{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:IX2HLOEB6S4GELXHD3IL4PKR3T","short_pith_number":"pith:IX2HLOEB","schema_version":"1.0","canonical_sha256":"45f475b881f4b8622ee71ed0be3d51dcf6e8377dff3b61edf1bb988690d06858","source":{"kind":"arxiv","id":"2502.12191","version":3},"attestation_state":"computed","paper":{"title":"AnyTouch: Learning Unified Static-Dynamic Representation across Multiple Visuo-tactile Sensors","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","cs.RO"],"primary_cat":"cs.LG","authors_text":"Ao Shen, Bin Fang, Di Hu, Jiangyu Hu, Ruoxuan Feng, Tianci Gao, Wenke Xia, Yuhao Sun","submitted_at":"2025-02-15T08:33:25Z","abstract_excerpt":"Visuo-tactile sensors aim to emulate human tactile perception, enabling robots to precisely understand and manipulate objects. Over time, numerous meticulously designed visuo-tactile sensors have been integrated into robotic systems, aiding in completing various tasks. However, the distinct data characteristics of these low-standardized visuo-tactile sensors hinder the establishment of a powerful tactile perception system. We consider that the key to addressing this issue lies in learning unified multi-sensor representations, thereby integrating the sensors and promoting tactile knowledge tran"},"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":"2502.12191","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-15T08:33:25Z","cross_cats_sorted":["cs.CV","cs.RO"],"title_canon_sha256":"f99bd2a71dcef36fcc3fcd65978860952c8a7a31af5396451a4522975c607e1c","abstract_canon_sha256":"d9e27f754aaf0f52754d58d31b32cb6d9bc93d98bad20f00ec389dd5e4211418"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:42:43.691735Z","signature_b64":"Naqtr3ywc+fkJfJRZJj2imynNE2T3peUAKfRNIOBxJjRDbyAtQJxuHZTrmZT/KqUWcN5x3Rg6BCm5hbOTFLaCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"45f475b881f4b8622ee71ed0be3d51dcf6e8377dff3b61edf1bb988690d06858","last_reissued_at":"2026-07-05T10:42:43.691218Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:42:43.691218Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AnyTouch: Learning Unified Static-Dynamic Representation across Multiple Visuo-tactile Sensors","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","cs.RO"],"primary_cat":"cs.LG","authors_text":"Ao Shen, Bin Fang, Di Hu, Jiangyu Hu, Ruoxuan Feng, Tianci Gao, Wenke Xia, Yuhao Sun","submitted_at":"2025-02-15T08:33:25Z","abstract_excerpt":"Visuo-tactile sensors aim to emulate human tactile perception, enabling robots to precisely understand and manipulate objects. Over time, numerous meticulously designed visuo-tactile sensors have been integrated into robotic systems, aiding in completing various tasks. However, the distinct data characteristics of these low-standardized visuo-tactile sensors hinder the establishment of a powerful tactile perception system. We consider that the key to addressing this issue lies in learning unified multi-sensor representations, thereby integrating the sensors and promoting tactile knowledge tran"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.12191","kind":"arxiv","version":3},"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/2502.12191/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":"2502.12191","created_at":"2026-07-05T10:42:43.691285+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.12191v3","created_at":"2026-07-05T10:42:43.691285+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.12191","created_at":"2026-07-05T10:42:43.691285+00:00"},{"alias_kind":"pith_short_12","alias_value":"IX2HLOEB6S4G","created_at":"2026-07-05T10:42:43.691285+00:00"},{"alias_kind":"pith_short_16","alias_value":"IX2HLOEB6S4GELXH","created_at":"2026-07-05T10:42:43.691285+00:00"},{"alias_kind":"pith_short_8","alias_value":"IX2HLOEB","created_at":"2026-07-05T10:42:43.691285+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":16,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.25877","citing_title":"TacVerse: A Multi-Sensor Dataset and Benchmark for Cross-Sensor Vision-Based Tactile