LaMP: Learning Vision-Language-Action Policies with 3D Scene Flow as Latent Motion Prior

We introduce LaMP, a dual-expert Vision-Language-Actionframework that embeds dense 3D scene flow as a latent motion prior forrobotic manipulation. Existing VLA models regress actions directly from2D semantic visual features, forcing them to learn complex 3D physicalinteractions implicitly. This implicit learning strategy degrades underunfamiliar spatial dynamics. LaMP addresses this limitation by aligninga flow-matching Motion Expert with a policy-predicting Action Expertthrough gated cross-attention. Specifically, the Motion Expert generatesa one-step partially denoised 3D scene flow, and its hidden states condi-tion the Action Expert without full multi-step reconstruction. We eval-uate LaMP on the LIBERO, LIBERO-Plus, and SimplerEnv-WidowXsimulation benchmarks as well as real-world experiments. LaMP consis-tently outperforms evaluated VLA baselines across LIBERO, LIBERO-Plus, and SimplerEnv-WidowX benchmarks, achieving the highest re-ported average success rates under the same training budgets. On LIBERO-Plus OOD perturbations, LaMP shows improved robustness with an av-erage 9.7% gain over the strongest prior baseline. Our project page isavailable at https://summerwxk.github.io/lamp-project-page/.