Agentic Robotics Control

This challenge focuses on developing an advanced agentic control system for a simulated humanoid robot. You will use AutoGen to orchestrate a team of specialized agents, each responsible for different robotic sub-systems (e.g., perception, locomotion, manipulation). The system must demonstrate robust task execution, adaptability to dynamic environments, and adhere to safety protocols using constitutional AI principles. It will integrate Gemini 3 Pro for deep reasoning and code generation for complex actions, and OpenAI GPT 5.2 for fast, reactive decision-making via MCP tool calls to a simulated robot API.

• Orchestrate a role-based agent team using AutoGen for simulated humanoid robot control, including a Task Planner, Perception Processor, Action Executor, and Safety Monitor agents.

• Implement hybrid instant/deep reasoning patterns: utilize OpenAI o3 for rapid, reactive decision-making in safety-critical situations or simple commands, and Gemini 3 Pro (with Deep Think mode) for complex task planning, code generation for new actions, and problem-solving.

• Design and integrate constitutional AI guards within agent prompts and post-processing to ensure all robotic actions adhere to predefined safety and ethical guidelines.

• Leverage Semantic Kernel for managing high-level mission planning, enabling agents to decompose complex goals into actionable steps and orchestrate MCP tool calls.

• Build MCP-enabled tools that simulate a robot's API (e.g., 'move_joint', 'sense_environment', 'grasp_object'), allowing agents to interact with the simulated robot environment.

• Implement A2A protocol within AutoGen for seamless communication and collaboration between specialized robot control agents, ensuring coordinated action.

• Develop error handling and recovery mechanisms, allowing the agent system to adapt to unexpected events in the simulated environment and safely re-plan.

• Explore Marvin for ensuring structured and reliable output from LLMs for robot commands and state updates.