{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:HMCCESUYL25VDSESWRMOWXJJ7X","short_pith_number":"pith:HMCCESUY","schema_version":"1.0","canonical_sha256":"3b04224a985ebb51c892b458eb5d29fdfa8df6fdad163824e6f28ff99fbee675","source":{"kind":"arxiv","id":"2411.08566","version":2},"attestation_state":"computed","paper":{"title":"Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Leonidas Askianakis","submitted_at":"2024-11-13T12:26:08Z","abstract_excerpt":"Grasping by a robot in unstructured environments is deemed a critical challenge because of the requirement for effective adaptation to a wide variation in object geometries, material properties, and other environmental factors. In this paper, we propose a novel framework for robotic grasping based on the idea of compressing high-dimensional target and gripper features in a common latent space using a set of autoencoders. Our approach simplifies grasping by using three autoencoders dedicated to the target, the gripper, and a third one that fuses their latent representations. This allows the RL "},"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":"2411.08566","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-11-13T12:26:08Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"de7a0c5791db85db926d0ad839f7f82e95ed6cc0dce66dcf3bca2a4920feb81d","abstract_canon_sha256":"5e5212b22acf308526bbd78798785edd9ceddfdcacb2d5f38f0ebe7fec13c43f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:37:43.007180Z","signature_b64":"jqS7kmJpY0t3W5y/+KrE+y86mcuk753qYa4Zf2tvSijPYagt6YjW1Wi4CJ7yw1Bdog1NzvOSDq+FpUmd86PPBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3b04224a985ebb51c892b458eb5d29fdfa8df6fdad163824e6f28ff99fbee675","last_reissued_at":"2026-07-05T09:37:43.006767Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:37:43.006767Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Leonidas Askianakis","submitted_at":"2024-11-13T12:26:08Z","abstract_excerpt":"Grasping by a robot in unstructured environments is deemed a critical challenge because of the requirement for effective adaptation to a wide variation in object geometries, material properties, and other environmental factors. In this paper, we propose a novel framework for robotic grasping based on the idea of compressing high-dimensional target and gripper features in a common latent space using a set of autoencoders. Our approach simplifies grasping by using three autoencoders dedicated to the target, the gripper, and a third one that fuses their latent representations. This allows the RL "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.08566","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/2411.08566/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":"2411.08566","created_at":"2026-07-05T09:37:43.006824+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.08566v2","created_at":"2026-07-05T09:37:43.006824+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.08566","created_at":"2026-07-05T09:37:43.006824+00:00"},{"alias_kind":"pith_short_12","alias_value":"HMCCESUYL25V","created_at":"2026-07-05T09:37:43.006824+00:00"},{"alias_kind":"pith_short_16","alias_value":"HMCCESUYL25VDSES","created_at":"2026-07-05T09:37:43.006824+00:00"},{"alias_kind":"pith_short_8","alias_value":"HMCCESUY","created_at":"2026-07-05T09:37:43.006824+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.07777","citing_title":"Continuous In-Situ and Remote Sun Observation for Space Weather Monitoring and Mitigation of Infrastructure Threats Through an Optimized Heliocentric Satellite Constellation","ref_index":36,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HMCCESUYL25VDSESWRMOWXJJ7X","json":"https://pith.science/pith/HMCCESUYL25VDSESWRMOWXJJ7X.json","graph_json":"https://pith.science/api/pith-number/HMCCESUYL25VDSESWRMOWXJJ7X/graph.json","events_json":"https://pith.science/api/pith-number/HMCCESUYL25VDSESWRMOWXJJ7X/events.json","paper":"https://pith.science/paper/HMCCESUY"},"agent_actions":{"view_html":"https://pith.science/pith/HMCCESUYL25VDSESWRMOWXJJ7X","download_json":"https://pith.science/pith/HMCCESUYL25VDSESWRMOWXJJ7X.json","view_paper":"https://pith.science/paper/HMCCESUY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.08566&json=true","fetch_graph":"https://pith.science/api/pith-number/HMCCESUYL25VDSESWRMOWXJJ7X/graph.json","fetch_events":"https://pith.science/api/pith-number/HMCCESUYL25VDSESWRMOWXJJ7X/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HMCCESUYL25VDSESWRMOWXJJ7X/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HMCCESUYL25VDSESWRMOWXJJ7X/action/storage_attestation","attest_author":"https://pith.science/pith/HMCCESUYL25VDSESWRMOWXJJ7X/action/author_attestation","sign_citation":"https://pith.science/pith/HMCCESUYL25VDSESWRMOWXJJ7X/action/citation_signature","submit_replication":"https://pith.science/pith/HMCCESUYL25VDSESWRMOWXJJ7X/action/replication_record"}},"created_at":"2026-07-05T09:37:43.006824+00:00","updated_at":"2026-07-05T09:37:43.006824+00:00"}