{"id":"W4285102651","doi":"10.1109/icra46639.2022.9811994","title":"Looking for Trouble: Informative Planning for Safe Trajectories with Occlusions","year":2022,"lang":"en","type":"article","venue":"2022 International Conference on Robotics and Automation (ICRA)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada); University of Waterloo","funders":"Huawei Technologies","keywords":"Trajectory; Computer science; Motion planning; Task (project management); Probabilistic logic; Artificial intelligence; Information gain; Path (computing); Collision avoidance; Motion (physics); Machine learning; Computer vision; Collision; Robot; Computer security; Engineering","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":[0.0003604431,0.0001706868,0.0001820579,0.0002322926,0.0007488764,0.0003008293,0.000559388,0.00003546792,0.00003617001],"category_scores_gemma":[0.00007792863,0.0001610078,0.00004803189,0.0001903657,0.00003781707,0.0004619894,0.0002268502,0.0001818199,0.000001890653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001244953,"about_ca_system_score_gemma":0.0001931658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005664368,"about_ca_topic_score_gemma":0.000001142732,"domain_scores_codex":[0.9986113,0.00003840411,0.000303485,0.0003175014,0.0004974506,0.0002318084],"domain_scores_gemma":[0.9988948,0.0003072279,0.0002678727,0.0001808809,0.000284086,0.00006515821],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007009137,0.00006276588,0.0002995527,0.00002437982,0.0000762596,0.000005151295,0.003269308,0.4303087,0.0001355393,0.5585495,0.001156111,0.006042629],"study_design_scores_gemma":[0.0009496167,0.000484506,0.001238785,0.00005822073,0.00001195724,0.00002806186,0.0009122859,0.9888629,0.00009811774,0.00577575,0.001360244,0.000219527],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001851002,0.00001742623,0.990845,0.00371152,0.0008818888,0.0005143615,0.0001416336,0.0001459412,0.001891231],"genre_scores_gemma":[0.5817318,0.000006910487,0.4154889,0.0007457503,0.0001189612,0.0005017389,0.0003433049,0.00002159926,0.001041061],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5798808,"threshold_uncertainty_score":0.6565709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03984109665198412,"score_gpt":0.2968488023453995,"score_spread":0.2570077056934154,"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."}}