The project presents various demonstrations of pick and place tasks with the Baxter research robot, with object recognition and manipulation with objects of various shapes, sizes, and colours. It also includes trajectory and motion planning and the use of different grippers and simulation with Gazebo.
This project presents an approach to learn the location of contours and their border ownership using Structured Random Forests on event-based features. The contour detection and boundary assignment are demonstrated in a proto-segmentation application
This project shows the development of various classification tasks in static and dynamic environments with the Baxter robot based on color, using also a conveyor where pieces are transported to be classified in real time.
This project presents an FPGA architecture for the computation of visual attention based on the combination of a bottom-up saliency and a top-down task-dependent modulation streams. The target applications are ADAS (Advanced Driving Assistance Systems), video surveillance, or robotics.
Fine-grain pipelined and superscalar datapath to reach high performance at low working clock frequencies with FPGAs. The final goal is to achieve a data-throughput of one data per clock cycle. We show implementations of optical flow, disparity, and low-level local features