The Advanced Educational Robot

Existing literature in the field of computer science education clearly demonstrates that robots can be ideal teaching tools for basic computer science concepts. Likewise, robots are an ideal platform for more complicated CS techniques such as evolutionary algorithms and neural networks. With these two distinct roles in mind, that of the teaching tool and that of the research tool, in collaboration with customers in the CS department we have developed a new robotics platform suitable for both roles that provides higher performance and improved ease-of-use in comparison to the robots currently in use at Union. We have successfully designed and built a medium-sized robotics platform for classroom and research use that provides better maneuverability, increased flexibility, and is easier to use than commercial equivalents at significantly lower cost. In particular, our robot provides a platform with human-level mobility suitable for use in human-machine interaction (HMI) research and testing. Using a combination of easily available off-the-shelf parts, newly available sensors, and open-source software, we have built a platform that is both easy enough for beginners to use but also powerful enough for advanced users to customize and adapt to their specific needs

Enabling Motion Planning and Execution for Tasks Involving Deformation and Uncertainty

A number of outstanding problems in robotic motion and manipulation involve tasks where degrees of freedom (DoF), be they part of the robot, an object being manipulated, or the surrounding environment, cannot be accurately controlled by the actuators of the robot alone. Rather, they are also controlled by physical properties or interactions - contact, robot dynamics, actuator behavior - that are influenced by the actuators of the robot. In particular, we focus on two important areas of poorly controlled robotic manipulation: motion planning for deformable objects and in deformable environments; and manipulation with uncertainty. Many everyday tasks we wish robots to perform, such as cooking and cleaning, require the robot to manipulate deformable objects. The limitations of real robotic actuators and sensors result in uncertainty that we must address to reliably perform fine manipulation. Notably, both areas share a common principle: contact, which is usually prohibited in motion planners, is not only sometimes unavoidable, but often necessary to accurately complete the task at hand. We make four contributions that enable robot manipulation in these poorly controlled tasks: First, an efficient discretized representation of elastic deformable objects and cost function that assess a ``cost of deformation\u27 for a specific configuration of a deformable object that enables deformable object manipulation tasks to be performed without physical simulation. Second, a method using active learning and inverse-optimal control to build these discretized representations from expert demonstrations. Third, a motion planner and policy-based execution approach to manipulation with uncertainty which incorporates contact with the environment and compliance of the robot to generate motion policies which are then adapted during execution to reflect actual robot behavior. Fourth, work towards the development of an efficient path quality metric for paths executed with actuation uncertainty that can be used inside a motion planner or trajectory optimizer