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Explores Bayesian disturbance injection for robust imitation in robot learning, demonstrating its effectiveness in reducing error compounding and achieving high task achievement.
Presents the Robot Competition, where students design robots to collect waste in an arena, offering hands-on interdisciplinary learning and a friendly competition.
Explores challenges and opportunities in vision-based robotic perception, covering topics like SLAM, place recognition, event cameras, and collaborative visual intelligence.
Discusses ensuring continuous surveillance in constrained environments using control barrier functions and local sensors for robust tracking of intruders.
Delves into physical and social factors in human-robot interaction, covering topics like joint overloading torque estimation and adaptive control strategies.
