{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:WAGSC4B46266LI2POGCNT3G2KI","short_pith_number":"pith:WAGSC4B4","schema_version":"1.0","canonical_sha256":"b00d21703cf6bde5a34f7184d9ecda5212f8a3dbfe22d86e6de512627971e9c8","source":{"kind":"arxiv","id":"2606.12334","version":1},"attestation_state":"computed","paper":{"title":"Fourier Features Let Agents Learn High Precision Policies with Imitation Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.LG","authors_text":"Bal\\'azs Gyenes, Emiliyan Gospodinov, Enrico Krohmer, Gerhard Neumann, Jan Frieling, Nicolas Schreiber, Niklas Freymuth, Xiaogang Jia","submitted_at":"2026-06-10T17:05:50Z","abstract_excerpt":"High-precision robotic manipulation requires fine-grained spatial reasoning that is often difficult to achieve with RGB-only policies due to depth ambiguity and perspective scale issues. Policies that leverage 3D information directly, such as those based on point clouds, offer a stronger geometric prior over purely image-based ones, yet their performance remains highly task-dependent. We hypothesize that this discrepancy may be due to the spectral bias of neural networks towards learning low frequency functions, which especially affects architectures conditioned on slow-moving Cartesian featur"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2606.12334","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-10T17:05:50Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"3c8f5bdcaaaa91bf1ca82c9225b3cccf96e29b33dad990b1a82754edd0c39082","abstract_canon_sha256":"494256fe77d29b58b35efb5f053d4272c3a5e105544645c600a8ce5497245471"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-11T01:11:02.354579Z","signature_b64":"6B6pWixvGrEz+6Hcu786r9oco8i32yV8P4UzOvXD6fQ7xL2xCZNANiJJwmn6lx3RQ2GTqLVS2zi0WJVRi21gCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b00d21703cf6bde5a34f7184d9ecda5212f8a3dbfe22d86e6de512627971e9c8","last_reissued_at":"2026-06-11T01:11:02.353780Z","signature_status":"signed_v1","first_computed_at":"2026-06-11T01:11:02.353780Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fourier Features Let Agents Learn High Precision Policies with Imitation Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.LG","authors_text":"Bal\\'azs Gyenes, Emiliyan Gospodinov, Enrico Krohmer, Gerhard Neumann, Jan Frieling, Nicolas Schreiber, Niklas Freymuth, Xiaogang Jia","submitted_at":"2026-06-10T17:05:50Z","abstract_excerpt":"High-precision robotic manipulation requires fine-grained spatial reasoning that is often difficult to achieve with RGB-only policies due to depth ambiguity and perspective scale issues. Policies that leverage 3D information directly, such as those based on point clouds, offer a stronger geometric prior over purely image-based ones, yet their performance remains highly task-dependent. We hypothesize that this discrepancy may be due to the spectral bias of neural networks towards learning low frequency functions, which especially affects architectures conditioned on slow-moving Cartesian featur"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.12334","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2606.12334/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":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2606.12334","created_at":"2026-06-11T01:11:02.353909+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.12334v1","created_at":"2026-06-11T01:11:02.353909+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.12334","created_at":"2026-06-11T01:11:02.353909+00:00"},{"alias_kind":"pith_short_12","alias_value":"WAGSC4B46266","created_at":"2026-06-11T01:11:02.353909+00:00"},{"alias_kind":"pith_short_16","alias_value":"WAGSC4B46266LI2P","created_at":"2026-06-11T01:11:02.353909+00:00"},{"alias_kind":"pith_short_8","alias_value":"WAGSC4B4","created_at":"2026-06-11T01:11:02.353909+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WAGSC4B46266LI2POGCNT3G2KI","json":"https://pith.science/pith/WAGSC4B46266LI2POGCNT3G2KI.json","graph_json":"https://pith.science/api/pith-number/WAGSC4B46266LI2POGCNT3G2KI/graph.json","events_json":"https://pith.science/api/pith-number/WAGSC4B46266LI2POGCNT3G2KI/events.json","paper":"https://pith.science/paper/WAGSC4B4"},"agent_actions":{"view_html":"https://pith.science/pith/WAGSC4B46266LI2POGCNT3G2KI","download_json":"https://pith.science/pith/WAGSC4B46266LI2POGCNT3G2KI.json","view_paper":"https://pith.science/paper/WAGSC4B4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.12334&json=true","fetch_graph":"https://pith.science/api/pith-number/WAGSC4B46266LI2POGCNT3G2KI/graph.json","fetch_events":"https://pith.science/api/pith-number/WAGSC4B46266LI2POGCNT3G2KI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WAGSC4B46266LI2POGCNT3G2KI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WAGSC4B46266LI2POGCNT3G2KI/action/storage_attestation","attest_author":"https://pith.science/pith/WAGSC4B46266LI2POGCNT3G2KI/action/author_attestation","sign_citation":"https://pith.science/pith/WAGSC4B46266LI2POGCNT3G2KI/action/citation_signature","submit_replication":"https://pith.science/pith/WAGSC4B46266LI2POGCNT3G2KI/action/replication_record"}},"created_at":"2026-06-11T01:11:02.353909+00:00","updated_at":"2026-06-11T01:11:02.353909+00:00"}