AI agents use simulation to solve tough robotics problems with code, not data

At the 2026 Conference on Robot Learning, UC Berkeley and NVIDIA announced "agentic robotics," an approach in which LLM-based coding agents decompose tasks like grocery packing into sub-tasks, write and iterate code in simulation, and even tune simulation parameters to match real-world video. UC Berkeley testing reached success rates up to 99 percent, matching human hand-engineered performance, versus 20 percent for baseline imitation learning, and NVIDIA's Spencer Huang said retasking a robot at Wistron drops from at least 8 hours to a minute or two. Intrinsic's Torsten Kroeger cautioned that no physics engine accurately models physical contact and raised alignment concerns about trusting agents.