{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:5FCOB7ZXXQLST6BNMBTJ4CQJNB","short_pith_number":"pith:5FCOB7ZX","canonical_record":{"source":{"id":"1810.00482","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-09-30T22:57:58Z","cross_cats_sorted":["cs.AI","cs.CV","cs.RO","stat.ML"],"title_canon_sha256":"f48f883b558011a9994d2114aa5ea55aa82043ff172c4abeaa10dce378f223cb","abstract_canon_sha256":"0ec372d431d92a69dd02ca1e34484056d050d5d3d30c49f27500fc2664257f67"},"schema_version":"1.0"},"canonical_sha256":"e944e0ff37bc1729f82d60669e0a0968651eea70ff9bd898743f5c7f2557959a","source":{"kind":"arxiv","id":"1810.00482","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.00482","created_at":"2026-05-18T00:04:26Z"},{"alias_kind":"arxiv_version","alias_value":"1810.00482v1","created_at":"2026-05-18T00:04:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.00482","created_at":"2026-05-18T00:04:26Z"},{"alias_kind":"pith_short_12","alias_value":"5FCOB7ZXXQLS","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_16","alias_value":"5FCOB7ZXXQLST6BN","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_8","alias_value":"5FCOB7ZX","created_at":"2026-05-18T12:32:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:5FCOB7ZXXQLST6BNMBTJ4CQJNB","target":"record","payload":{"canonical_record":{"source":{"id":"1810.00482","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-09-30T22:57:58Z","cross_cats_sorted":["cs.AI","cs.CV","cs.RO","stat.ML"],"title_canon_sha256":"f48f883b558011a9994d2114aa5ea55aa82043ff172c4abeaa10dce378f223cb","abstract_canon_sha256":"0ec372d431d92a69dd02ca1e34484056d050d5d3d30c49f27500fc2664257f67"},"schema_version":"1.0"},"canonical_sha256":"e944e0ff37bc1729f82d60669e0a0968651eea70ff9bd898743f5c7f2557959a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:04:26.030012Z","signature_b64":"glU5dfjco2SICnRoZchF5uKqK7TexTVPn+30dyo9UIq7JK7YPi6wXFTi8m/7Xr6gDuE6sDCkMImLMrXythxaBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e944e0ff37bc1729f82d60669e0a0968651eea70ff9bd898743f5c7f2557959a","last_reissued_at":"2026-05-18T00:04:26.029549Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:04:26.029549Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1810.00482","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:04:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tHFZHgaf1ALuNVGWay7bjm5RBA5ASPPLEQtLbwmmCYT13BhQeGHO9pe3IznpI0g4epxvTqtn8sCKo6j2DslXAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T04:09:38.735790Z"},"content_sha256":"52f5ac16fbcb3509c0acd2cc541edca86f70ce3196e35b002d0321c4b0e014d0","schema_version":"1.0","event_id":"sha256:52f5ac16fbcb3509c0acd2cc541edca86f70ce3196e35b002d0321c4b0e014d0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:5FCOB7ZXXQLST6BNMBTJ4CQJNB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Few-Shot Goal Inference for Visuomotor Learning and Planning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.RO","stat.ML"],"primary_cat":"cs.LG","authors_text":"Annie Xie, Avi Singh, Chelsea Finn, Sergey Levine","submitted_at":"2018-09-30T22:57:58Z","abstract_excerpt":"Reinforcement learning and planning methods require an objective or reward function that encodes the desired behavior. Yet, in practice, there is a wide range of scenarios where an objective is difficult to provide programmatically, such as tasks with visual observations involving unknown object positions or deformable objects. In these cases, prior methods use engineered problem-specific solutions, e.g., by instrumenting the environment with additional sensors to measure a proxy for the objective. Such solutions require a significant engineering effort on a per-task basis, and make it impract"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.00482","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":""},"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":"2026-05-18T00:04:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"60PBUWpNZGc76h7vsaq1RZR6KOCH61iquC/NK9psslUq/hrXA2nVpcmNuMgfHetxxj6R/6juyy0XmNXNZGpHCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T04:09:38.736147Z"},"content_sha256":"6a782648ff6701504329d9a89229dc2a17a440ef106fcf5a3512ef8b52734da3","schema_version":"1.0","event_id":"sha256:6a782648ff6701504329d9a89229dc2a17a440ef106fcf5a3512ef8b52734da3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5FCOB7ZXXQLST6BNMBTJ4CQJNB/bundle.json","state_url":"https://pith.science