{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:LW2IZWZ44H3XSUSPZRCFVIEBDE","short_pith_number":"pith:LW2IZWZ4","canonical_record":{"source":{"id":"1610.02627","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2016-10-09T04:22:49Z","cross_cats_sorted":[],"title_canon_sha256":"5938f94f16fa9a85ba375be09e41d93217c35a08d2079d49e0cbc626bc1062e9","abstract_canon_sha256":"d89769bbe0bc0eab16c564398b5037635945c2b775d0da8f985cc773637e5603"},"schema_version":"1.0"},"canonical_sha256":"5db48cdb3ce1f779524fcc445aa081192d222e9fe610d8aeafbdb5c8feb0a698","source":{"kind":"arxiv","id":"1610.02627","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1610.02627","created_at":"2026-05-18T00:27:04Z"},{"alias_kind":"arxiv_version","alias_value":"1610.02627v3","created_at":"2026-05-18T00:27:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1610.02627","created_at":"2026-05-18T00:27:04Z"},{"alias_kind":"pith_short_12","alias_value":"LW2IZWZ44H3X","created_at":"2026-05-18T12:30:29Z"},{"alias_kind":"pith_short_16","alias_value":"LW2IZWZ44H3XSUSP","created_at":"2026-05-18T12:30:29Z"},{"alias_kind":"pith_short_8","alias_value":"LW2IZWZ4","created_at":"2026-05-18T12:30:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:LW2IZWZ44H3XSUSPZRCFVIEBDE","target":"record","payload":{"canonical_record":{"source":{"id":"1610.02627","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2016-10-09T04:22:49Z","cross_cats_sorted":[],"title_canon_sha256":"5938f94f16fa9a85ba375be09e41d93217c35a08d2079d49e0cbc626bc1062e9","abstract_canon_sha256":"d89769bbe0bc0eab16c564398b5037635945c2b775d0da8f985cc773637e5603"},"schema_version":"1.0"},"canonical_sha256":"5db48cdb3ce1f779524fcc445aa081192d222e9fe610d8aeafbdb5c8feb0a698","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:27:04.820878Z","signature_b64":"ivOl8tesgK+SZo9j9oY1142RXU3ams7pWcUBQ1JhK8fqP0v/j3+DJlwGFzu7q/CagCWjuuX3SlNamnWayegYAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5db48cdb3ce1f779524fcc445aa081192d222e9fe610d8aeafbdb5c8feb0a698","last_reissued_at":"2026-05-18T00:27:04.820373Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:27:04.820373Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1610.02627","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:27:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8ocivhVSrF7KaYndW9LHtD2J+UeVdjQ4hhzq3kE6a75XRvZYGpKBy3Adm0+Uss+cCY7bfonwVhkR9Y6PuiTJCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T02:49:19.652720Z"},"content_sha256":"c57a563fdaf83d812bd9272dff2fd31431036705f6e186598de8acff9df28e65","schema_version":"1.0","event_id":"sha256:c57a563fdaf83d812bd9272dff2fd31431036705f6e186598de8acff9df28e65"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:LW2IZWZ44H3XSUSPZRCFVIEBDE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Deep Generative Spatial Models for Mobile Robots","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Andrzej Pronobis, Rajesh P. N. Rao","submitted_at":"2016-10-09T04:22:49Z","abstract_excerpt":"We propose a new probabilistic framework that allows mobile robots to autonomously learn deep, generative models of their environments that span multiple levels of abstraction. Unlike traditional approaches that combine engineered models for low-level features, geometry, and semantics, our approach leverages recent advances in Sum-Product Networks (SPNs) and deep learning to learn a single, universal model of the robot's spatial environment. Our model is fully probabilistic and generative, and represents a joint distribution over spatial information ranging from low-level geometry to semantic "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1610.02627","kind":"arxiv","version":3},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:27:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FHsSBfA+iEjh3ZQLpm+CB6mhLGcRNV+XMBsXtYyqTaBqr19ItlaVVenTjK5ZaT7O+jmG7EatPoKLwhf117v7Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T02:49:19.653086Z"},"content_sha256":"3e1bf2e6a49704667f4f1487f23335b59c6ab644707eb41af508e12683b0411a","schema_version":"1.0","event_id":"sha256:3e1bf2e6a49704667f4f1487f23335b59c6ab644707eb41af508e12683b0411a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LW2IZWZ44H3XSUSPZRCFVIEBDE/bundle.json","state_url":"https://pith.science/pith/LW2IZWZ44H3XSUSPZRCFVIEBDE