{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:WTLKL4YKCEV5XZAENXUXFVWNRR","short_pith_number":"pith:WTLKL4YK","schema_version":"1.0","canonical_sha256":"b4d6a5f30a112bdbe4046de972d6cd8c7ec12edc8a6c977bbb2642ebad28ec51","source":{"kind":"arxiv","id":"2104.01167","version":1},"attestation_state":"computed","paper":{"title":"Tactile-RL for Insertion: Generalization to Objects of Unknown Geometry","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Alberto Rodriguez, Daniel Nikovski, Devesh K. Jha, Diego Romeres, Sangwoon Kim, Siyuan Dong","submitted_at":"2021-04-02T17:34:43Z","abstract_excerpt":"Object insertion is a classic contact-rich manipulation task. The task remains challenging, especially when considering general objects of unknown geometry, which significantly limits the ability to understand the contact configuration between the object and the environment. We study the problem of aligning the object and environment with a tactile-based feedback insertion policy. The insertion process is modeled as an episodic policy that iterates between insertion attempts followed by pose corrections. We explore different mechanisms to learn such a policy based on Reinforcement Learning. Th"},"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":"2104.01167","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2021-04-02T17:34:43Z","cross_cats_sorted":[],"title_canon_sha256":"e260a1b488fbe891e18d318275d12a16940ff3561d541b8c17fa83f3103f19f6","abstract_canon_sha256":"55a7b6963ba072ba176d20adb5887e1eaef505633275a9995f7af5bc875e9d4d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:28:45.402561Z","signature_b64":"TMXo1J7o28ZitdsE2XBNwHqE8j1+s0oE9tB1xESuwzuV7HOuIz/jotBA0fHk4llqJZBlQlt6wKZL52qPWJNMAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b4d6a5f30a112bdbe4046de972d6cd8c7ec12edc8a6c977bbb2642ebad28ec51","last_reissued_at":"2026-07-05T02:28:45.402078Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:28:45.402078Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Tactile-RL for Insertion: Generalization to Objects of Unknown Geometry","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Alberto Rodriguez, Daniel Nikovski, Devesh K. Jha, Diego Romeres, Sangwoon Kim, Siyuan Dong","submitted_at":"2021-04-02T17:34:43Z","abstract_excerpt":"Object insertion is a classic contact-rich manipulation task. The task remains challenging, especially when considering general objects of unknown geometry, which significantly limits the ability to understand the contact configuration between the object and the environment. We study the problem of aligning the object and environment with a tactile-based feedback insertion policy. The insertion process is modeled as an episodic policy that iterates between insertion attempts followed by pose corrections. We explore different mechanisms to learn such a policy based on Reinforcement Learning. Th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.01167","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2104.01167/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":"2104.01167","created_at":"2026-07-05T02:28:45.402136+00:00"},{"alias_kind":"arxiv_version","alias_value":"2104.01167v1","created_at":"2026-07-05T02:28:45.402136+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.01167","created_at":"2026-07-05T02:28:45.402136+00:00"},{"alias_kind":"pith_short_12","alias_value":"WTLKL4YKCEV5","created_at":"2026-07-05T02:28:45.402136+00:00"},{"alias_kind":"pith_short_16","alias_value":"WTLKL4YKCEV5XZAE","created_at":"2026-07-05T02:28:45.402136+00:00"},{"alias_kind":"pith_short_8","alias_value":"WTLKL4YK","created_at":"2026-07-05T02:28:45.402136+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2603.04531","citing_title":"PTLD: Sim-to-real Privileged Tactile Latent Distillation for Dexterous Manipulation","ref_index":45,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WTLKL4YKCEV5XZAENXUXFVWNRR","json":"https://pith.science/pith/WTLKL4YKCEV5XZAENXUXFVWNRR.json","graph_json":"https://pith.science/api/pith-number/WTLKL4YKCEV5XZAENXUXFVWNRR/graph.json","events_json":"https://pith.science/api/pith-number/WTLKL4YKCEV5XZAENXUXFVWNRR/events.json","paper":"https://pith.science/paper/WTLKL4YK"},"agent_actions":{"view_html":"https://pith.science/pith/WTLKL4YKCEV5XZAENXUXFVWNRR","download_json":"https://pith.science/pith/WTLKL4YKCEV5XZAENXUXFVWNRR.json","view_paper":"https://pith.science/paper/WTLKL4YK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2104.01167&json=true","fetch_graph":"https://pith.science/api/pith-number/WTLKL4YKCEV5XZAENXUXFVWNRR/graph.json","fetch_events":"https://pith.science/api/pith-number/WTLKL4YKCEV5XZAENXUXFVWNRR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WTLKL4YKCEV5XZAENXUXFVWNRR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WTLKL4YKCEV5XZAENXUXFVWNRR/action/storage_attestation","attest_author":"https://pith.science/pith/WTLKL4YKCEV5XZAENXUXFVWNRR/action/author_attestation","sign_citation":"https://pith.science/pith/WTLKL4YKCEV5XZAENXUXFVWNRR/action/citation_signature","submit_replication":"https://pith.science/pith/WTLKL4YKCEV5XZAENXUXFVWNRR/action/replication_record"}},"created_at":"2026-07-05T02:28:45.402136+00:00","updated_at":"2026-07-05T02:28:45.402136+00:00"}