eureka-research/DrEureka

• hightopics#1

  Add specific topics to the repository

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  sim-to-real, robotics, large-language-models, llm, reinforcement-learning, simulation, robot-learning, rss2024

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• highreadme#2

  Reposition the README's opening paragraph to clarify the repo's nature

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  Transferring policies learned in simulation to the real world is a promising strategy for acquiring robot skills at scale. However, sim-to-real approaches typically rely on manual design and tuning of the task reward function as well as the simulation physics parameters, rendering the process slow and human-labor intensive. In this paper, we investigate using Large Language Models (LLMs) to automate and accelerate sim-to-real design. Our LLM-guided sim-to-real approach

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  This repository provides the official codebase for DrEureka, a novel framework for Language Model Guided Sim-to-Real Transfer, as presented in our RSS 2024 paper. DrEureka addresses the challenge of manually designing and tuning sim-to-real approaches by leveraging Large Language Models (LLMs) to automate and accelerate the transfer of robot policies from simulation to the real world.

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• mediumreadme#3

  Add a concise 'What is DrEureka?' section to the README

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  ## What is DrEureka?
  DrEureka is a research framework designed to automate and accelerate the sim-to-real transfer process for robot policies. It uniquely integrates Large Language Models (LLMs) to guide the design and optimization of both task reward functions and simulation physics parameters, significantly reducing human labor. Key capabilities include:
  - LLM-driven automation of sim-to-real design.
  - Accelerated transfer of robot skills from simulation to real-world environments.
  - Focus on policy transfer for robotics applications.

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Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• CATEGORY QUERY

  How to effectively transfer robot policies developed in simulation to real-world environments?

  you: not recommended
  AI recommended (in order):

  1. NVIDIA Isaac Sim
  2. MuJoCo (google-deepmind/mujoco)
  3. Gazebo (osrf/gazebo)
  4. ROS (ros/ros)
  5. MATLAB
  6. Simulink
  7. Simscape Multibody
  8. PyBullet (bulletphysics/bullet3)
  9. PyTorch (pytorch/pytorch)
  10. TensorFlow (tensorflow/tensorflow)
  11. dSPACE
  12. National Instruments LabVIEW
  13. VeriStand

  AI recommended 13 alternatives but never named eureka-research/DrEureka. This is the gap to close.

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• CATEGORY QUERY

  Looking for tools to improve sim-to-real robot policy transfer using large language models.

  you: not recommended
  AI recommended (in order):

  1. RoboCat
  2. RT-1 / RT-2
  3. OpenAI Gym / Gymnasium (Farama-Foundation/Gymnasium)
  4. Isaac Gym
  5. Habitat (facebookresearch/habitat-lab)
  6. RLBench (google-deepmind/rlbench)
  7. PyBullet (bulletphysics/bullet3)
  8. ROS (Robot Operating System)
  9. MoveIt (ros-planning/moveit2)

  AI recommended 9 alternatives but never named eureka-research/DrEureka. This is the gap to close.

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• Metadata completeness

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  Suggestion:

• README presence

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• Compared to common alternatives in this category, what is the core differentiator of eureka-research/DrEureka?

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  ✓AI named eureka-research/DrEureka explicitly

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• If a team adopts eureka-research/DrEureka in production, what risks or prerequisites should they evaluate first?

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  ✓AI named eureka-research/DrEureka explicitly

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• In one sentence, what problem does the repo eureka-research/DrEureka solve, and who is the primary audience?

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  ✓AI named eureka-research/DrEureka explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?