Presents a novel architecture for robot learning of haptic interaction, achieving robust object class estimation and enhancing haptic interaction efficiency.
Explores Bayesian disturbance injection for robust imitation in robot learning, demonstrating its effectiveness in reducing error compounding and achieving high task achievement.
Explores training robots through reinforcement learning and learning from demonstration, highlighting challenges in human-robot interaction and data collection.
Explores elastomer actuators using electrostatic forces for soft robotics applications, showcasing their potential in creating efficient and controllable soft machines.
Delves into the theory of material activation, proposing a unified mathematical framework to model how multiple stimuli can produce changes at the macroscopic level.
Explores the development of a soft robotic gripper system for aerial object manipulation, emphasizing actuator selection and untethered aerial robot applications.
Explores advancements in robot learning for autonomy at scale, covering deep learning challenges, efficient architecture, benchmarking results, and societal implications.
Explores the design and manufacturing of soft materials for bioinspired robotics and bio-integrated electronics, showcasing advancements in sustainable manufacturing.
Delves into material-enabled technologies for soft and fluidic robots, covering fabrication, gripping force, spider gripper functionality, and future directions.
