PathTime-VLA: Path-Time Decoupling for Factorized Post-Training of Vision-Language-Action Policies

arXiv:2610.11771v1 Announce Type: new Abstract: Vision-Language-Action (VLA) policies typically predict actions at fixed time intervals, coupling the route a robot follows with its execution pace. This coupling complicates adaptation from teleoperation: useful geometric guidance comes with timing shaped by interface delays and operator behavior. Our key insight is to bring the path-time parameterization of classical motion planning into the learned action representation of a VLA. We introduce PathTime-VLA, which represents motion as a progress-indexed interaction path X(s) and a positive interval-time profile. The latter defines a monotone time law t(s), yielding controller commands X(s(t)). For a given path, alternative executions are expressed through the time profile, allowing chunk-wise speed choices without changing the geometric prediction target. This representation supports a staged post-training procedure: demonstrations and DAgger interventions establish a target-domain prior, Speed-DQN learns execution multipliers from robot interaction, and Path-AWR uses rollout outcomes to refine the diffusion path generator. A path-conditioned action expert realizes the resulting motions while maintaining distinct learning interfaces for path generation and execution timing. Across three tasks, the complete method achieves 58/60 successes versus 57/60 for PathTime-VLA under BC + DAgger at fixed 1imes, with approximately 39-52% shorter mean completion times over successful trials.

Source: arXiv cs.RO | 2026-10-09