{"id":"W3160323480","doi":"10.1017/s0263574722000777","title":"Stability-constrained mobile manipulation planning on rough terrain","year":2022,"lang":"en","type":"article","venue":"Robotica","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Motion planning; Terrain; Zero moment point; Computer science; Kinematics; Mobile robot; Stability (learning theory); Control theory (sociology); Mobile manipulator; Trajectory; Robot; Constraint (computer-aided design); Point (geometry); Robotics; Control engineering; Artificial intelligence; Humanoid robot; Engineering; Control (management); Mathematics","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":[0.0006943344,0.0001821908,0.0002066537,0.000134275,0.0004838552,0.0001063263,0.0008337277,0.00004688676,0.000133408],"category_scores_gemma":[0.00008274193,0.0001946571,0.00007121434,0.0004539726,0.00004457621,0.0002311293,0.0003936014,0.0004165751,0.00006403896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000200847,"about_ca_system_score_gemma":0.00009287138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001286083,"about_ca_topic_score_gemma":1.951576e-7,"domain_scores_codex":[0.9978353,0.0002726213,0.0003064208,0.0005801029,0.0005831431,0.0004224154],"domain_scores_gemma":[0.9987119,0.0002912364,0.0001109301,0.0007452911,0.00003223578,0.0001083791],"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":[0.000009899171,0.0001261923,0.0006149667,0.000007992895,0.00001319201,0.00009502775,0.002919885,0.9792634,0.0003117484,0.0114552,0.0003925176,0.004789974],"study_design_scores_gemma":[0.0003913347,0.0005053813,0.003462555,0.00002152941,0.00000696507,0.00007887656,0.000343578,0.9919729,0.0001989957,0.001779902,0.0009354887,0.000302528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01675997,0.00005873715,0.9727712,0.001170102,0.001074157,0.0005207919,0.00001071996,0.0006360813,0.006998252],"genre_scores_gemma":[0.8670183,2.46154e-7,0.1321586,0.0004243001,0.00007345376,0.0001139946,0.00002128486,0.00001639052,0.000173481],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8502583,"threshold_uncertainty_score":0.7937887,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03974482111340216,"score_gpt":0.2764529600934079,"score_spread":0.2367081389800057,"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."}}