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Imitation learning from demonstrations has shown itself to be effective in training robots to solve a diversity of complex tasks requiring fine motor control and manipulation over low-level (LL), continuous environments. Yet, it remains a difficult endeavour to generate long-horizon plans from imitation learning alone. In contrast, high-level (HL), symbolic abstractions facilitate efficient and interpretable long-horizon planning. We propose to combine the strengths of LL imitation learning for manipulation and control, and HL symbolic abstractions for long-horizon planning. We realise this idea via \\emph{bilevel policies} of the form $(\\pi^{\\mathrm{hl}}, \\pi^{\\mathrm{ll}})$, consisting of a neural policy $\\pi^{\\mathrm{ll}}$ learned from LL demonstrations, and an HL symbolic policy $\\pi^{\\mathrm{hl}}$ that is constructed fro","title":"Learning Bilevel Policies over Symbolic World Models for Long-Horizon Planning","url":"https://arxiv.org/abs/2605.15975","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.15975v2 Announce Type: replace \nAbstract: We tackle the challenge of building embodied AI agents that can reliably solve long-horizon planning problems. Imitation learning from demonstrations has shown itself to be effective in training robots to solve a diversity of complex tasks requiring fine motor control and manipulation over low-level (LL), continuous environments. Yet, it remains a difficult endeavour to generate long-horizon plans from imitation learning alone. In contrast, high-level (HL), symbolic abstractions facilitate efficient and interpretable long-horizon planning. We propose to combine the strengths of LL imitation learning for manipulation and control, and HL symbolic abstractions for long-horizon planning. We realise this idea via \\emph{bilevel policies} of the form $(\\pi^{\\mathrm{hl}}, \\pi^{\\mathrm{ll}})$, consisting of a neural policy $\\pi^{\\mathrm{ll}}$ learned from LL demonstrations, and an HL symbolic policy $\\pi^{\\mathrm{hl}}$ that is constructed fro","title":"Learning Bilevel Policies over Symbolic World Models for Long-Horizon Planning","vendor":"arxiv_cs_ai"},"confidence":{"method":"deterministic"},"decision":"POSITIVE","issued_at":"2026-05-20T04:43:44Z","notes":"Spider vendor_press (news) news.vendor_press.v1","object":{"captured_by":"crovia.spider.vendor_press","primary_source_url":"https://arxiv.org/abs/2605.15975"},"predecessors":[],"schema":"crovia.axiom.v1","signature":"ed25519:1f7167bdf26156b66b742f304d4519dbe21197b83632dbf0213437f515e03c0db22e1634174eddd6afd8c19742654cc13f27f4a70b4ec9d1ee60a9717f7b5706","signer":"crovia.substrate","subject":{"observed_at":"2026-05-20T04:43:44Z","source_collector":"spider:vendor_press","target_id":"https://arxiv.org/abs/2605.15975"},"tsa":{"authority":"crovia.substrate.bootstrap","rfc3161_token":"{\"kind\":\"crovia.bootstrap.tsa\",\"source_jsonl\":\"/opt/crovia/spider/data/news/vendor_press_v1.jsonl\",\"source_seal_merkle_root\":\"spider_vendor_press_v1\",\"upgrade_path\":\"Sessione H \\u2014 OpenTimestamps weekly anchor\"}"},"zk_mode":"clear","zk_proof":null},"ledger":{"leaf_hash":"263b2cfbba600ce37fdd9b908908604cfbed1766f27cd772f250b9c2e7771a23","leaf_index":145237,"ledger_path":"/opt/crovia/substrate/axiom_ledger.jsonl"},"merkle_proof":{"hash_alg":"sha256","leaf_prefix":"0x00","node_prefix":"0x01","odd_leaf_rule":"duplicate_last","path":[{"sibling":"07ae6f26f12993dc8e0c5aaa6389f93269529b7c9a034e828d840f262affc870","side":"left"},{"sibling":"f3a2d17db59f67d50fd7ea8f8a4c8d0e1adc31e5f5a6935bc0b2e64201d17136","side":"right"},{"sibling":"3edfe361cbea0e180db57dc30c60cd377c5797a1c051b9c75052b81cd6a57351","side":"left"},{"sibling":"6dbfee39346753040083e62be48144fe3c07540250d5d7fecc0a087e2d6cf885","side":"right"},{"sibling":"62dbd7d0462b787c75411a24ae0b6704a4f33856e7dc4b1f1544d64d03921e8c","side":"left"},{"sibling":"af09839a9327edea7ec2950bd032eab02092755e13d80c0e12d58c0b1686ab2f","side":"right"},{"sibling":"8abfb0c0cfeb30fba16eb01548a8b7ce35f077e44d727cc0fe4a62d471800c6e","side":"left"},{"sibling":"79fb6d27e8a49dd15a97fca96f52d62ff5ee7213d44a5526459d33f42a050928","side":"right"},{"sibling":"f073aa7be27ee7e1d9eb0de5f129f7e9bae0c584fb959378c395b65831dc1d1d","side":"left"},{"sibling":"1526885f19d1fadf6955cf519dbc4e62d593a4bba99d741e8c301740a7068233","side":"left"},{"sibling":"e3a7d5c07f161682d61bd453ffc02ecdf87cfeda70f986d6650017f9d2d6b265","side":"left"},{"sibling":"edbc49f08e5b92291934c05c9e6efd270a6b0698d8d2fa474006366027dfe098","side":"right"},{"sibling":"3e4df6e7457cecbf36f350375e72dcab336a3984422e4c406ef809e4e2944e96","side":"left"},{"sibling":"8f4c0fbe56b6c010fbb8c782ebcd478079bb3f991d8704e2534209a075d9163c","side":"left"},{"sibling":"be08fedc4e72a6fb56606f66812fae7317e09690b9acb18385f4ab117a981238","side":"right"},{"sibling":"0534329a7475dc9df51998c83c16892126679dade0fa34182f21e869599386c7","side":"right"},{"sibling":"2d24720928ead0e7670650eb55f558c4f20e4c18df376f47ba72cfa8cf0ed344","side":"right"},{"sibling":"d841ad93efda0869e5eb97678f348f03f5caab4353e05ff4bf18f47fb945b822","side":"left"}]},"schema":"crovia.axiom_proof.v1","seal":{"first_collector_run_id":"","first_receipt_hash":"","jsonl_path":"/opt/crovia/substrate/axiom_ledger.jsonl","key_id":"430895f101d38164","last_collector_run_id":"","last_receipt_hash":"","leaf_count":147301,"merkle_root":"08903d7159c3b38eeeeafc09eab15139ea417f1d94f02f1fbc87296b37db840a","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","run_id":"hourly_json_retrofit_20260521T183701Z","schema":"crovia.seal.v1","seal_family_version":"crovia-seal-family/1","seal_kind":"substrate_batch","sealed_at":"2026-05-21T18:37:33Z","sig_algorithm":"ed25519","signature":"905f2924632dfa2970c8690285f5b5d4a1d891d0e0ef1cbc404ebec2fd937215ac768e16f0a9f28b18977a55ae0bfd226db6833ae7ef588729054117d2da7303","signer_version":"1.1.0"},"trust_root":{"key_id":"430895f101d38164","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","signature_algorithm":"ed25519","url":"/registry/canon/TRUST_ROOT.md"},"verifier":{"spec":"/registry/canon/AXIOM_RECEIPT_v1.md","url":"/v/axm_f2ee3bb7e8c4af23ffdcc344f912fe50747596ea77b388407cce6e784c8d90df"}}