By separating semantic planning from physical execution and using failure evidence to guide targeted capability improvements, robots can achieve 4x better performance on long-horizon manipulation tasks compared to frozen policies.
DynaHarness is a system that improves robot manipulation by coupling semantic reasoning with physical execution monitoring. It uses a two-level architecture where a 'slow brain' plans high-level actions and a 'fast brain' grounds and monitors execution, refusing unsafe actions and requesting replans when needed. The system learns from failures to improve reusable capabilities.