{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:3CJYGBH4VTN3P6WWPUS6ZQMR6K","short_pith_number":"pith:3CJYGBH4","schema_version":"1.0","canonical_sha256":"d8938304fcacdbb7fad67d25ecc191f29e5509bd1a09a2ad78c025db7e57be23","source":{"kind":"arxiv","id":"2407.16081","version":2},"attestation_state":"computed","paper":{"title":"PECAN: Personalizing Robot Behaviors through a Learned Canonical Space","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Dylan P. Losey, Heramb Nemlekar, Robert Ramirez Sanchez","submitted_at":"2024-07-22T22:59:26Z","abstract_excerpt":"Robots should personalize how they perform tasks to match the needs of individual human users. Today's robot achieve this personalization by asking for the human's feedback in the task space. For example, an autonomous car might show the human two different ways to decelerate at stoplights, and ask the human which of these motions they prefer. This current approach to personalization is indirect: based on the behaviors the human selects (e.g., decelerating slowly), the robot tries to infer their underlying preference (e.g., defensive driving). By contrast, our paper develops a learning and int"},"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":"2407.16081","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-07-22T22:59:26Z","cross_cats_sorted":[],"title_canon_sha256":"0641f3cdfa8388d0e4758cdb966c603264a39c9595d47ad70eee9dcf75017228","abstract_canon_sha256":"34e8b893d60f08b77b9bdfedb9b32b77500ac2c4eae497c72ed011e93f7fb65e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:09:42.888152Z","signature_b64":"jeZfZI6leMYFUWRMsfZVAyZ7HZMtI3RyyRlcyhFpAhI5Vj30GnEg1GQt2BLbf7Eph9jB8fRHco4DgyqSy5ULDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d8938304fcacdbb7fad67d25ecc191f29e5509bd1a09a2ad78c025db7e57be23","last_reissued_at":"2026-07-05T11:09:42.887665Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:09:42.887665Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PECAN: Personalizing Robot Behaviors through a Learned Canonical Space","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Dylan P. Losey, Heramb Nemlekar, Robert Ramirez Sanchez","submitted_at":"2024-07-22T22:59:26Z","abstract_excerpt":"Robots should personalize how they perform tasks to match the needs of individual human users. Today's robot achieve this personalization by asking for the human's feedback in the task space. For example, an autonomous car might show the human two different ways to decelerate at stoplights, and ask the human which of these motions they prefer. This current approach to personalization is indirect: based on the behaviors the human selects (e.g., decelerating slowly), the robot tries to infer their underlying preference (e.g., defensive driving). By contrast, our paper develops a learning and int"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.16081","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2407.16081/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":"2407.16081","created_at":"2026-07-05T11:09:42.887723+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.16081v2","created_at":"2026-07-05T11:09:42.887723+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.16081","created_at":"2026-07-05T11:09:42.887723+00:00"},{"alias_kind":"pith_short_12","alias_value":"3CJYGBH4VTN3","created_at":"2026-07-05T11:09:42.887723+00:00"},{"alias_kind":"pith_short_16","alias_value":"3CJYGBH4VTN3P6WW","created_at":"2026-07-05T11:09:42.887723+00:00"},{"alias_kind":"pith_short_8","alias_value":"3CJYGBH4","created_at":"2026-07-05T11:09:42.887723+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.04737","citing_title":"Imitation Learning Based on Disentangled Representation Learning of Behavioral Characteristics","ref_index":40,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3CJYGBH4VTN3P6WWPUS6ZQMR6K","json":"https://pith.science/pith/3CJYGBH4VTN3P6WWPUS6ZQMR6K.json","graph_json":"https://pith.science/api/pith-number/3CJYGBH4VTN3P6WWPUS6ZQMR6K/graph.json","events_json":"https://pith.science/api/pith-number/3CJYGBH4VTN3P6WWPUS6ZQMR6K/events.json","paper":"https://pith.science/paper/3CJYGBH4"},"agent_actions":{"view_html":"https://pith.science/pith/3CJYGBH4VTN3P6WWPUS6ZQMR6K","download_json":"https://pith.science/pith/3CJYGBH4VTN3P6WWPUS6ZQMR6K.json","view_paper":"https://pith.science/paper/3CJYGBH4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.16081&json=true","fetch_graph":"https://pith.science/api/pith-number/3CJYGBH4VTN3P6WWPUS6ZQMR6K/graph.json","fetch_events":"https://pith.science/api/pith-number/3CJYGBH4VTN3P6WWPUS6ZQMR6K/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3CJYGBH4VTN3P6WWPUS6ZQMR6K/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3CJYGBH4VTN3P6WWPUS6ZQMR6K/action/storage_attestation","attest_author":"https://pith.science/pith/3CJYGBH4VTN3P6WWPUS6ZQMR6K/action/author_attestation","sign_citation":"https://pith.science/pith/3CJYGBH4VTN3P6WWPUS6ZQMR6K/action/citation_signature","submit_replication":"https://pith.science/pith/3CJYGBH4VTN3P6WWPUS6ZQMR6K/action/replication_record"}},"created_at":"2026-07-05T11:09:42.887723+00:00","updated_at":"2026-07-05T11:09:42.887723+00:00"}