{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:OHTYT2G7DQZVALMMGDP4V6HMWY","short_pith_number":"pith:OHTYT2G7","schema_version":"1.0","canonical_sha256":"71e789e8df1c33502d8c30dfcaf8ecb6129650a1e27c3f415aa0f664180b18d8","source":{"kind":"arxiv","id":"2306.08205","version":2},"attestation_state":"computed","paper":{"title":"Agile Catching with Whole-Body MPC and Blackbox Policy Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Alex Bewley, Anish Shankar, David D'Ambrosio, Deepali Jain, Jean-Jacques Slotine, Krzysztof Choromanski, Nicholas M. Boffi, Pannag Sanketi, Saminda Abeyruwan, Stephen Tu, Sumeet Singh, Vikas Sindhwani","submitted_at":"2023-06-14T02:13:25Z","abstract_excerpt":"We address a benchmark task in agile robotics: catching objects thrown at high-speed. This is a challenging task that involves tracking, intercepting, and cradling a thrown object with access only to visual observations of the object and the proprioceptive state of the robot, all within a fraction of a second. We present the relative merits of two fundamentally different solution strategies: (i) Model Predictive Control using accelerated constrained trajectory optimization, and (ii) Reinforcement Learning using zeroth-order optimization. We provide insights into various performance trade-offs "},"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":"2306.08205","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2023-06-14T02:13:25Z","cross_cats_sorted":[],"title_canon_sha256":"70481ce0a780d66b78d172d89bc8afd9433e1b580c71e6abc46e5a5a4e44bc2e","abstract_canon_sha256":"324a62ae677798756664940eb68272787b9e0a1c1298aacd5b216bc5ca530cac"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:02:51.253172Z","signature_b64":"NUkDc95MpCxnkQG7xF7pdkPFkNF2ZKueXZ6ci4cCkufvu5CALGzWeZsxhGvbjTILC8KjF6ViYydCIwLCOVTSBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"71e789e8df1c33502d8c30dfcaf8ecb6129650a1e27c3f415aa0f664180b18d8","last_reissued_at":"2026-07-05T07:02:51.252724Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:02:51.252724Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Agile Catching with Whole-Body MPC and Blackbox Policy Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Alex Bewley, Anish Shankar, David D'Ambrosio, Deepali Jain, Jean-Jacques Slotine, Krzysztof Choromanski, Nicholas M. Boffi, Pannag Sanketi, Saminda Abeyruwan, Stephen Tu, Sumeet Singh, Vikas Sindhwani","submitted_at":"2023-06-14T02:13:25Z","abstract_excerpt":"We address a benchmark task in agile robotics: catching objects thrown at high-speed. This is a challenging task that involves tracking, intercepting, and cradling a thrown object with access only to visual observations of the object and the proprioceptive state of the robot, all within a fraction of a second. We present the relative merits of two fundamentally different solution strategies: (i) Model Predictive Control using accelerated constrained trajectory optimization, and (ii) Reinforcement Learning using zeroth-order optimization. We provide insights into various performance trade-offs "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.08205","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/2306.08205/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":"2306.08205","created_at":"2026-07-05T07:02:51.252782+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.08205v2","created_at":"2026-07-05T07:02:51.252782+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.08205","created_at":"2026-07-05T07:02:51.252782+00:00"},{"alias_kind":"pith_short_12","alias_value":"OHTYT2G7DQZV","created_at":"2026-07-05T07:02:51.252782+00:00"},{"alias_kind":"pith_short_16","alias_value":"OHTYT2G7DQZVALMM","created_at":"2026-07-05T07:02:51.252782+00:00"},{"alias_kind":"pith_short_8","alias_value":"OHTYT2G7","created_at":"2026-07-05T07:02:51.252782+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/OHTYT2G7DQZVALMMGDP4V6HMWY","json":"https://pith.science/pith/OHTYT2G7DQZVALMMGDP4V6HMWY.json","graph_json":"https://pith.science/api/pith-number/OHTYT2G7DQZVALMMGDP4V6HMWY/graph.json","events_json":"https://pith.science/api/pith-number/OHTYT2G7DQZVALMMGDP4V6HMWY/events.json","paper":"https://pith.science/paper/OHTYT2G7"},"agent_actions":{"view_html":"https://pith.science/pith/OHTYT2G7DQZVALMMGDP4V6HMWY","download_json":"https://pith.science/pith/OHTYT2G7DQZVALMMGDP4V6HMWY.json","view_paper":"https://pith.science/paper/OHTYT2G7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.08205&json=true","fetch_graph":"https://pith.science/api/pith-number/OHTYT2G7DQZVALMMGDP4V6HMWY/graph.json","fetch_events":"https://pith.science/api/pith-number/OHTYT2G7DQZVALMMGDP4V6HMWY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OHTYT2G7DQZVALMMGDP4V6HMWY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OHTYT2G7DQZVALMMGDP4V6HMWY/action/storage_attestation","attest_author":"https://pith.science/pith/OHTYT2G7DQZVALMMGDP4V6HMWY/action/author_attestation","sign_citation":"https://pith.science/pith/OHTYT2G7DQZVALMMGDP4V6HMWY/action/citation_signature","submit_replication":"https://pith.science/pith/OHTYT2G7DQZVALMMGDP4V6HMWY/action/replication_record"}},"created_at":"2026-07-05T07:02:51.252782+00:00","updated_at":"2026-07-05T07:02:51.252782+00:00"}