{"id":"W4226188445","doi":"10.1109/robio54168.2021.9739455","title":"Robot Navigation with Interaction-based Deep Reinforcement Learning","year":2021,"lang":"en","type":"article","venue":"2021 IEEE International Conference on Robotics and Biomimetics (ROBIO)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Research and Development; Fundamental Research Funds for the Central Universities; Graduate Research and Innovation Projects of Jiangsu Province; National Natural Science Foundation of China","keywords":"Reinforcement learning; Computer science; Robot; Artificial intelligence; Representation (politics); Mobile robot navigation; Human–computer interaction; Robot learning; Collision avoidance; Motion planning; Mobile robot; Computer vision; Collision; Robot control; Computer security","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008349853,0.0001866769,0.0001638024,0.0001199364,0.0001014735,0.0001025716,0.0001332583,0.0001368102,0.000321881],"category_scores_gemma":[0.00001685635,0.0001778874,0.0000425915,0.0001426048,0.00008411513,0.0000745213,0.00003023197,0.0003658179,0.00004612114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001009018,"about_ca_system_score_gemma":0.00006081781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006143249,"about_ca_topic_score_gemma":0.00002128235,"domain_scores_codex":[0.9991205,0.0000224634,0.0002531346,0.0002449138,0.0001776585,0.0001813853],"domain_scores_gemma":[0.9994109,0.00005897936,0.00007169912,0.0001575579,0.0002413811,0.00005949836],"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":[0.00003272698,0.00004441047,0.0003468112,0.00002084537,0.0001262184,0.0000354494,0.00005168639,0.950176,0.02440917,0.01945799,0.00003087135,0.005267828],"study_design_scores_gemma":[0.0005109598,0.0001327771,0.0001579579,0.0001413258,0.00002947928,0.00002492872,0.0001686772,0.9397562,0.0579406,0.0003220729,0.0005732729,0.00024174],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09844632,0.0001118529,0.8766818,0.003811748,0.001868863,0.0002671241,0.00001742129,0.0003846828,0.0184102],"genre_scores_gemma":[0.9933289,0.0001655861,0.005622365,0.00009490806,0.00005839625,0.000009998675,0.0002062694,0.00002097569,0.0004925429],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8948826,"threshold_uncertainty_score":0.7254038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02102017194737839,"score_gpt":0.25149805970116,"score_spread":0.2304778877537816,"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."}}