ODEAction proposes chunk-free action generation paradigm using ODE evolution instead of action-chunk prediction

ODEAction proposes a chunk-free action generation paradigm that replaces the action-chunk prediction common in VLA models with continuous action evolution. It characterizes the rate of change of the current action over evolution time using a transition field over action space; during execution, it takes the current action as the initial condition and recursively generates subsequent actions via ODE integration. The method is implemented as an action head and does not depend on a specific vision-language backbone architecture. Experiments on LIBERO, EBench, RoboTwin, and real robots show better task performance and trajectory quality than matched action-chunking baselines, with particularly notable gains on fine manipulation tasks.