Home Robot Agent
This challenge focuses on designing and implementing an advanced agentic system for an autonomous home robot, simulating tasks like complex multi-step operations (e.g., making espresso or loading a dishwasher). Participants will leverage GPT-5 for high-level, extended thinking and planning, enabling the robot agent to break down abstract commands into concrete, executable actions. The system will integrate with a simulated smart home environment using the MCP for seamless tool integration. Langroid will be used to ground the GPT-5 agent in the simulated physical environment, allowing it to perceive its state, execute actions, and handle dynamic changes. This challenge emphasizes robust task decomposition, adaptive planning, and reliable tool use in a simulated real-world context, showcasing how cutting-edge generative AI can drive future robotics.
What you are building
The core problem, expected build, and operating context for this challenge.
This challenge focuses on designing and implementing an advanced agentic system for an autonomous home robot, simulating tasks like complex multi-step operations (e.g., making espresso or loading a dishwasher). Participants will leverage GPT-5 for high-level, extended thinking and planning, enabling the robot agent to break down abstract commands into concrete, executable actions. The system will integrate with a simulated smart home environment using the MCP for seamless tool integration. Langroid will be used to ground the GPT-5 agent in the simulated physical environment, allowing it to perceive its state, execute actions, and handle dynamic changes. This challenge emphasizes robust task decomposition, adaptive planning, and reliable tool use in a simulated real-world context, showcasing how cutting-edge generative AI can drive future robotics.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
What you should walk away with
Master extended thinking techniques with GPT-5, implementing adaptive reasoning budgets to enable complex, multi-step task decomposition and planning.
Build Langroid agents capable of grounding an LLM in a simulated physical environment, handling observations and issuing commands via a robot interface.
Implement Model Context Protocol (Model Context Protocol) for defining, registering, and invoking tools (simulated robot actions, smart device controls) securely and efficiently within the agent.
Design a `ToolController` component that translates agent-generated Model Context Protocol calls into actual API calls for the simulated smart home and robot.
Develop state management and memory for the Langroid agent, allowing it to maintain context across multiple interactions and task steps.
Orchestrate dynamic planning and re-planning workflows in response to environment feedback or unexpected events, leveraging GPT-5's reasoning.
Integrate monitoring and logging for agent actions and tool invocations to debug and optimize complex robot behaviors.
How this agent runs
The evaluation will assess the robot agent's ability to successfully complete complex, multi-step household tasks in a simulated environment, demonstrating robust planning, tool invocation via MCP, error handling, and...
Challenge input
{ "command": "string", "initial_env_state": { "coffee_machine_status": "off", "water_level": "full", "beans_level": "full", "cup_available": true } }
Agent execution
The configured agent processes the input under the challenge policy.
Evaluated output
{ "final_env_state": { "coffee_machine_status": "on", "water_level": "low", "beans_level": "low", "cup_available": false, "espresso_made": true },...
- Verify that the `espresso_made` flag in the final environment state is true.
- Ensure all necessary MCP-enabled tool calls were successfully made (e.g., power_on_coffee_machine, brew_esp...
- If an initial action fails (simulated), verify the agent attempts to re-plan or correct.
- ActionEfficiency target: 0
- Python execution harness
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- The evaluation module defines the checks.
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.
Run this agent on your dataset and AI stack
Bring your dataset, model providers, and success criteria. We will scope the right managed run for your team.
Scope a managed run[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
Requires VERSALIST_API_KEY. Works with any MCP-aware editor.
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