{"id":"W3215761265","doi":"10.1109/lra.2023.3242201","title":"Learning to Search in Task and Motion Planning With Streams","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"STREAMS; Task (project management); Motion (physics); Computer science; Artificial intelligence; Economics; Computer network; Management","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001370268,0.001299902,0.001247333,0.0009471601,0.0005654923,0.001220453,0.002064208,0.001568998,0.004210293],"category_scores_gemma":[0.006420348,0.0008898901,0.0008905177,0.001259641,0.001436732,0.003336955,0.001987925,0.002179235,0.0006276611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001483527,"about_ca_system_score_gemma":0.00165816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008076193,"about_ca_topic_score_gemma":0.01155411,"domain_scores_codex":[0.9993944,0.0001629655,0.0000441656,0.0001653675,0.0001573402,0.00007560547],"domain_scores_gemma":[0.9974688,0.001881337,0.0001442448,0.0001838302,0.0002203666,0.0001014618],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002105558,0.00009931005,0.0007656428,0.0001450594,0.00004086406,0.00007061283,0.0001087875,0.8854268,0.0006263944,0.02267069,0.002220041,0.08761517],"study_design_scores_gemma":[0.00002164347,0.00002166705,0.00003780827,0.000007440134,0.000004940794,0.000007151328,0.0000122131,0.9779524,0.0003677767,0.02110208,0.000461898,0.000002895273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03399729,0.0005802796,0.9590828,0.0004839005,0.00005710593,0.0001573645,0.0002758984,0.001869743,0.003495673],"genre_scores_gemma":[0.5795571,0.0005251236,0.4128378,0.0003967297,0.0001001101,0.0006382071,0.001013789,0.0002573198,0.00467368],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008076193,"threshold_uncertainty_score":0.01605839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01819505755356033,"score_gpt":0.2579148893678889,"score_spread":0.2397198318143286,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}