[2605.08511] Trajectory-Consistent Flow Matching for Robust Visuomotor Policy Learning
[Submitted on 8 May 2026]
Title:Trajectory-Consistent Flow Matching for Robust Visuomotor Policy Learning
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Abstract:Flow matching policies learn continuous velocity fields that transport noise to actions, enabling fast deterministic inference for robot manipulation. However, standard training optimizes a pointwise velocity objective while inference requires numerical integration of that field -- a mismatch that causes compounding trajectory errors. We propose four complementary remedies: (1) auxiliary rectified flow velocity regression that provides uniform temporal supervision across the full time interval; (2) multi-step trajectory consistency training that supervises the integrated displacement of the velocity field over trajectory segments, directly closing the train-inference gap; (3) velocity field regularization that enforces temporal smoothness, preventing oscillations that destabilize integration; and (4) fourth-order Runge-Kutta (RK4) inference that reduces global discretization error by orders of magnitude over Euler methods. Critically, these components are not independently sufficient -- RK4 without a smooth velocity field fails, and smoothness without trajectory-level supervision still drifts, as our ablation study confirms. We further pair these with a dual-view 3D point cloud encoder using two independent PointNet encoders for complementary spatial perception. On four real-robot tasks across a Franka arm and a Boston Dynamics Spot, our method achieves 70% and 60% overall success on two long-horizon multi-phase tasks where both baselines score 0%, and reaches 100% on precision tool placement. Three MetaWorld simulation tasks confirm consistent improvements, validating that trajectory-level supervision is essential for reliable policy execution.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2605.08511 [cs.RO]
(or arXiv:2605.08511v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.08511
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Riad Ahmed [view email] [v1] Fri, 8 May 2026 21:44:10 UTC (17,803 KB)
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