Brain-inspired visually guided grasping system

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The field of advanced robotics is expected to grow dramatically over the next decade, primarily by expanding from highly predictable factory settings to unstructured and novel situations. A key challenge in this area is enabling robots to reliably manipulate unfamiliar objects. The project goal is to develop an advanced grasping system that can sense the shape of an object, and position a robotic gripper appropriately to reliably grasp the object. This process is rapid and effortless for humans, but it has been difficult to achieve in robotics. In conventional robotic grasping, a human programs the exact movements that the robot performs. This is only useful in scenarios such as assembly lines, where it is known in advance exactly what shapes objects have, and where they will be at what time. Less-controlled environments require robots to make such determinations and choose appropriate grasp parameters automatically. The project will begin by adapting an existing state-of-the-art deep-learning system that has shown promise recently for automatic grasping (in somewhat restricted scenarios), to produce a working end-to-end system in the first six months. The system will then be extended to make more intelligent decisions about the applied grip force. In the second year, two new variations of the system will be developed and tested, with the goal of allowing the robot to approach objects from a wider variety of angles. This will allow better-quality grasps, as well as grasping for a wider variety of purposes. Finally, inspired by certain parallels between deep networks and the brain, we will systematically compare network activity in each of our networks to activity in the grasping-related areas of the monkey brain, using recent studies in the neuroscience literature. Finally, the third year of the project will develop an iteration of the system that incorporates lessons learned from the performance of earlier networks and comparisons with the primate brain. The resulting system will become a key product of Applied Brain Research. More broadly, it will contribute advanced technology to the Canadian robotics sector, and provide valuable training for HQP in this rapidly growing area. ******