{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:QTTRUUXCQNB7JLTMT7FIANCHPJ","short_pith_number":"pith:QTTRUUXC","schema_version":"1.0","canonical_sha256":"84e71a52e28343f4ae6c9fca8034477a4f497c618b5b7b0e9ea679f7a055b914","source":{"kind":"arxiv","id":"2208.01605","version":1},"attestation_state":"computed","paper":{"title":"Learning Skill-based Industrial Robot Tasks with User Priors","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Carl Hvarfner, Konstantinos Chatzilygeroudis, Luigi Nardi, Matthias Mayr, Volker Krueger","submitted_at":"2022-08-02T17:20:39Z","abstract_excerpt":"Robot skills systems are meant to reduce robot setup time for new manufacturing tasks. Yet, for dexterous, contact-rich tasks, it is often difficult to find the right skill parameters. One strategy is to learn these parameters by allowing the robot system to learn directly on the task. For a learning problem, a robot operator can typically specify the type and range of values of the parameters. Nevertheless, given their prior experience, robot operators should be able to help the learning process further by providing educated guesses about where in the parameter space potential optimal solutio"},"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":"2208.01605","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2022-08-02T17:20:39Z","cross_cats_sorted":[],"title_canon_sha256":"d328e3bf4d85e22c5e1377752f54e86899c45d4e75f142821e639524af33061b","abstract_canon_sha256":"704f5653301c2968335e1deb92f83b8ac382fb0d79a053316f25e94e7c3f3ce1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:45:35.330288Z","signature_b64":"7ZWtYk0KPPH2qtCDssXMCnjdiSVo+T8TiHicIBC+B1GBE26tp9VWzKsifZ55w70mQB9PemPoVDRUZM+9PBuGDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"84e71a52e28343f4ae6c9fca8034477a4f497c618b5b7b0e9ea679f7a055b914","last_reissued_at":"2026-07-05T04:45:35.329863Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:45:35.329863Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Skill-based Industrial Robot Tasks with User Priors","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Carl Hvarfner, Konstantinos Chatzilygeroudis, Luigi Nardi, Matthias Mayr, Volker Krueger","submitted_at":"2022-08-02T17:20:39Z","abstract_excerpt":"Robot skills systems are meant to reduce robot setup time for new manufacturing tasks. Yet, for dexterous, contact-rich tasks, it is often difficult to find the right skill parameters. One strategy is to learn these parameters by allowing the robot system to learn directly on the task. For a learning problem, a robot operator can typically specify the type and range of values of the parameters. Nevertheless, given their prior experience, robot operators should be able to help the learning process further by providing educated guesses about where in the parameter space potential optimal solutio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.01605","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/2208.01605/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":"2208.01605","created_at":"2026-07-05T04:45:35.329913+00:00"},{"alias_kind":"arxiv_version","alias_value":"2208.01605v1","created_at":"2026-07-05T04:45:35.329913+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.01605","created_at":"2026-07-05T04:45:35.329913+00:00"},{"alias_kind":"pith_short_12","alias_value":"QTTRUUXCQNB7","created_at":"2026-07-05T04:45:35.329913+00:00"},{"alias_kind":"pith_short_16","alias_value":"QTTRUUXCQNB7JLTM","created_at":"2026-07-05T04:45:35.329913+00:00"},{"alias_kind":"pith_short_8","alias_value":"QTTRUUXC","created_at":"2026-07-05T04:45:35.329913+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/QTTRUUXCQNB7JLTMT7FIANCHPJ","json":"https://pith.science/pith/QTTRUUXCQNB7JLTMT7FIANCHPJ.json","graph_json":"https://pith.science/api/pith-number/QTTRUUXCQNB7JLTMT7FIANCHPJ/graph.json","events_json":"https://pith.science/api/pith-number/QTTRUUXCQNB7JLTMT7FIANCHPJ/events.json","paper":"https://pith.science/paper/QTTRUUXC"},"agent_actions":{"view_html":"https://pith.science/pith/QTTRUUXCQNB7JLTMT7FIANCHPJ","download_json":"https://pith.science/pith/QTTRUUXCQNB7JLTMT7FIANCHPJ.json","view_paper":"https://pith.science/paper/QTTRUUXC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2208.01605&json=true","fetch_graph":"https://pith.science/api/pith-number/QTTRUUXCQNB7JLTMT7FIANCHPJ/graph.json","fetch_events":"https://pith.science/api/pith-number/QTTRUUXCQNB7JLTMT7FIANCHPJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QTTRUUXCQNB7JLTMT7FIANCHPJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QTTRUUXCQNB7JLTMT7FIANCHPJ/action/storage_attestation","attest_author":"https://pith.science/pith/QTTRUUXCQNB7JLTMT7FIANCHPJ/action/author_attestation","sign_citation":"https://pith.science/pith/QTTRUUXCQNB7JLTMT7FIANCHPJ/action/citation_signature","submit_replication":"https://pith.science/pith/QTTRUUXCQNB7JLTMT7FIANCHPJ/action/replication_record"}},"created_at":"2026-07-05T04:45:35.329913+00:00","updated_at":"2026-07-05T04:45:35.329913+00:00"}