{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:5S6OQO6V6LM4M33CZQLVTXK4MP","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":"6842b374bcc24002a373aedab1ad7dea3e83a6f08ee21c244a5d221286816c66","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2023-03-09T11:45:48Z","title_canon_sha256":"bcd833411bf045a96522f91b3688ca3ecda4917f3f0a205f39682999298cb24a"},"schema_version":"1.0","source":{"id":"2303.05193","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.05193","created_at":"2026-07-05T06:53:35Z"},{"alias_kind":"arxiv_version","alias_value":"2303.05193v4","created_at":"2026-07-05T06:53:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.05193","created_at":"2026-07-05T06:53:35Z"},{"alias_kind":"pith_short_12","alias_value":"5S6OQO6V6LM4","created_at":"2026-07-05T06:53:35Z"},{"alias_kind":"pith_short_16","alias_value":"5S6OQO6V6LM4M33C","created_at":"2026-07-05T06:53:35Z"},{"alias_kind":"pith_short_8","alias_value":"5S6OQO6V","created_at":"2026-07-05T06:53:35Z"}],"graph_snapshots":[{"event_id":"sha256:8b7f2a9d963c3f5819f8cd12396e09f18aa39d18628f473bb280bf6cd56a49b5","target":"graph","created_at":"2026-07-05T06:53:35Z","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/2303.05193/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this work, we first formulate the problem of robotic water scooping using goal-conditioned reinforcement learning. This task is particularly challenging due to the complex dynamics of fluids and the need to achieve multi-modal goals. The policy is required to successfully reach both position goals and water amount goals, which leads to a large convoluted goal state space. To overcome these challenges, we introduce Goal Sampling Adaptation for Scooping (GOATS), a curriculum reinforcement learning method that can learn an effective and generalizable policy for robot scooping tasks. Specifical","authors_text":"Ding Zhao, Jiacheng Zhu, Liangjun Zhang, Shiyu Jin, Yaru Niu, Zeqing Zhang","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2023-03-09T11:45:48Z","title":"GOATS: Goal Sampling Adaptation for Scooping with Curriculum Reinforcement Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.05193","kind":"arxiv","version":4},"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:efa3765adc28429652386000162160b6d9b853064fe5ececb7bfa502a8a73754","target":"record","created_at":"2026-07-05T06:53:35Z","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":"6842b374bcc24002a373aedab1ad7dea3e83a6f08ee21c244a5d221286816c66","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2023-03-09T11:45:48Z","title_canon_sha256":"bcd833411bf045a96522f91b3688ca3ecda4917f3f0a205f39682999298cb24a"},"schema_version":"1.0","source":{"id":"2303.05193","kind":"arxiv","version":4}},"canonical_sha256":"ecbce83bd5f2d9c66f62cc1759dd5c63deb0201d226050fd1833a15c72f947cf","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ecbce83bd5f2d9c66f62cc1759dd5c63deb0201d226050fd1833a15c72f947cf","first_computed_at":"2026-07-05T06:53:35.407030Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:53:35.407030Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"w430GhMm6ZQdUxKEPizVdOoAJ9dRPj5W/xY1NsIw5UeyACUihG9QjPi0o1RZW11Nt129MSBlVvNBDAtcavXTCw==","signature_status":"signed_v1","signed_at":"2026-07-05T06:53:35.407547Z","signed_message":"canonical_sha256_bytes"},"source_id":"2303.05193","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:efa3765adc28429652386000162160b6d9b853064fe5ececb7bfa502a8a73754","sha256:8b7f2a9d963c3f5819f8cd12396e09f18aa39d18628f473bb280bf6cd56a49b5"],"state_sha256":"dc7c9744d29623db6586e066cdfc0f005f28d1a6baa20b6c1e433c7aa6aacfa5"}