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:

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:

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