Robot ROSE – Tablet User Interaction

BSc/MSc Student Assignment Robot Rose

Robot ROSE is intended to work fully autonomously. Regardless, the robot should be able to be commanded locally through a tablet interface disposed on top of the robot. This interface must enable healthcare staff to command the robot to perform specific tasks (such as: go back to charging station, skip room, get me an item, report to room #, etc.). Similarly, this interface must provide information to care staff on the robot its current status, a log of its recent activities, detected irregularity, etc. Such an interface must be designed properly with the care staff in mind.

Designing the robot-user tablet interface requires an indexation of the scenario’s in which a nurse might wish to command the robot and specific commands the nurse wishes to give. In addition, an indexation must be made of the data that the robot collects during its activities and relevant information derived to be presented to care staff. Based the required robot-nurse data input (robot command) and output (relevant information) an interface must be designed. Human factors and proper user experience design must be considered.

Project output:
  • Indexation of required robot input commands from nurses;
  • Indexation of possible information the robot has collected that may be provided to the nurses;
  • Design of a robot-user interface both for care workers and clients;
  • Implementation of interface on the robot and test with care staff and clients.
  • Excitement about new technologies, eager to work on state-of-the-art technologies;
  • Independence, research appetite, enthusiastic, self-critical;
  • Sense of responsibility, duty fulfillment;
  • Eager to work in an international environment, team-player, collaborate with multiple institutions and companies, determined to dedicate time in the project, able to set priorities;
  • Basic understanding of math and programming (C++, Python, Matlab);
  • Familiarity with the Robot Operating System (ROS).
Nice to have:
  • Basic understanding of machine and deep learning, neural networks, optimization techniques (gradient descent, mini-batch gradient descent);
  • Basic understanding and/or experience with computer vision techniques (object recognition, feature detection, image classification etc.);
  • Experience with deep learning frameworks (Caffe, CMTK, Tensorflow, Pytorch, Theano, etc.) would be highly appreciated;
  • Hardware and software skills (experience with NVIDIA Cuda, Jetson, etc.).