Perception","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2606.19161","citing_title":"HT-Bench: Benchmarking and Learning Dexterous Full-Hand Tactile Representations with Egocentric Vision","ref_index":50,"is_internal_anchor":false},{"citing_arxiv_id":"2606.13102","citing_title":"FTP-1: A Generalist Foundation Tactile Policy Across Tactile Sensors for Contact-Rich Manipulation","ref_index":32,"is_internal_anchor":false},{"citing_arxiv_id":"2607.01684","citing_title":"Imagining the Sense of Touch: Touch-Informed Manipulation via Imagined Tactile Representations","ref_index":17,"is_internal_anchor":false},{"citing_arxiv_id":"2606.11743","citing_title":"TacCoRL: Integrating Tactile Feedback into VLA via Simulation","ref_index":35,"is_internal_anchor":false},{"citing_arxiv_id":"2606.11637","citing_title":"TouchThinker: Scaling Tactile Commonsense Reasoning to the Open World with Large-scale Data and Action-aware Representation","ref_index":22,"is_internal_anchor":false},{"citing_arxiv_id":"2606.12069","citing_title":"Tac-DINO: Learning Vision-Tactile Features with Patch Alignment","ref_index":136,"is_internal_anchor":false},{"citing_arxiv_id":"2606.11184","citing_title":"TacForeSight: Force-Guided Tactile World Model for Contact-Rich Manipulation","ref_index":17,"is_internal_anchor":false},{"citing_arxiv_id":"2606.08765","citing_title":"RGB-S: Image-Aligned Tactile Saliency for Robust Dexterous Manipulation","ref_index":34,"is_internal_anchor":false},{"citing_arxiv_id":"2606.06281","citing_title":"Multi-Resolution Tactile Imitation Learning for Contact-Rich Robotic Manipulation","ref_index":30,"is_internal_anchor":false},{"citing_arxiv_id":"2606.31236","citing_title":"TactX: Learning Shared Tactile Representations Across Diverse Sensors","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2606.31451","citing_title":"UniTac: A Unified Multimodal Model for Cross-Sensor Tactile Understanding and Generation","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2606.29941","citing_title":"Seeing Touch from Motion: A Unified Modality-Aware Visuo-Tactile Policy with Tactile Motion Correlation","ref_index":17,"is_internal_anchor":false},{"citing_arxiv_id":"2606.28899","citing_title":"You Only Touch Once: 6-DoF Object Pose Estimation from Single Tactile Contact","ref_index":33,"is_internal_anchor":false},{"citing_arxiv_id":"2605.27154","citing_title":"Touch-R1: Reinforcing Touch Reasoning in MLLMs","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2605.17336","citing_title":"Tactile-based Multimodal Fusion in Embodied Intelligence: A Survey of Vision, Language, and Contact-Driven Paradigms","ref_index":9,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IX2HLOEB6S4GELXHD3IL4PKR3T","json":"https://pith.science/pith/IX2HLOEB6S4GELXHD3IL4PKR3T.json","graph_json":"https://pith.science/api/pith-number/IX2HLOEB6S4GELXHD3IL4PKR3T/graph.json","events_json":"https://pith.science/api/pith-number/IX2HLOEB6S4GELXHD3IL4PKR3T/events.json","paper":"https://pith.science/paper/IX2HLOEB"},"agent_actions":{"view_html":"https://pith.science/pith/IX2HLOEB6S4GELXHD3IL4PKR3T","download_json":"https://pith.science/pith/IX2HLOEB6S4GELXHD3IL4PKR3T.json","view_paper":"https://pith.science/paper/IX2HLOEB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.12191&json=true","fetch_graph":"https://pith.science/api/pith-number/IX2HLOEB6S4GELXHD3IL4PKR3T/graph.json","fetch_events":"https://pith.science/api/pith-number/IX2HLOEB6S4GELXHD3IL4PKR3T/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IX2HLOEB6S4GELXHD3IL4PKR3T/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IX2HLOEB6S4GELXHD3IL4PKR3T/action/storage_attestation","attest_author":"https://pith.science/pith/IX2HLOEB6S4GELXHD3IL4PKR3T/action/author_attestation","sign_citation":"https://pith.science/pith/IX2HLOEB6S4GELXHD3IL4PKR3T/action/citation_signature","submit_replication":"https://pith.science/pith/IX2HLOEB6S4GELXHD3IL4PKR3T/action/replication_record"}},"created_at":"2026-07-05T10:42:43.691285+00:00","updated_at":"2026-07-05T10:42:43.691285+00:00"}