fast physics simulation |
| PyBullet | Lightweight, open source |
| Habitat | Navigation, embodied AI |
**Research Directions**
| Direction | Description |
|-----------|-------------|
| RT-2 (Google) | VLM for robot control |
| Robot Foundation Models | Pre-trained on diverse robot data |
| Sim-to-Real | Train in sim, deploy on real robot |
| Multi-modal grounding | Connect language to physical world |
**Challenges**
| Challenge | Consideration |
|-----------|---------------|
| Safety | Real-world consequences |
| Generalization | New objects, environments |
| Latency | Real-time requirements |
| Perception | Noisy, partial observations |
| Data scarcity | Limited robot data |
**Best Practices**
- Use simulation extensively before real robot
- Implement safety boundaries
- Human-in-the-loop for critical operations
- Start with constrained tasks
- Combine LLM reasoning with specialized control