{"paper":{"title":"Rectified Schr\\\"odinger Bridge Matching for Few-Step Visual Navigation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"A single velocity network works across all regularization strengths in Schrödinger Bridge policies, enabling 3-step visual navigation at 92% success.","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Junhui Li, Rui Ma, Tieru Wu, Weiguang Zhao, Wenjian Zhang, Wuyang Luan","submitted_at":"2026-04-07T10:22:27Z","abstract_excerpt":"Visual navigation is a core challenge in Embodied AI, requiring autonomous agents to translate high-dimensional sensory observations into continuous, long-horizon action trajectories. While generative policies based on diffusion models and Schr\\\"odinger Bridges (SB) effectively capture multimodal action distributions, they require dozens of integration steps due to high-variance stochastic transport, posing a critical barrier for real-time robotic control. We propose Rectified Schr\\\"odinger Bridge Matching (RSBM), a framework that exploits a shared velocity-field structure between standard Sch"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"We prove two key results: (1) the conditional velocity field's functional form is invariant across the entire ε-spectrum (Velocity Structure Invariance), enabling a single network to serve all regularization strengths; and (2) reducing ε linearly decreases the conditional velocity variance, enabling more stable coarse-step ODE integration. Empirically, RSBM achieves over 94% cosine similarity and 92% success rate in merely 3 integration steps -- without distillation or multi-stage training.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The assumption that a learned conditional prior reliably shortens transport distance and that the velocity structure invariance holds in practice for high-dimensional visual navigation observations without requiring additional training or post-hoc adjustments.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"RSBM exploits velocity field invariance across regularization levels to achieve over 94% cosine similarity and 92% success in visual navigation using only 3 integration steps.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A single velocity network works across all regularization strengths in Schrödinger Bridge policies, enabling 3-step visual navigation at 92% success.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"e6d8dfa33b632b9adffbd44da9062e184fa8258080244a4a5a880c1115d36831"},"source":{"id":"2604.05673","kind":"arxiv","version":3},"verdict":{"id":"bc6e2d07-4020-4b3d-b4dd-2e3f14c01a1b","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T19:20:57.485599Z","strongest_claim":"We prove two key results: (1) the conditional velocity field's functional form is invariant across the entire ε-spectrum (Velocity Structure Invariance), enabling a single network to serve all regularization strengths; and (2) reducing ε linearly decreases the conditional velocity variance, enabling more stable coarse-step ODE integration. Empirically, RSBM achieves over 94% cosine similarity and 92% success rate in merely 3 integration steps -- without distillation or multi-stage training.","one_line_summary":"RSBM exploits velocity field invariance across regularization levels to achieve over 94% cosine similarity and 92% success in visual navigation using only 3 integration steps.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The assumption that a learned conditional prior reliably shortens transport distance and that the velocity structure invariance holds in practice for high-dimensional visual navigation observations without requiring additional training or post-hoc adjustments.","pith_extraction_headline":"A single velocity network works across all regularization strengths in Schrödinger Bridge policies, enabling 3-step visual navigation at 92% success."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.05673/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":3,"snapshot_sha256":"44422574f46cd2a18bff46362a9dbbb56397eb9b78c6715daea0e0065538384d"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}