A Biomimetic Approach based on Tactile Sensing for Stable Grasp and Manipulation using Biomechatronic Hand Prostheses and Assistive Robots

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Tactile sensing is a fundamental component of how humans interact with their environment. With the loss of a limb in the upper body, the amputees also lose their ability to explore, understand and interact with the surrounding world using their sense of touch. The active hand prostheses used by a variety of amputees are only able to provide crude control over object handling; The users cannot adjust their hand movement for grasping an object or handling a tool without closely looking at it. To increase the acceptance of artificial limbs by amputees, the users should be able to perform enhanced, intuitive motor control and simultaneously feel with their prosthetic limbs. When operating in an unknown environment, tactile sensitivity, in conjunction with other sensory sources, becomes vital for a safe and effective physical interaction and performing complex tasks. In the long-term, I aim to develop a cost-efficient biomechatronic prosthetic hand capable of stable object grasping and in-hand manipulation by incorporating tactile sensing and haptic feedback. This prosthesis will ideally help users through facilitating safe interactions with the environment in various activities of daily living. Inspired by the schemes that humans employ for grasping, handling and moving objects in their hand, this Discovery Grant will be used in a short-term window of 5 years to create a framework for object grasping and manipulation by robotic arms using tactile sensors. I hypothesize that understanding and mimicking the human way of incorporating information from the sense of touch into the process of object handling, will result in devising a more efficient planning and control approach. First, we will develop a machine learning model to learn, from demonstrations, how tactile sensitivity in non-amputee subjects works in collaboration with vision, and how it corresponds to muscle synergies creating motor actions for grasping and manipulating objects. Then, we will formulate an index to assess the stability of a grasp using tactile information without having prior knowledge about the objects and the environment. Using this index, we will subsequently design a reinforcement learning model to fine-tune the control model by learning from successful and failed attempts. Lastly, a haptic interface will be prototyped to control an assistive robotic hand using the developed AI models. At the end, we will validate the effectiveness of the proposed framework by performing grasping and manipulation tasks with both the training set and a set of unknown real-world objects. The haptic interface and control algorithm will directly benefit individuals with neuromuscular disorders and upper-limb amputation in their day-to-day activities, which will hopefully elevate Canada's place as an international leader in the field of assistive and prosthetic robotics.