{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:O3HZTNYGYVV5WDPCC3L7UPKT5A","short_pith_number":"pith:O3HZTNYG","schema_version":"1.0","canonical_sha256":"76cf99b706c56bdb0de216d7fa3d53e82055e0b8da8d98fac0265e11515d3d13","source":{"kind":"arxiv","id":"1805.08329","version":2},"attestation_state":"computed","paper":{"title":"Guided Feature Transformation (GFT): A Neural Language Grounding Module for Embodied Agents","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.LG","cs.RO"],"primary_cat":"cs.AI","authors_text":"Haichao Zhang, Haonan Yu, Wei Xu, Xiaochen Lian","submitted_at":"2018-05-22T00:16:39Z","abstract_excerpt":"Recently there has been a rising interest in training agents, embodied in virtual environments, to perform language-directed tasks by deep reinforcement learning. In this paper, we propose a simple but effective neural language grounding module for embodied agents that can be trained end to end from scratch taking raw pixels, unstructured linguistic commands, and sparse rewards as the inputs. We model the language grounding process as a language-guided transformation of visual features, where latent sentence embeddings are used as the transformation matrices. In several language-directed navig"},"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":"1805.08329","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2018-05-22T00:16:39Z","cross_cats_sorted":["cs.CL","cs.LG","cs.RO"],"title_canon_sha256":"be437b62bb8b5a27c254b00577290cf9c66cc201e5f6fd345c648cbc0c0d13ea","abstract_canon_sha256":"21f771380163fee60c32362894ff9aa9f52f8cfc96d62602250ce8281741411f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:06:31.529196Z","signature_b64":"QOj8xwD9FDKo/j+iz4IEN3JJoioE+4EiyI2j7R5i+cvLjsYF1YRXmUY+0yXwYuiLur6Iah/yJh7J6CCWid5sDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"76cf99b706c56bdb0de216d7fa3d53e82055e0b8da8d98fac0265e11515d3d13","last_reissued_at":"2026-05-18T00:06:31.528748Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:06:31.528748Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Guided Feature Transformation (GFT): A Neural Language Grounding Module for Embodied Agents","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.LG","cs.RO"],"primary_cat":"cs.AI","authors_text":"Haichao Zhang, Haonan Yu, Wei Xu, Xiaochen Lian","submitted_at":"2018-05-22T00:16:39Z","abstract_excerpt":"Recently there has been a rising interest in training agents, embodied in virtual environments, to perform language-directed tasks by deep reinforcement learning. In this paper, we propose a simple but effective neural language grounding module for embodied agents that can be trained end to end from scratch taking raw pixels, unstructured linguistic commands, and sparse rewards as the inputs. We model the language grounding process as a language-guided transformation of visual features, where latent sentence embeddings are used as the transformation matrices. In several language-directed navig"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.08329","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":""},"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":"1805.08329","created_at":"2026-05-18T00:06:31.528838+00:00"},{"alias_kind":"arxiv_version","alias_value":"1805.08329v2","created_at":"2026-05-18T00:06:31.528838+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.08329","created_at":"2026-05-18T00:06:31.528838+00:00"},{"alias_kind":"pith_short_12","alias_value":"O3HZTNYGYVV5","created_at":"2026-05-18T12:32:40.477152+00:00"},{"alias_kind":"pith_short_16","alias_value":"O3HZTNYGYVV5WDPC","created_at":"2026-05-18T12:32:40.477152+00:00"},{"alias_kind":"pith_short_8","alias_value":"O3HZTNYG","created_at":"2026-05-18T12:32:40.477152+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/O3HZTNYGYVV5WDPCC3L7UPKT5A","json":"https://pith.science/pith/O3HZTNYGYVV5WDPCC3L7UPKT5A.json","graph_json":"https://pith.science/api/pith-number/O3HZTNYGYVV5WDPCC3L7UPKT5A/graph.json","events_json":"https://pith.science/api/pith-number/O3HZTNYGYVV5WDPCC3L7UPKT5A/events.json","paper":"https://pith.science/paper/O3HZTNYG"},"agent_actions":{"view_html":"https://pith.science/pith/O3HZTNYGYVV5WDPCC3L7UPKT5A","download_json":"https://pith.science/pith/O3HZTNYGYVV5WDPCC3L7UPKT5A.json","view_paper":"https://pith.science/paper/O3HZTNYG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1805.08329&json=true","fetch_graph":"https://pith.science/api/pith-number/O3HZTNYGYVV5WDPCC3L7UPKT5A/graph.json","fetch_events":"https://pith.science/api/pith-number/O3HZTNYGYVV5WDPCC3L7UPKT5A/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/O3HZTNYGYVV5WDPCC3L7UPKT5A/action/timestamp_anchor","attest_storage":"https://pith.science/pith/O3HZTNYGYVV5WDPCC3L7UPKT5A/action/storage_attestation","attest_author":"https://pith.science/pith/O3HZTNYGYVV5WDPCC3L7UPKT5A/action/author_attestation","sign_citation":"https://pith.science/pith/O3HZTNYGYVV5WDPCC3L7UPKT5A/action/citation_signature","submit_replication":"https://pith.science/pith/O3HZTNYGYVV5WDPCC3L7UPKT5A/action/replication_record"}},"created_at":"2026-05-18T00:06:31.528838+00:00","updated_at":"2026-05-18T00:06:31.528838+00:00"}