Simulation provides two specific benefits for robotics that real-world data alone cannot: systematic reliability through controlled coverage of all state-space variations, and efficiency through faster-than-human data generation.
Yunzhu Li explains that simulation enables systematic randomization of lighting, friction, geometry, and other physical parameters to ensure sufficient coverage of the state space for robust robot performance. It also enables faster-than-human data collection by speeding up robot behaviors in simulation, which is impossible in the real world due to physics constraints. ✦ AI generated
Yunzhu Li · a16z Podcast · 2026-07-28 · original ↗
starts at this moment · 21:40
If you put things more specific, simulation can provide two levels of benefits. The first one is reliability and the second one is efficiency. For reliability, if you're thinking about a robotic system working reliably in the real environments, you need data to provide systematic coverage of all the state space and the variations that robots might encounter. That's how you can learn to be robust. So with simulation, you can do systematic randomizations and control over the variations of lighting, frictions, geometries, object types and also all different kind of physical parameters to make sure you have sufficient coverage of the state space. Second is about efficiency. Right now many people are doing teleoperation — you're collecting the data at a speed that is actually slower than humans actually doing the task. But for many of our clients, human speed is not good enough. They want faster than human speeds. For the robot to move faster, it's not as simple as just drive the robot faster because the gravity doesn't change. But in simulation, you can do systematic speed up of the robots behaviors to train the robots such that it considers all the dynamics changes of the environments.
verbatim transcript · starts at 21:40
21:43environments, you need data to provide systematic coverage of all the state space and the variations that robots might encounter. That's how you can learn of how that is robust. So with simulation, you can do systematic randomizations and control and the variations of lighting, frictions, geometries, object types and also all different kind of physical parameters to making sure you have sufficient coverage of the state space. So this is what can
22:08give the robotic systems reliability and second is about efficiency. So right now many people are doing tele operation and if you look at many of the television device imagining all the actual skeletons you are using you're actually collecting the data at a speed that is actually slower than human actually doing the task. >> But for many of our clients human speed to them is not good enough. They want
22:31faster than human speeds. Yeah. >> So for the robot to like move faster, it's not as simple as just drive the robot faster because the gravity doesn't change. >> But in simulation, you can do systematic speed up of the robots behaviors to train the robots such that it considers all the dynamics changes of the environments. So this is what's can give like our clients for them efficiency. So
22:54both for the reliability and efficiency though there are some kind of like a very unique like values where simulation can provide. You've talked about the technology and the platform, what it does. Maybe talk about the specific use cases people use it for. >> There are essential like two specific use cases especially around both training and also around evaluations. >> Okay. >> Um starting from the evaluations. >> So evaluation is something like people
23:18often overlooked in the robotics >> but if you are tuning like robotic models you have to know how well it works and that is the only source of information for you to iterate. >> Yeah. By the way, a lot of a lot every every AI person really understands what eval are and uses it all the time. NonAI people, it often means something a little different. So maybe it's even