/pith/5FCOB7ZXXQLST6BNMBTJ4CQJNB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5FCOB7ZXXQLST6BNMBTJ4CQJNB/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":"2026-05-30T04:09:38Z","links":{"resolver":"https://pith.science/pith/5FCOB7ZXXQLST6BNMBTJ4CQJNB","bundle":"https://pith.science/pith/5FCOB7ZXXQLST6BNMBTJ4CQJNB/bundle.json","state":"https://pith.science/pith/5FCOB7ZXXQLST6BNMBTJ4CQJNB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5FCOB7ZXXQLST6BNMBTJ4CQJNB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:5FCOB7ZXXQLST6BNMBTJ4CQJNB","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":"0ec372d431d92a69dd02ca1e34484056d050d5d3d30c49f27500fc2664257f67","cross_cats_sorted":["cs.AI","cs.CV","cs.RO","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-09-30T22:57:58Z","title_canon_sha256":"f48f883b558011a9994d2114aa5ea55aa82043ff172c4abeaa10dce378f223cb"},"schema_version":"1.0","source":{"id":"1810.00482","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.00482","created_at":"2026-05-18T00:04:26Z"},{"alias_kind":"arxiv_version","alias_value":"1810.00482v1","created_at":"2026-05-18T00:04:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.00482","created_at":"2026-05-18T00:04:26Z"},{"alias_kind":"pith_short_12","alias_value":"5FCOB7ZXXQLS","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_16","alias_value":"5FCOB7ZXXQLST6BN","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_8","alias_value":"5FCOB7ZX","created_at":"2026-05-18T12:32:08Z"}],"graph_snapshots":[{"event_id":"sha256:6a782648ff6701504329d9a89229dc2a17a440ef106fcf5a3512ef8b52734da3","target":"graph","created_at":"2026-05-18T00:04:26Z","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":"Reinforcement learning and planning methods require an objective or reward function that encodes the desired behavior. Yet, in practice, there is a wide range of scenarios where an objective is difficult to provide programmatically, such as tasks with visual observations involving unknown object positions or deformable objects. In these cases, prior methods use engineered problem-specific solutions, e.g., by instrumenting the environment with additional sensors to measure a proxy for the objective. Such solutions require a significant engineering effort on a per-task basis, and make it impract","authors_text":"Annie Xie, Avi Singh, Chelsea Finn, Sergey Levine","cross_cats":["cs.AI","cs.CV","cs.RO","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-09-30T22:57:58Z","title":"Few-Shot Goal Inference for Visuomotor Learning and Planning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.00482","kind":"arxiv","version":1},"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":"sha256:52f5ac16fbcb3509c0acd2cc541edca86f70ce3196e35b002d0321c4b0e014d0","target":"record","created_at":"2026-05-18T00:04:26Z","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":"0ec372d431d92a69dd02ca1e34484056d050d5d3d30c49f27500fc2664257f67","cross_cats_sorted":["cs.AI","cs.CV","cs.RO","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-09-30T22:57:58Z","title_canon_sha256":"f48f883b558011a9994d2114aa5ea55aa82043ff172c4abeaa10dce378f223cb"},"schema_version":"1.0","source":{"id":"1810.00482","kind":"arxiv","version":1}},"canonical_sha256":"e944e0ff37bc1729f82d60669e0a0968651eea70ff9bd898743f5c7f2557959a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e944e0ff37bc1729f82d60669e0a0968651eea70ff9bd898743f5c7f2557959a","first_computed_at":"2026-05-18T00:04:26.029549Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:04:26.029549Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"glU5dfjco2SICnRoZchF5uKqK7TexTVPn+30dyo9UIq7JK7YPi6wXFTi8m/7Xr6gDuE6sDCkMImLMrXythxaBg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:04:26.030012Z","signed_message":"canonical_sha256_bytes"},"source_id":"1810.00482","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:52f5ac16fbcb3509c0acd2cc541edca86f70ce3196e35b002d0321c4b0e014d0","sha256:6a782648ff6701504329d9a89229dc2a17a440ef106fcf5a3512ef8b52734da3"],"state_sha256":"da5177b6d7264d2f30d2684c3cf16bbe63328f07088efa7fc91a076c22af9f47"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"y2PUs7SExsE5gI0dQHJxUrVF56B32p//oCRAUB4KV/ADgaq6igZIp6S+3agr68zPe7XTCQEUF7FFzTVvpK8KDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T04:09:38.738204Z","bundle_sha256":"28ce9c332136911bcb94ef79f7274e06c0413856a2c7820428688c807a26ddbb"}}