Descriptive statistics. by Sergey V. Popov (6719075)

Random effects regressions: Controlling for initial placement. by Sergey V. Popov (6719075)

Panel Tobit regressions: Controlling for initial placement. by Sergey V. Popov (6719075)

OLS regressions: All top 100 authors. by Sergey V. Popov (6719075)

Experimental setup under ablation experiments. by Long Di (9977453)

“…Simulation-based experimental results demonstrate that, under complex obstacle motion scenarios, the proposed method achieves a 55.8% reduction in trajectory tracking error compared with recently proposed improved APF methods and a 41.5% decrease relative to Dynamic Movement Primitives (DMP) baselines. …”

Obstacle uniform linear motion scenarios. by Long Di (9977453)

“…Simulation-based experimental results demonstrate that, under complex obstacle motion scenarios, the proposed method achieves a 55.8% reduction in trajectory tracking error compared with recently proposed improved APF methods and a 41.5% decrease relative to Dynamic Movement Primitives (DMP) baselines. …”

Analysis of the tangential force of the obstacle. by Long Di (9977453)

“…Simulation-based experimental results demonstrate that, under complex obstacle motion scenarios, the proposed method achieves a 55.8% reduction in trajectory tracking error compared with recently proposed improved APF methods and a 41.5% decrease relative to Dynamic Movement Primitives (DMP) baselines. …”

Table of values for <i>p.</i> by Long Di (9977453)

“…Simulation-based experimental results demonstrate that, under complex obstacle motion scenarios, the proposed method achieves a 55.8% reduction in trajectory tracking error compared with recently proposed improved APF methods and a 41.5% decrease relative to Dynamic Movement Primitives (DMP) baselines. …”

Static obstacle avoidance scene setup. by Long Di (9977453)

“…Simulation-based experimental results demonstrate that, under complex obstacle motion scenarios, the proposed method achieves a 55.8% reduction in trajectory tracking error compared with recently proposed improved APF methods and a 41.5% decrease relative to Dynamic Movement Primitives (DMP) baselines. …”

Random effects regressions: Top 50 authors per field. by Sergey V. Popov (6719075)

OLS regressions: Top 50 authors per field. by Sergey V. Popov (6719075)

Random effects regressions: Ranking of publications omitting unranked publications. by Sergey V. Popov (6719075)

Quantile regression models: Ranking of university or other institution. by Sergey V. Popov (6719075)

Random effects regressions: Of authors ranked between 51-100 per field. by Sergey V. Popov (6719075)

Nested random effects regressions using all unique author observations. by Sergey V. Popov (6719075)

OLS regressions: Authors ranked from 51-100 per field. by Sergey V. Popov (6719075)

Panel Tobit regressions: Ranking of publications using IDEAS journal rankings. by Sergey V. Popov (6719075)

Random effects regressions: Ranking of publications using IDEAS journal rankings. by Sergey V. Popov (6719075)

Random effects regressions: Ranking of publications using only solo authored papers. by Sergey V. Popov (6719075)

Quantile regression coefficient associated with the effect of rankings of <i>Almae matres</i> on journal-publication rankings. by Sergey V. Popov (6719075)