{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:46QSYMRHDSH33J3RWWYORJ2MJZ","short_pith_number":"pith:46QSYMRH","schema_version":"1.0","canonical_sha256":"e7a12c32271c8fbda771b5b0e8a74c4e7515c1e497a39a7dcdda54b24973abbe","source":{"kind":"arxiv","id":"1712.08521","version":2},"attestation_state":"computed","paper":{"title":"An Incremental Self-Organizing Architecture for Sensorimotor Learning and Prediction","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.NE","cs.RO"],"primary_cat":"cs.CV","authors_text":"German I. Parisi, Luiza Mici, Stefan Wermter","submitted_at":"2017-12-22T15:34:19Z","abstract_excerpt":"During visuomotor tasks, robots must compensate for temporal delays inherent in their sensorimotor processing systems. Delay compensation becomes crucial in a dynamic environment where the visual input is constantly changing, e.g., during the interacting with a human demonstrator. For this purpose, the robot must be equipped with a prediction mechanism for using the acquired perceptual experience to estimate possible future motor commands. In this paper, we present a novel neural network architecture that learns prototypical visuomotor representations and provides reliable predictions on the b"},"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":"1712.08521","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2017-12-22T15:34:19Z","cross_cats_sorted":["cs.NE","cs.RO"],"title_canon_sha256":"cc68424542be9f9ea721c1611a4d6540a705c008b6bcd4f5e7e4e5da2206ad51","abstract_canon_sha256":"3d4180f9f1f491e4a878e3ad93451e6e44218f1e8e0f35e80bb92070caf1e632"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:21:40.440287Z","signature_b64":"neNyEyKNUNeoRvsoZ/dd0Cc8NGvtxHaGyW7VjtaMA5Osz2gQgLEBjRueTYhGB/f9nlOxaOEhHCAE6zkhQHmwAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e7a12c32271c8fbda771b5b0e8a74c4e7515c1e497a39a7dcdda54b24973abbe","last_reissued_at":"2026-05-18T00:21:40.439635Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:21:40.439635Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Incremental Self-Organizing Architecture for Sensorimotor Learning and Prediction","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.NE","cs.RO"],"primary_cat":"cs.CV","authors_text":"German I. Parisi, Luiza Mici, Stefan Wermter","submitted_at":"2017-12-22T15:34:19Z","abstract_excerpt":"During visuomotor tasks, robots must compensate for temporal delays inherent in their sensorimotor processing systems. Delay compensation becomes crucial in a dynamic environment where the visual input is constantly changing, e.g., during the interacting with a human demonstrator. For this purpose, the robot must be equipped with a prediction mechanism for using the acquired perceptual experience to estimate possible future motor commands. In this paper, we present a novel neural network architecture that learns prototypical visuomotor representations and provides reliable predictions on the b"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.08521","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1712.08521","created_at":"2026-05-18T00:21:40.439712+00:00"},{"alias_kind":"arxiv_version","alias_value":"1712.08521v2","created_at":"2026-05-18T00:21:40.439712+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.08521","created_at":"2026-05-18T00:21:40.439712+00:00"},{"alias_kind":"pith_short_12","alias_value":"46QSYMRHDSH3","created_at":"2026-05-18T12:30:58.224056+00:00"},{"alias_kind":"pith_short_16","alias_value":"46QSYMRHDSH33J3R","created_at":"2026-05-18T12:30:58.224056+00:00"},{"alias_kind":"pith_short_8","alias_value":"46QSYMRH","created_at":"2026-05-18T12:30:58.224056+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/46QSYMRHDSH33J3RWWYORJ2MJZ","json":"https://pith.science/pith/46QSYMRHDSH33J3RWWYORJ2MJZ.json","graph_json":"https://pith.science/api/pith-number/46QSYMRHDSH33J3RWWYORJ2MJZ/graph.json","events_json":"https://pith.science/api/pith-number/46QSYMRHDSH33J3RWWYORJ2MJZ/events.json","paper":"https://pith.science/paper/46QSYMRH"},"agent_actions":{"view_html":"https://pith.science/pith/46QSYMRHDSH33J3RWWYORJ2MJZ","download_json":"https://pith.science/pith/46QSYMRHDSH33J3RWWYORJ2MJZ.json","view_paper":"https://pith.science/paper/46QSYMRH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1712.08521&json=true","fetch_graph":"https://pith.science/api/pith-number/46QSYMRHDSH33J3RWWYORJ2MJZ/graph.json","fetch_events":"https://pith.science/api/pith-number/46QSYMRHDSH33J3RWWYORJ2MJZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/46QSYMRHDSH33J3RWWYORJ2MJZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/46QSYMRHDSH33J3RWWYORJ2MJZ/action/storage_attestation","attest_author":"https://pith.science/pith/46QSYMRHDSH33J3RWWYORJ2MJZ/action/author_attestation","sign_citation":"https://pith.science/pith/46QSYMRHDSH33J3RWWYORJ2MJZ/action/citation_signature","submit_replication":"https://pith.science/pith/46QSYMRHDSH33J3RWWYORJ2MJZ/action/replication_record"}},"created_at":"2026-05-18T00:21:40.439712+00:00","updated_at":"2026-05-18T00:21:40.439712+00:00"}