{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:3DYWE2TCI73CQY5AVLJHRT7W2Y","short_pith_number":"pith:3DYWE2TC","schema_version":"1.0","canonical_sha256":"d8f1626a6247f62863a0aad278cff6d617b458dc3e9b3d4a432c618a7e814bec","source":{"kind":"arxiv","id":"2503.02834","version":1},"attestation_state":"computed","paper":{"title":"MuBlE: MuJoCo and Blender simulation Environment and Benchmark for Task Planning in Robot Manipulation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Jan Kristof Behrens, Karla Stepanova, Krystian Mikolajczyk, Matej Hoffmann, Michal Nazarczuk","submitted_at":"2025-03-04T17:57:35Z","abstract_excerpt":"Current embodied reasoning agents struggle to plan for long-horizon tasks that require to physically interact with the world to obtain the necessary information (e.g. 'sort the objects from lightest to heaviest'). The improvement of the capabilities of such an agent is highly dependent on the availability of relevant training environments. In order to facilitate the development of such systems, we introduce a novel simulation environment (built on top of robosuite) that makes use of the MuJoCo physics engine and high-quality renderer Blender to provide realistic visual observations that are al"},"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":"2503.02834","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-03-04T17:57:35Z","cross_cats_sorted":[],"title_canon_sha256":"2d2def22d8882f87253f0c75caaf1da6b0b3d5e5907f2a154a36fcbd080feeb7","abstract_canon_sha256":"83b3b22c7e57e2b54fe7623c1773f6bff7eaeaad1149b15d3cefbec63471355f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:24:14.925704Z","signature_b64":"ZAoPtInof06em48FXmzvlr16SZSECJTSZuEg8FYtdy42mwhYbhvO/ZWA6GkgwrJvdBh0Y2fZezYEKMg0OkIMAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d8f1626a6247f62863a0aad278cff6d617b458dc3e9b3d4a432c618a7e814bec","last_reissued_at":"2026-07-05T10:24:14.925183Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:24:14.925183Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MuBlE: MuJoCo and Blender simulation Environment and Benchmark for Task Planning in Robot Manipulation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Jan Kristof Behrens, Karla Stepanova, Krystian Mikolajczyk, Matej Hoffmann, Michal Nazarczuk","submitted_at":"2025-03-04T17:57:35Z","abstract_excerpt":"Current embodied reasoning agents struggle to plan for long-horizon tasks that require to physically interact with the world to obtain the necessary information (e.g. 'sort the objects from lightest to heaviest'). The improvement of the capabilities of such an agent is highly dependent on the availability of relevant training environments. In order to facilitate the development of such systems, we introduce a novel simulation environment (built on top of robosuite) that makes use of the MuJoCo physics engine and high-quality renderer Blender to provide realistic visual observations that are al"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.02834","kind":"arxiv","version":1},"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/2503.02834/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":"2503.02834","created_at":"2026-07-05T10:24:14.925251+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.02834v1","created_at":"2026-07-05T10:24:14.925251+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.02834","created_at":"2026-07-05T10:24:14.925251+00:00"},{"alias_kind":"pith_short_12","alias_value":"3DYWE2TCI73C","created_at":"2026-07-05T10:24:14.925251+00:00"},{"alias_kind":"pith_short_16","alias_value":"3DYWE2TCI73CQY5A","created_at":"2026-07-05T10:24:14.925251+00:00"},{"alias_kind":"pith_short_8","alias_value":"3DYWE2TC","created_at":"2026-07-05T10:24:14.925251+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.27886","citing_title":"Tabero: Learning Gentle Manipulation with Closed-Loop Force Feedback from Vision, Touch, and Language","ref_index":10,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3DYWE2TCI73CQY5AVLJHRT7W2Y","json":"https://pith.science/pith/3DYWE2TCI73CQY5AVLJHRT7W2Y.json","graph_json":"https://pith.science/api/pith-number/3DYWE2TCI73CQY5AVLJHRT7W2Y/graph.json","events_json":"https://pith.science/api/pith-number/3DYWE2TCI73CQY5AVLJHRT7W2Y/events.json","paper":"https://pith.science/paper/3DYWE2TC"},"agent_actions":{"view_html":"https://pith.science/pith/3DYWE2TCI73CQY5AVLJHRT7W2Y","download_json":"https://pith.science/pith/3DYWE2TCI73CQY5AVLJHRT7W2Y.json","view_paper":"https://pith.science/paper/3DYWE2TC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.02834&json=true","fetch_graph":"https://pith.science/api/pith-number/3DYWE2TCI73CQY5AVLJHRT7W2Y/graph.json","fetch_events":"https://pith.science/api/pith-number/3DYWE2TCI73CQY5AVLJHRT7W2Y/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3DYWE2TCI73CQY5AVLJHRT7W2Y/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3DYWE2TCI73CQY5AVLJHRT7W2Y/action/storage_attestation","attest_author":"https://pith.science/pith/3DYWE2TCI73CQY5AVLJHRT7W2Y/action/author_attestation","sign_citation":"https://pith.science/pith/3DYWE2TCI73CQY5AVLJHRT7W2Y/action/citation_signature","submit_replication":"https://pith.science/pith/3DYWE2TCI73CQY5AVLJHRT7W2Y/action/replication_record"}},"created_at":"2026-07-05T10:24:14.925251+00:00","updated_at":"2026-07-05T10:24:14.925251+00:00"}