{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:EZRI7XMNK2X5L5ZKPU4NFFFFVP","short_pith_number":"pith:EZRI7XMN","schema_version":"1.0","canonical_sha256":"26628fdd8d56afd5f72a7d38d294a5abd38477971da215fd47629d825a363d9c","source":{"kind":"arxiv","id":"2405.19885","version":2},"attestation_state":"computed","paper":{"title":"Fourier Controller Networks for Real-Time Decision-Making in Embodied Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.LG","authors_text":"Chengyang Ying, Hang Su, Hengkai Tan, Jun Zhu, Kai Ma, Songming Liu, Xingxing Zhang","submitted_at":"2024-05-30T09:43:59Z","abstract_excerpt":"Transformer has shown promise in reinforcement learning to model time-varying features for obtaining generalized low-level robot policies on diverse robotics datasets in embodied learning. However, it still suffers from the issues of low data efficiency and high inference latency. In this paper, we propose to investigate the task from a new perspective of the frequency domain. We first observe that the energy density in the frequency domain of a robot's trajectory is mainly concentrated in the low-frequency part. Then, we present the Fourier Controller Network (FCNet), a new network that uses "},"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":"2405.19885","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-30T09:43:59Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"a6bcbdd1c55c957d48ac614b77ce7dcc5c60c3960115f3f0e04b5185e8738fdb","abstract_canon_sha256":"e90e28fb23b91b398681a659eea637c572332ece46f49d1d205cf9d2cef98578"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:50:48.036653Z","signature_b64":"Yk1ZinX2R2E8wApJa5dsjX7scesrbC5lP73ZVDKRF7odfJPJxSyzg5pqz4TKvJMdM1Vg0eQxdBsK/54qa4abCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"26628fdd8d56afd5f72a7d38d294a5abd38477971da215fd47629d825a363d9c","last_reissued_at":"2026-07-05T09:50:48.036152Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:50:48.036152Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fourier Controller Networks for Real-Time Decision-Making in Embodied Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.LG","authors_text":"Chengyang Ying, Hang Su, Hengkai Tan, Jun Zhu, Kai Ma, Songming Liu, Xingxing Zhang","submitted_at":"2024-05-30T09:43:59Z","abstract_excerpt":"Transformer has shown promise in reinforcement learning to model time-varying features for obtaining generalized low-level robot policies on diverse robotics datasets in embodied learning. However, it still suffers from the issues of low data efficiency and high inference latency. In this paper, we propose to investigate the task from a new perspective of the frequency domain. We first observe that the energy density in the frequency domain of a robot's trajectory is mainly concentrated in the low-frequency part. Then, we present the Fourier Controller Network (FCNet), a new network that uses "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.19885","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/2405.19885/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":"2405.19885","created_at":"2026-07-05T09:50:48.036210+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.19885v2","created_at":"2026-07-05T09:50:48.036210+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.19885","created_at":"2026-07-05T09:50:48.036210+00:00"},{"alias_kind":"pith_short_12","alias_value":"EZRI7XMNK2X5","created_at":"2026-07-05T09:50:48.036210+00:00"},{"alias_kind":"pith_short_16","alias_value":"EZRI7XMNK2X5L5ZK","created_at":"2026-07-05T09:50:48.036210+00:00"},{"alias_kind":"pith_short_8","alias_value":"EZRI7XMN","created_at":"2026-07-05T09:50:48.036210+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.01581","citing_title":"Hyper-DP3: Frequency-Aware Right-Sizing of 3D Diffusion Policies for Visuomotor Control","ref_index":32,"is_internal_anchor":false},{"citing_arxiv_id":"2605.01581","citing_title":"Hyper-DP3: Frequency-Aware Right-Sizing of 3D Diffusion Policies for Visuomotor Control","ref_index":32,"is_internal_anchor":false},{"citing_arxiv_id":"2605.01581","citing_title":"Hyper-DP3: Frequency-Aware Right-Sizing of 3D Diffusion Policies for Visuomotor Control","ref_index":28,"is_internal_anchor":false},{"citing_arxiv_id":"2605.01581","citing_title":"Hyper-DP3: Frequency-Aware Right-Sizing of 3D Diffusion Policies for Visuomotor Control","ref_index":28,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EZRI7XMNK2X5L5ZKPU4NFFFFVP","json":"https://pith.science/pith/EZRI7XMNK2X5L5ZKPU4NFFFFVP.json","graph_json":"https://pith.science/api/pith-number/EZRI7XMNK2X5L5ZKPU4NFFFFVP/graph.json","events_json":"https://pith.science/api/pith-number/EZRI7XMNK2X5L5ZKPU4NFFFFVP/events.json","paper":"https://pith.science/paper/EZRI7XMN"},"agent_actions":{"view_html":"https://pith.science/pith/EZRI7XMNK2X5L5ZKPU4NFFFFVP","download_json":"https://pith.science/pith/EZRI7XMNK2X5L5ZKPU4NFFFFVP.json","view_paper":"https://pith.science/paper/EZRI7XMN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.19885&json=true","fetch_graph":"https://pith.science/api/pith-number/EZRI7XMNK2X5L5ZKPU4NFFFFVP/graph.json","fetch_events":"https://pith.science/api/pith-number/EZRI7XMNK2X5L5ZKPU4NFFFFVP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EZRI7XMNK2X5L5ZKPU4NFFFFVP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EZRI7XMNK2X5L5ZKPU4NFFFFVP/action/storage_attestation","attest_author":"https://pith.science/pith/EZRI7XMNK2X5L5ZKPU4NFFFFVP/action/author_attestation","sign_citation":"https://pith.science/pith/EZRI7XMNK2X5L5ZKPU4NFFFFVP/action/citation_signature","submit_replication":"https://pith.science/pith/EZRI7XMNK2X5L5ZKPU4NFFFFVP/action/replication_record"}},"created_at":"2026-07-05T09:50:48.036210+00:00","updated_at":"2026-07-05T09:50:48.036210+00:00"}