The paper presents a user study characterizing the effect of vibrotactile feedback on performance and cognitive load during teleoperated robotic grasping and manipulation using an affordable bilateral system with a Leap Motion and vibrotactile glove controlling a sensorized robot hand, showing vibrotactile feedback improves teleoperation and reduces cognitive load especially for complex in-hand manipulation tasks.

The paper proposes a method using motion and tactile features to automatically segment atomic actions from teleoperated demonstrations for complex robotic tasks, provides a dataset of pick-and-place teleoperation with a dexterous hand, and shows the proposed features generalize between episodes and similar objects while tactile sensing improves activity recognition within demonstrations.

The paper presents an approach using depth sensing and tactile feedback to predict safe robotic grasps of completely unknown objects through haptic exploration and comparing grid search, standard Bayesian Optimization, and unscented Bayesian Optimization, with results showing unscented Bayesian Optimization provides higher confidence grasps with fewer exploratory observations.

The paper presents a probabilistic approach using spatio-temporal and social features to recognize human-human interaction activities, learns priors based on proxemics theory to improve classification, and provides a new public RGB-D dataset of social activities including risk situations, with results showing the proposed method merging different features and proximity priors improves precision, recall and accuracy over alternative approaches