{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:X5TJ7LVKBL7MQDVYGFYGLAAEMR","short_pith_number":"pith:X5TJ7LVK","schema_version":"1.0","canonical_sha256":"bf669faeaa0afec80eb831706580046442fc3132adce9f796d3c2b68feba61c8","source":{"kind":"arxiv","id":"1605.03261","version":1},"attestation_state":"computed","paper":{"title":"Sensorimotor Input as a Language Generalisation Tool: A Neurorobotics Model for Generation and Generalisation of Noun-Verb Combinations with Sensorimotor Inputs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.RO","authors_text":"Angelo Cangelosi, Junpei Zhong, Jun Tani, Martin Peniak, Tetsuya Ogata","submitted_at":"2016-05-11T02:31:21Z","abstract_excerpt":"The paper presents a neurorobotics cognitive model to explain the understanding and generalisation of nouns and verbs combinations when a vocal command consisting of a verb-noun sentence is provided to a humanoid robot. This generalisation process is done via the grounding process: different objects are being interacted, and associated, with different motor behaviours, following a learning approach inspired by developmental language acquisition in infants. This cognitive model is based on Multiple Time-scale Recurrent Neural Networks (MTRNN).With the data obtained from object manipulation task"},"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":"1605.03261","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2016-05-11T02:31:21Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"669d1340c55858be507cba54f4f21f863312b41218acb945a1e2dd3bcc1d14d5","abstract_canon_sha256":"ffd583316085130ddf4df9baf5b01968ebe75408f8b546083e31a1dd565ead23"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:15:05.846205Z","signature_b64":"BSeJ79ypw3A02svQRoNs98ri2cYISUwvDQx2oD/GoYAHJd0y4YXEwsCEpzmB8EAnvR+MgTLVpHOUar1QeNekAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bf669faeaa0afec80eb831706580046442fc3132adce9f796d3c2b68feba61c8","last_reissued_at":"2026-05-18T01:15:05.845739Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:15:05.845739Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sensorimotor Input as a Language Generalisation Tool: A Neurorobotics Model for Generation and Generalisation of Noun-Verb Combinations with Sensorimotor Inputs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.RO","authors_text":"Angelo Cangelosi, Junpei Zhong, Jun Tani, Martin Peniak, Tetsuya Ogata","submitted_at":"2016-05-11T02:31:21Z","abstract_excerpt":"The paper presents a neurorobotics cognitive model to explain the understanding and generalisation of nouns and verbs combinations when a vocal command consisting of a verb-noun sentence is provided to a humanoid robot. This generalisation process is done via the grounding process: different objects are being interacted, and associated, with different motor behaviours, following a learning approach inspired by developmental language acquisition in infants. This cognitive model is based on Multiple Time-scale Recurrent Neural Networks (MTRNN).With the data obtained from object manipulation task"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1605.03261","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":""},"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":"1605.03261","created_at":"2026-05-18T01:15:05.845816+00:00"},{"alias_kind":"arxiv_version","alias_value":"1605.03261v1","created_at":"2026-05-18T01:15:05.845816+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1605.03261","created_at":"2026-05-18T01:15:05.845816+00:00"},{"alias_kind":"pith_short_12","alias_value":"X5TJ7LVKBL7M","created_at":"2026-05-18T12:30:51.357362+00:00"},{"alias_kind":"pith_short_16","alias_value":"X5TJ7LVKBL7MQDVY","created_at":"2026-05-18T12:30:51.357362+00:00"},{"alias_kind":"pith_short_8","alias_value":"X5TJ7LVK","created_at":"2026-05-18T12:30:51.357362+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/X5TJ7LVKBL7MQDVYGFYGLAAEMR","json":"https://pith.science/pith/X5TJ7LVKBL7MQDVYGFYGLAAEMR.json","graph_json":"https://pith.science/api/pith-number/X5TJ7LVKBL7MQDVYGFYGLAAEMR/graph.json","events_json":"https://pith.science/api/pith-number/X5TJ7LVKBL7MQDVYGFYGLAAEMR/events.json","paper":"https://pith.science/paper/X5TJ7LVK"},"agent_actions":{"view_html":"https://pith.science/pith/X5TJ7LVKBL7MQDVYGFYGLAAEMR","download_json":"https://pith.science/pith/X5TJ7LVKBL7MQDVYGFYGLAAEMR.json","view_paper":"https://pith.science/paper/X5TJ7LVK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1605.03261&json=true","fetch_graph":"https://pith.science/api/pith-number/X5TJ7LVKBL7MQDVYGFYGLAAEMR/graph.json","fetch_events":"https://pith.science/api/pith-number/X5TJ7LVKBL7MQDVYGFYGLAAEMR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/X5TJ7LVKBL7MQDVYGFYGLAAEMR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/X5TJ7LVKBL7MQDVYGFYGLAAEMR/action/storage_attestation","attest_author":"https://pith.science/pith/X5TJ7LVKBL7MQDVYGFYGLAAEMR/action/author_attestation","sign_citation":"https://pith.science/pith/X5TJ7LVKBL7MQDVYGFYGLAAEMR/action/citation_signature","submit_replication":"https://pith.science/pith/X5TJ7LVKBL7MQDVYGFYGLAAEMR/action/replication_record"}},"created_at":"2026-05-18T01:15:05.845816+00:00","updated_at":"2026-05-18T01:15:05.845816+00:00"}