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

Research Directions

Challenges

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