Artem Molchanov

Force estimation and slip detection/classification for grip control using a biomimetic tactile sensor

We introduce and evaluate contact-based techniques to estimate tactile properties and detect manipulation events using a biomimetic tactile sensor. In particular, we estimate finger forces, and detect and classify slip events. In addition, we present a grip force controller that uses the estimation results to gently pick up objects of various weights and texture. The estimation techniques and the grip controller are experimentally evaluated on a robotic system consisting of Barrett arms and hands. Our results indicate that we are able to accurately estimate forces acting in all directions, detect the incipient slip, and classify slip with over 80% success rate.

Contact localization on grasped objects using tactile sensing

Manipulation tasks often require robots to make contact between a grasped tool and another object in the robots environment. The ability to detect and estimate the positions and directions of these contact points is crucial for monitoring the progress of the task, and detecting failures. In this paper, we present a data-driven approach for detecting and localizing contacts between a grasped object and the environment using tactile sensing. We explore framing the contact localization as both a regression and a classification problem and train neural networks accordingly to estimate the contact parameters. We also compare the neural networks with Gaussian process regression and support vector machine classification with spatio-temporal hierarchical matching pursuit feature learning. We evaluate the presented approach using hundreds of contact events on eighteen objects with different shapes, sizes and material properties. The experiments show that the neural network approach can learn to localize contact events for individual objects with a mean absolute error of less than 2.5 cm for the positions and less than 10° for the directions.

Active drifters: Towards a practical multi-robot system for ocean monitoring

We propose a method for controlling multiple active drifters in the presence of external forcing induced by the ocean. Our active drifters have one actuator: they can lower and raise their drogues in depth. By exploiting the vertically stratified nature of ocean currents, we show how classical multi-robot tasks (spreading out and aggregation) can be accomplished by the multi-drifter system. Tests with a realistic simulation based on an ocean model suggest that a practical implementation of active drifters which aggregate and disperse in the coastal ocean could be realized through our control method with relatively inexpensive components. Specifically, we are able to show that over a 90 day deployment a significant fraction of drifters can be made to aggregate in few clusters suitable for recovery.