Ep#116: SimEX: Simulation-Integrated Robotics AutoResearch

Coding agents powered by large language models are increasingly able to complete complex, long-horizon goals, but this capability is still largely relegated to computer tasks. Bringing agents to real-world robotics is much harder.Enter SimEX, by Jiaheng Hu, Zhenyu Jiang, and company. It is a simulation-based auto-research framework, wherein agents can develop skills which can be used to control real robots, developing a robust toolbox of skills which can then be used for real-world tasks like towel folding and plate manipulation.To learn more, watch Episode 116 of RoboPapers with Michael Cho and Jiafei Duan!AbstractCoding agents powered by large language models (LLMs) have shown remarkable abilities to autonomously reason about and achieve goals in the digital world. However, bringing this success to the physical world remains challenging. On the one hand, direct generation methods (e.g., Code as Policies) often suffer from the LLMs' insufficient understanding of robots and physical environments. On the other hand, iterative trial-and-error tuning in the physical world (e.g., physical autoresearch) induces significant experimental cost and safety concerns. We introduce SimEX: Simulation-Integrated Robotics AutoResearch, an autoresearch framework that tightly integrates simulated experimentation, enabling coding agents to efficiently acquire physical capabilities for controlling real robots. SimEX operates in two stages. First, the agent conducts open-ended probe-and-optimize iterations in simulation, developing a robot toolbox with robust and generalizable capabilities. Second, the agent adapts the toolbox and the simulator together through only a few physical trials: each trial corrects the simulator, and the corrected simulator is used to diagnose failures and screen candidate repairs. We evaluate SimEX extensively in sim-to-sim settings and on physical robots. On challenging real-world manipulation tasks including towel folding, barcode scanning, and plate manipulation, SimEX enables coding agents to efficiently acquire robot skills without any demonstration and with only 10 minutes of real-robot interaction. These results suggest that simulation can be a critical component in achieving physical intelligence, not only as a source of training data that must closely replicate the real world, but also as a roughly correct laboratory where a coding agent develops the knowledge and procedures needed to act on the robot. More details and robot videos at this https URLLearn MoreProject Page: https://robo-simex.github.io/ArXiV: https://arxiv.org/abs/2609.38982TranscriptEdited for clarity and readability.Introducing SimXMichael Cho: Welcome to another episode of RoboPapers. Today we have Jiaheng Hu and Zhenyu Jiang, two of the coauthors of SimX: Simulation-Integrated Robotics Auto-Research. Thank you for making the time. Could you each share a little about yourselves?Jiaheng Hu: Thank you for inviting us. I’m a final-year PhD student at UT Austin, co-advised by Professor Peter Stone and Professor Roberto Martín-Martín. My research has always focused on building self-improving robots that can continually learn and adapt in the real world.Recently, I’ve become more interested in how foundation models, such as VLAs and coding agents, can facilitate that process. During my PhD, I’ve interned at the Allen Institute for AI, Google DeepMind, and Amazon FAR. The project we’re discussing today was my internship project at Amazon.Zhenyu Jiang: I’m a research scientist at Amazon FAR. I graduated from UT Austin last year, where I worked with Yuke Zhu. I now work on manipulation. My focus is straightforward: how do we make robotics work in the real world? Whatever makes it work, I’ll try.Bringing trial and error to roboticsJiaheng Hu: One of the biggest motivations for SimX is the recent success of agentic AI. Coding agents are a primary example, but there are also applications such as AlphaEvolve, where agents discover new algorithms.These agents are powerful partly because the LLMs behind them are smart, but also because they can perform trial and error in the digital world. They implement something, see whether it works, and improve it based on feedback from the environment.As roboticists, we want to know whether we can bring this kind of agentic AI to robots. Previous work falls into two main categories.The first is direct generation. A representative approach is Code as Policies: you tell a coding agent that you w