{"id":"W4414418856","doi":"10.23977/jaip.2025.080313","title":"Prioritized Reward of Deep Reinforcement Learning Applied Mobile Manipulation Reaching Tasks","year":2025,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Reinforcement learning; Task (project management); Function (biology); Mobile robot; Mobile manipulator; Base (topology); Robot","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001062347,0.0007311266,0.0006520831,0.0002404401,0.0001714044,0.000425941,0.0007633825,0.0006699622,0.001222902],"category_scores_gemma":[0.003013824,0.0002272233,0.0001916642,0.0001785739,0.0004648713,0.0006416674,0.0006165795,0.0007071496,0.0001620334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007724834,"about_ca_system_score_gemma":0.0007094591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001732828,"about_ca_topic_score_gemma":0.001525121,"domain_scores_codex":[0.9996279,0.0001209203,0.00001956343,0.00006097922,0.0001006536,0.00006989382],"domain_scores_gemma":[0.9990115,0.0004965367,0.0001293961,0.00006323855,0.0002094006,0.00008986503],"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":[0.0002771396,0.0001694761,0.001278754,0.0001080543,0.0000356609,0.0000908501,0.00004864456,0.9035019,0.01104728,0.006680314,0.0004734632,0.07628851],"study_design_scores_gemma":[0.000008194081,0.00006743652,0.0001372095,0.000002699229,0.000003047772,0.000007570116,0.000002167985,0.9976164,0.0008872187,0.001185186,0.0000802326,0.00000268768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1890616,0.0004132055,0.8068625,0.0002525467,0.00005800957,0.0000733903,0.00002990728,0.0005988608,0.002649951],"genre_scores_gemma":[0.9743688,0.00005633725,0.02432414,0.00004672994,0.000009491863,0.00003986091,0.0000163538,0.00002116158,0.001117086],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001732828,"threshold_uncertainty_score":0.005618274,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03294986184125855,"score_gpt":0.3167157270523156,"score_spread":0.2837658652110571,"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."}}