{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002434147,0.0003488651,0.000477745,0.0003571524,0.0004082871,0.000404776,0.0007017754,0.0003578163,0.001534928],"category_scores_gemma":[0.0007190327,0.0003381591,0.0003458758,0.0002777104,0.0007276242,0.0005470432,0.0008081267,0.0004373782,0.0002264391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005369546,"about_ca_system_score_gemma":0.000805337,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005428359,"about_ca_topic_score_gemma":0.004981932,"domain_scores_codex":[0.9998529,0.00002436241,0.000005486659,0.00003235587,0.00006290087,0.00002195522],"domain_scores_gemma":[0.9997682,0.0001019428,0.0000401625,0.0000351468,0.00003732783,0.00001729651],"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":[0.0000230026,0.00001080398,0.0001849016,0.00002553461,0.000005879342,0.00005402404,0.00004735929,0.9713809,0.005802789,0.006849378,0.0001439684,0.01547146],"study_design_scores_gemma":[0.000002268047,0.0000126014,0.0000645195,0.000001572903,0.00000109388,0.000006639591,0.000005551688,0.9973156,0.0005250882,0.00189344,0.0001697844,0.000001869677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03076083,0.00004462379,0.9674388,0.00003019218,0.000005238214,0.00002387703,0.00002106787,0.0002187412,0.001456595],"genre_scores_gemma":[0.8035768,0.00008607694,0.1945173,0.0000164461,0.00000987253,0.000089306,0.00006672706,0.00005386891,0.001583529],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005428359,"threshold_uncertainty_score":0.01079357,"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."}}