{"id":"W4362597980","doi":"10.48550/arxiv.2304.00119","title":"End-to-end deep learning-based framework for path planning and collision checking: bin picking application","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Mitacs","keywords":"Waypoint; Motion planning; Computer science; Path (computing); Collision; Real-time computing; Distributed computing; Robot; Artificial intelligence; Simulation; Mathematical optimization; Mathematics; Computer network","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002879181,0.0003009666,0.0003046714,0.0004131487,0.0002585858,0.0001069574,0.0002558754,0.0004073615,0.00001758829],"category_scores_gemma":[0.0001566618,0.0003979137,0.0001098523,0.0004814334,0.00002930483,0.00008715577,0.0002038376,0.0008252506,0.00003861368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001979101,"about_ca_system_score_gemma":0.00003019879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000361644,"about_ca_topic_score_gemma":0.0000121047,"domain_scores_codex":[0.998642,0.00005642524,0.0002143181,0.0006707651,0.00008944137,0.000327021],"domain_scores_gemma":[0.9989093,0.0003761247,0.000141672,0.0003433296,0.00007485517,0.000154748],"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":[0.00002744292,0.000008357104,0.01535335,0.0002190754,0.00003988519,0.00001537713,0.0004228647,0.9793101,0.00008574023,0.003820648,0.00003529291,0.000661882],"study_design_scores_gemma":[0.000309659,0.0000341296,0.01084335,0.0003548008,0.00005972233,4.960141e-7,0.0002652369,0.9837181,0.00009864128,0.002130107,0.001774817,0.0004109464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1490496,0.00006545486,0.8491964,0.00003719992,0.0002741177,0.0005011873,0.000003238463,0.0007509243,0.0001218582],"genre_scores_gemma":[0.9946384,0.00003332791,0.004673664,0.00003538369,0.000133501,0.00001269052,0.0001472252,0.00009890787,0.0002268818],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8455889,"threshold_uncertainty_score":0.9998473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0885458441330636,"score_gpt":0.2246059893841327,"score_spread":0.1360601452510691,"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."}}