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How to map the robot-use agent field from current surveys with Awesome Robot Use Agent

Read the survey papers to learn the main approaches, terms, and open problems in robot-use agents.

  • Top pick
  • Research
  • Beginner
  • 30 minutes
  • From the README
  • Any agent

Who it is forA graduate student or product lead who is new to robot-use agents and needs a fast overview before choosing a research or build direction.

The problemRobot-use agent work is spread across papers, blogs, and demos, and it is hard to tell which terms and categories matter.

The outcomeA short written map of the field that names the main categories, the survey sources, and the open challenges.

What you need first

  • A web browser to open the README and the searchable website
  • For the benchmark and tutorial use cases: a Python environment and access to the linked project pages, which the README does not set up for you
  • For the tutorial use case: a Reachy Mini robot and the hardware named in the linked Hugging Face and NVIDIA post

Steps

  1. Open the Articles section and read the Robot-Use Agents perspective to get the core definition.
  2. Open the Surveys section and read the two 2026 surveys, Towards Agentic Robots and Survey on Multimodal Embodied Agents, which list their own taxonomies.
  3. Read the Towards Embodied Agentic AI review for how LLM and VLM agents connect to robot APIs and ROS.
  4. Write down the categories and terms the surveys use, such as PAPAV or the four domains of Model, Data, Environment, and Harness.
  5. List the open problems the surveys mention, such as physical constraints and benchmark coverage.

Prompt for your agent

Using the Awesome-Robot-Use-Agent README at https://github.com/kairunwen/Awesome-Robot-Use-Agent, read the Surveys section and produce a one-page summary of the categories and open problems named in the two 2026 surveys for <topic>. Verify each claim against the linked survey entry and note any claim the README does not support.

Caveat. The list summarizes each paper from the README text, so read the linked paper before relying on any specific taxonomy.

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