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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.
Delves into material-enabled technologies for soft and fluidic robots, covering fabrication, gripping force, spider gripper functionality, and future directions.
Delves into self-reconfigurable modular robots, discussing advantages, design concepts, planning strategies, and challenges in motion planning and search algorithms.
Presents a novel architecture for robot learning of haptic interaction, achieving robust object class estimation and enhancing haptic interaction efficiency.
