2024 DrEurekaLanguageModelGuidedSimT

• (Ma, Liang et al., 2024) ⇒ Jason Ma, William Liang, Hungju Wang, Sam Wang, Yuke Zhu, Linxi "Jim" Fan, Osbert Bastani, and Dinesh Jayaraman. (2024). “DrEureka: Language Model Guided Sim-To-Real Transfer.”

Subject Headings:

Notes

• The paper introduces DrEureka, a novel algorithm leveraging Large Language Models (LLMs) to automate the design of reward functions and domain randomization parameters for sim-to-real transfer in robotics. This approach minimizes human labor by optimizing both components simultaneously, aiming for efficient and scalable policy deployment in the real world.
• The paper demonstrates that DrEureka can autonomously generate configurations that perform comparably or better than existing human-designed setups. The tested domains include quadruped locomotion and dexterous manipulation tasks, showing broad applicability across different robotic platforms.
• The paper highlights a method where LLMs first synthesize reward functions followed by a simulation that helps define a suitable range for domain randomization parameters, which are then fine-tuned by the LLM to finalize the sim-to-real transfer configuration.
• The paper provides extensive real-world validation and comparative analysis against human-designed configurations. The results indicate that DrEureka-enhanced policies achieve significant improvements in task performance metrics like speed and distance traveled over various terrains.
• The paper tackles the challenge of applying the DrEureka framework to novel tasks such as a quadruped robot balancing and walking atop a yoga ball, a task with no pre-existing sim-to-real transfer configurations, showcasing DrEureka's potential in developing capabilities for new, complex tasks.
• The paper discusses the limitations of DrEureka, such as the static nature of the domain randomization parameters and the absence of a mechanism for selecting the most effective policy from the generated candidates, pointing out areas for future improvement.
• The paper concludes that DrEureka presents a significant step towards fully automated sim-to-real transfers, potentially accelerating the development and deployment of robotic skills without extensive manual intervention, thus broadening the scope of tasks robots can learn and perform autonomously.

Cited By

• http://scholar.google.com/scholar?q=%222024%22+DrEureka%3A+Language+Model+Guided+Sim-To-Real+Transfer

Quotes

Abstract

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 requires only the physics simulation for the target task and automatically constructs suitable reward functions and domain randomization distributions to support real-world transfer. We first demonstrate our approach can discover sim-to-real configurations that are competitive with existing human-designed ones on quadruped locomotion and dexterous manipulation tasks. Then, we showcase that our approach is capable of solving novel robot tasks, such as quadruped balancing and walking atop a yoga ball, without iterative manual design.

Body

• QUOTE: “Specifically, to generate the highest quality of reward functions, we build on Eureka, a state-of-the-art LLM-based reward design algorithm that can generate free-form, effective reward functions in code.”
  • (Ma, Liang et al., 2023) ⇒ Yecheng Jason Ma, William Liang, Guanzhi Wang, De-An Huang, Osbert Bastani, Dinesh Jayaraman, Yuke Zhu, Linxi Fan, and Anima Anandkumar. (2023). “Eureka: Human-level Reward Design via Coding Large Language Models.” In: arXiv.
    • NOTE: This paper is indeed published and available on arXiv. It explores the application of large language models (LLMs) for automated reward design in robotics, demonstrating the capabilities of LLMs in generating and refining reward functions with minimal human input. This reference is confirmed by its listing and discussion in multiple academic and research platforms​ (ar5iv)​​ (GitHub)​.
• QUOTE: “DrEureka decomposes the optimization into three stages: an LLM first synthesizes reward functions, then an initial policy is rolled out in perturbed simulations to create a suitable sampling range for physics parameters, which is finally used by the LLM to generate valid domain randomization configurations.”
  • (Andrychowicz et al., 2020) ⇒ Marcin Andrychowicz, Bowen Baker, Maciek Chociej, Rafal Jozefowicz, Bob McGrew, Jakub Pachocki, Arthur Petron, Matthias Plappert, Glenn Powell, Alex Ray, et al. (2020). “Learning dexterous in-hand manipulation.” In: The International Journal of Robotics Research, 39(1):3–20.
    • NOTE: this seminal paper discusses the challenges and advancements in dexterous in-hand robotic manipulation. It is widely cited in the robotics community for its insights into the complexities of adapting simulation-trained models to real-world tasks​ (ar5iv)​.
• QUOTE: “Our experiments primarily focus on quadruped locomotion and dexterous manipulation because reward design, domain randomization, and sim-to-real reinforcement learning at large have already been established as critical components of effective policy learning strategies within these domains.”
  • (Handa et al., 2023) ⇒ Ankur Handa, Arthur Allshire, Viktor Makoviychuk, Aleksei Petrenko, Ritvik Singh, Jingzhou Liu, Denys Makoviichuk, Karl Van Wyk, Alexander Zhurkevich, Balakumar Sundaralingam, et al. (2023). “Dextreme: Transfer of agile in-hand manipulation from simulation to reality.” In: Proceedings of the 2023 IEEE International Conference on Robotics and Automation (ICRA), pages 5977–5984. IEEE.
    • NOTE: This conference paper, presented at the 2023 IEEE International Conference on Robotics and Automation (ICRA), focuses on transferring simulated dexterity into real-world applications. It is particularly relevant for its discussion on domain randomization and sim-to-real reinforcement learning, crucial components in the development of robotic manipulation technologies​ (GitHub)​.

References

;

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2024 DrEurekaLanguageModelGuidedSimT	Yuke Zhu Jason Ma William Liang Hungju Wang Sam Wang Jim Fan Osbert Bastani Dinesh Jayaraman			DrEureka: Language Model Guided Sim-To-Real Transfer						2024