{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:7SHGOZXDVW2Q57XKJTHN45ITMM","short_pith_number":"pith:7SHGOZXD","canonical_record":{"source":{"id":"2404.07344","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2024-04-10T20:59:59Z","cross_cats_sorted":["cs.AI","cs.IT","math.IT"],"title_canon_sha256":"e145d1847fcff292a24f1d53a8a4f5baff62c530f28050135e208995d9f5d18b","abstract_canon_sha256":"acfe929d34552fe4e3c02b97138d23e432a499143d50517e154c9ab0742750a9"},"schema_version":"1.0"},"canonical_sha256":"fc8e6766e3adb50efeea4ccede7513630b5ea72010bebf81293356ffd0601147","source":{"kind":"arxiv","id":"2404.07344","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2404.07344","created_at":"2026-07-05T10:08:15Z"},{"alias_kind":"arxiv_version","alias_value":"2404.07344v2","created_at":"2026-07-05T10:08:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.07344","created_at":"2026-07-05T10:08:15Z"},{"alias_kind":"pith_short_12","alias_value":"7SHGOZXDVW2Q","created_at":"2026-07-05T10:08:15Z"},{"alias_kind":"pith_short_16","alias_value":"7SHGOZXDVW2Q57XK","created_at":"2026-07-05T10:08:15Z"},{"alias_kind":"pith_short_8","alias_value":"7SHGOZXD","created_at":"2026-07-05T10:08:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:7SHGOZXDVW2Q57XKJTHN45ITMM","target":"record","payload":{"canonical_record":{"source":{"id":"2404.07344","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2024-04-10T20:59:59Z","cross_cats_sorted":["cs.AI","cs.IT","math.IT"],"title_canon_sha256":"e145d1847fcff292a24f1d53a8a4f5baff62c530f28050135e208995d9f5d18b","abstract_canon_sha256":"acfe929d34552fe4e3c02b97138d23e432a499143d50517e154c9ab0742750a9"},"schema_version":"1.0"},"canonical_sha256":"fc8e6766e3adb50efeea4ccede7513630b5ea72010bebf81293356ffd0601147","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:08:15.531811Z","signature_b64":"bZKpeSXb/VasWxPwf3bSBPBdp0xoGTX48CSWGRVaGpMFIwUJ42MNTov0QJnjU9RuSrO5jmFYIZe1/N91NEGrCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fc8e6766e3adb50efeea4ccede7513630b5ea72010bebf81293356ffd0601147","last_reissued_at":"2026-07-05T10:08:15.531029Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:08:15.531029Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2404.07344","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:08:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fr7jZjeu/lX+BsRwqg5B/JaReYCH0/UQif2d9f2Bgq+MSmlRvimA3XRs8kcrCj7bsJFP+dEqWV59aFlMIwPADA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-12T08:28:52.747960Z"},"content_sha256":"e5cd77aed045462d10005d52e312a0ef3bed24c45fe6aa77c47d8aff1c560ab6","schema_version":"1.0","event_id":"sha256:e5cd77aed045462d10005d52e312a0ef3bed24c45fe6aa77c47d8aff1c560ab6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:7SHGOZXDVW2Q57XKJTHN45ITMM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Interactive Learning of Physical Object Properties Through Robot Manipulation and Database of Object Measurements","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.IT","math.IT"],"primary_cat":"cs.RO","authors_text":"Andrej Kruzliak, Fares J. Abu-Dakka, Jan Kristof Behrens, Jiri Hartvich, Krystian Mikolajczyk, Lukas Rustler, Matej Hoffmann, Shubhan P. Patni, Ville Kyrki","submitted_at":"2024-04-10T20:59:59Z","abstract_excerpt":"This work presents a framework for automatically extracting physical object properties, such as material composition, mass, volume, and stiffness, through robot manipulation and a database of object measurements. The framework involves exploratory action selection to maximize learning about objects on a table. A Bayesian network models conditional dependencies between object properties, incorporating prior probability distributions and uncertainty associated with measurement actions. The algorithm selects optimal exploratory actions based on expected information gain and updates object propert"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.07344","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/2404.07344/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:08:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Jo+R0zWQgReI/UBpa28rlutpUxYfvTsjsIWZ+FXecM6FqCvMEwR3hz/Xz5JVO8pWXpMv3JJL2XxTmaCWBjrACQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-12T08:28:52.748338Z"},"content_sha256":"2ece04a5e878d6e9eb98750acb91f376218ac5b7be18361112165545cfe82abf","schema_version":"1.0","event_id":"sha256:2ece04a5e878d6e9eb98750acb91f376218ac5b7be18361112165545cfe82abf"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7SHGOZXDVW2Q57XKJTHN45ITMM/bundle.json","state_url":"https://pith.science/pith/7SHGOZXDVW2Q57XKJTHN45ITMM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7SHGOZXDVW2Q57XKJTHN45ITMM/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-12T08:28:52Z","links":{"resolver":"https://pith.science/pith/7SHGOZXDVW2Q57XKJTHN45ITMM","bundle":"https://pith.science/pith/7SHGOZXDVW2Q57XKJTHN45ITMM/bundle.json","state":"https://pith.science/pith/7SHGOZXDVW2Q57XKJTHN45ITMM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7SHGOZXDVW2Q57XKJTHN45ITMM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:7SHGOZXDVW2Q57XKJTHN45ITMM","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"acfe929d34552fe4e3c02b97138d23e432a499143d50517e154c9ab0742750a9","cross_cats_sorted":["cs.AI","cs.IT","math.IT"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2024-04-10T20:59:59Z","title_canon_sha256":"e145d1847fcff292a24f1d53a8a4f5baff62c530f28050135e208995d9f5d18b"},"schema_version":"1.0","source":{"id":"2404.07344","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2404.07344","created_at":"2026-07-05T10:08:15Z"},{"alias_kind":"arxiv_version","alias_value":"2404.07344v2","created_at":"2026-07-05T10:08:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.07344","created_at":"2026-07-05T10:08:15Z"},{"alias_kind":"pith_short_12","alias_value":"7SHGOZXDVW2Q","created_at":"2026-07-05T10:08:15Z"},{"alias_kind":"pith_short_16","alias_value":"7SHGOZXDVW2Q57XK","created_at":"2026-07-05T10:08:15Z"},{"alias_kind":"pith_short_8","alias_value":"7SHGOZXD","created_at":"2026-07-05T10:08:15Z"}],"graph_snapshots":[{"event_id":"sha256:2ece04a5e878d6e9eb98750acb91f376218ac5b7be18361112165545cfe82abf","target":"graph","created_at":"2026-07-05T10:08:15Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2404.07344/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This work presents a framework for automatically extracting physical object properties, such as material composition, mass, volume, and stiffness, through robot manipulation and a database of object measurements. The framework involves exploratory action selection to maximize learning about objects on a table. A Bayesian network models conditional dependencies between object properties, incorporating prior probability distributions and uncertainty associated with measurement actions. The algorithm selects optimal exploratory actions based on expected information gain and updates object propert","authors_text":"Andrej Kruzliak, Fares J. Abu-Dakka, Jan Kristof Behrens, Jiri Hartvich, Krystian Mikolajczyk, Lukas Rustler, Matej Hoffmann, Shubhan P. Patni, Ville Kyrki","cross_cats":["cs.AI","cs.IT","math.IT"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2024-04-10T20:59:59Z","title":"Interactive Learning of Physical Object Properties Through Robot Manipulation and Database of Object Measurements"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.07344","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:e5cd77aed045462d10005d52e312a0ef3bed24c45fe6aa77c47d8aff1c560ab6","target":"record","created_at":"2026-07-05T10:08:15Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"acfe929d34552fe4e3c02b97138d23e432a499143d50517e154c9ab0742750a9","cross_cats_sorted":["cs.AI","cs.IT","math.IT"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2024-04-10T20:59:59Z","title_canon_sha256":"e145d1847fcff292a24f1d53a8a4f5baff62c530f28050135e208995d9f5d18b"},"schema_version":"1.0","source":{"id":"2404.07344","kind":"arxiv","version":2}},"canonical_sha256":"fc8e6766e3adb50efeea4ccede7513630b5ea72010bebf81293356ffd0601147","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fc8e6766e3adb50efeea4ccede7513630b5ea72010bebf81293356ffd0601147","first_computed_at":"2026-07-05T10:08:15.531029Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:08:15.531029Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bZKpeSXb/VasWxPwf3bSBPBdp0xoGTX48CSWGRVaGpMFIwUJ42MNTov0QJnjU9RuSrO5jmFYIZe1/N91NEGrCA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:08:15.531811Z","signed_message":"canonical_sha256_bytes"},"source_id":"2404.07344","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e5cd77aed045462d10005d52e312a0ef3bed24c45fe6aa77c47d8aff1c560ab6","sha256:2ece04a5e878d6e9eb98750acb91f376218ac5b7be18361112165545cfe82abf"],"state_sha256":"151743aea389696671a29017526269ed87ed62d01d211db664c124c94a897e73"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1OGkclb7Xr7yQI+kQzFzfofxR5OhGNk8qrJm23jpUnMXZAmfLHX09+UaShhdIDFLtEXli888inYM2OecrznPAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-12T08:28:52.750597Z","bundle_sha256":"1614978591e155e14ef22d423aae2c34d4816d00fc8bad0f25cac6a7ac8f71a1"}}