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LW2IZWZ44H3XSUSPZRCFVIEBDE/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":"2026-06-03T02:49:19Z","links":{"resolver":"https://pith.science/pith/LW2IZWZ44H3XSUSPZRCFVIEBDE","bundle":"https://pith.science/pith/LW2IZWZ44H3XSUSPZRCFVIEBDE/bundle.json","state":"https://pith.science/pith/LW2IZWZ44H3XSUSPZRCFVIEBDE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LW2IZWZ44H3XSUSPZRCFVIEBDE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:LW2IZWZ44H3XSUSPZRCFVIEBDE","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":"d89769bbe0bc0eab16c564398b5037635945c2b775d0da8f985cc773637e5603","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2016-10-09T04:22:49Z","title_canon_sha256":"5938f94f16fa9a85ba375be09e41d93217c35a08d2079d49e0cbc626bc1062e9"},"schema_version":"1.0","source":{"id":"1610.02627","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1610.02627","created_at":"2026-05-18T00:27:04Z"},{"alias_kind":"arxiv_version","alias_value":"1610.02627v3","created_at":"2026-05-18T00:27:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1610.02627","created_at":"2026-05-18T00:27:04Z"},{"alias_kind":"pith_short_12","alias_value":"LW2IZWZ44H3X","created_at":"2026-05-18T12:30:29Z"},{"alias_kind":"pith_short_16","alias_value":"LW2IZWZ44H3XSUSP","created_at":"2026-05-18T12:30:29Z"},{"alias_kind":"pith_short_8","alias_value":"LW2IZWZ4","created_at":"2026-05-18T12:30:29Z"}],"graph_snapshots":[{"event_id":"sha256:3e1bf2e6a49704667f4f1487f23335b59c6ab644707eb41af508e12683b0411a","target":"graph","created_at":"2026-05-18T00:27:04Z","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"},"paper":{"abstract_excerpt":"We propose a new probabilistic framework that allows mobile robots to autonomously learn deep, generative models of their environments that span multiple levels of abstraction. Unlike traditional approaches that combine engineered models for low-level features, geometry, and semantics, our approach leverages recent advances in Sum-Product Networks (SPNs) and deep learning to learn a single, universal model of the robot's spatial environment. Our model is fully probabilistic and generative, and represents a joint distribution over spatial information ranging from low-level geometry to semantic ","authors_text":"Andrzej Pronobis, Rajesh P. N. Rao","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2016-10-09T04:22:49Z","title":"Learning Deep Generative Spatial Models for Mobile Robots"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1610.02627","kind":"arxiv","version":3},"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:c57a563fdaf83d812bd9272dff2fd31431036705f6e186598de8acff9df28e65","target":"record","created_at":"2026-05-18T00:27:04Z","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":"d89769bbe0bc0eab16c564398b5037635945c2b775d0da8f985cc773637e5603","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2016-10-09T04:22:49Z","title_canon_sha256":"5938f94f16fa9a85ba375be09e41d93217c35a08d2079d49e0cbc626bc1062e9"},"schema_version":"1.0","source":{"id":"1610.02627","kind":"arxiv","version":3}},"canonical_sha256":"5db48cdb3ce1f779524fcc445aa081192d222e9fe610d8aeafbdb5c8feb0a698","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5db48cdb3ce1f779524fcc445aa081192d222e9fe610d8aeafbdb5c8feb0a698","first_computed_at":"2026-05-18T00:27:04.820373Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:27:04.820373Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ivOl8tesgK+SZo9j9oY1142RXU3ams7pWcUBQ1JhK8fqP0v/j3+DJlwGFzu7q/CagCWjuuX3SlNamnWayegYAQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:27:04.820878Z","signed_message":"canonical_sha256_bytes"},"source_id":"1610.02627","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c57a563fdaf83d812bd9272dff2fd31431036705f6e186598de8acff9df28e65","sha256:3e1bf2e6a49704667f4f1487f23335b59c6ab644707eb41af508e12683b0411a"],"state_sha256":"da7b897a738fd695912b6a32e4cb1cfad8c668a0a696be710842578907b93b5c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Gv0JU8dY8ZToJi5cy8fY7ArL3qXNSS9XtTUh2BTs0J02LlZuU+NuYR4Vr3kEPzidBpjdsUkS1SCJElf/0tNMAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T02:49:19.655041Z","bundle_sha256":"85f49f9f3236532e36e23c32578b923945b21c47af8481cc8f280c834ab2a609"}}