{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:GXGNDN5WJFSHVIWUOUGZPDNMMB","short_pith_number":"pith:GXGNDN5W","schema_version":"1.0","canonical_sha256":"35ccd1b7b649647aa2d4750d978dac606f992298833b48f9cdfc12d4cd1b3ae9","source":{"kind":"arxiv","id":"1908.10398","version":1},"attestation_state":"computed","paper":{"title":"A Data-Efficient Deep Learning Approach for Deployable Multimodal Social Robots","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.RO"],"primary_cat":"cs.AI","authors_text":"Heriberto Cuay\\'ahuitl","submitted_at":"2019-08-27T18:30:49Z","abstract_excerpt":"The deep supervised and reinforcement learning paradigms (among others) have the potential to endow interactive multimodal social robots with the ability of acquiring skills autonomously. But it is still not very clear yet how they can be best deployed in real world applications. As a step in this direction, we propose a deep learning-based approach for efficiently training a humanoid robot to play multimodal games---and use the game of `Noughts & Crosses' with two variants as a case study. Its minimum requirements for learning to perceive and interact are based on a few hundred example images"},"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":"1908.10398","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2019-08-27T18:30:49Z","cross_cats_sorted":["cs.LG","cs.RO"],"title_canon_sha256":"0f6bd4be85144cffd029ed209dcbfb1e7eae160dac9fad46ea93959c7d22cf7d","abstract_canon_sha256":"492012ab77114040164b40a3bac9925e68d493fd8c5d0a79793fb9c10426b4cb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:00:16.824221Z","signature_b64":"+vQemsbOiiW6vy7qGy8hHeOEePy7mnX6vplQtsuOOZHj+TIXj1LBIVC2seoJoHXYZ01Pwk2YOAZ7vpzu+zdGCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"35ccd1b7b649647aa2d4750d978dac606f992298833b48f9cdfc12d4cd1b3ae9","last_reissued_at":"2026-07-05T00:00:16.823754Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:00:16.823754Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Data-Efficient Deep Learning Approach for Deployable Multimodal Social Robots","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.RO"],"primary_cat":"cs.AI","authors_text":"Heriberto Cuay\\'ahuitl","submitted_at":"2019-08-27T18:30:49Z","abstract_excerpt":"The deep supervised and reinforcement learning paradigms (among others) have the potential to endow interactive multimodal social robots with the ability of acquiring skills autonomously. But it is still not very clear yet how they can be best deployed in real world applications. As a step in this direction, we propose a deep learning-based approach for efficiently training a humanoid robot to play multimodal games---and use the game of `Noughts & Crosses' with two variants as a case study. Its minimum requirements for learning to perceive and interact are based on a few hundred example images"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.10398","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/1908.10398/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":"1908.10398","created_at":"2026-07-05T00:00:16.823814+00:00"},{"alias_kind":"arxiv_version","alias_value":"1908.10398v1","created_at":"2026-07-05T00:00:16.823814+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.10398","created_at":"2026-07-05T00:00:16.823814+00:00"},{"alias_kind":"pith_short_12","alias_value":"GXGNDN5WJFSH","created_at":"2026-07-05T00:00:16.823814+00:00"},{"alias_kind":"pith_short_16","alias_value":"GXGNDN5WJFSHVIWU","created_at":"2026-07-05T00:00:16.823814+00:00"},{"alias_kind":"pith_short_8","alias_value":"GXGNDN5W","created_at":"2026-07-05T00:00:16.823814+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/GXGNDN5WJFSHVIWUOUGZPDNMMB","json":"https://pith.science/pith/GXGNDN5WJFSHVIWUOUGZPDNMMB.json","graph_json":"https://pith.science/api/pith-number/GXGNDN5WJFSHVIWUOUGZPDNMMB/graph.json","events_json":"https://pith.science/api/pith-number/GXGNDN5WJFSHVIWUOUGZPDNMMB/events.json","paper":"https://pith.science/paper/GXGNDN5W"},"agent_actions":{"view_html":"https://pith.science/pith/GXGNDN5WJFSHVIWUOUGZPDNMMB","download_json":"https://pith.science/pith/GXGNDN5WJFSHVIWUOUGZPDNMMB.json","view_paper":"https://pith.science/paper/GXGNDN5W","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1908.10398&json=true","fetch_graph":"https://pith.science/api/pith-number/GXGNDN5WJFSHVIWUOUGZPDNMMB/graph.json","fetch_events":"https://pith.science/api/pith-number/GXGNDN5WJFSHVIWUOUGZPDNMMB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GXGNDN5WJFSHVIWUOUGZPDNMMB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GXGNDN5WJFSHVIWUOUGZPDNMMB/action/storage_attestation","attest_author":"https://pith.science/pith/GXGNDN5WJFSHVIWUOUGZPDNMMB/action/author_attestation","sign_citation":"https://pith.science/pith/GXGNDN5WJFSHVIWUOUGZPDNMMB/action/citation_signature","submit_replication":"https://pith.science/pith/GXGNDN5WJFSHVIWUOUGZPDNMMB/action/replication_record"}},"created_at":"2026-07-05T00:00:16.823814+00:00","updated_at":"2026-07-05T00:00:16.823814+00:00"}