Enabling Mechanical Intelligence through Hybrid-Stiffness Robotics
Abstract: In recent years, there has been growing recognition that the physical bodies of animals play just as crucial a role in performing complex tasks as the control signals that drive them. In the field of robotics, this has given rise to the concept of “mechanical intelligence,” whereby desired behavior is embedded directly into robotic hardware rather than relying purely on active control. Though much work remains to match the level of performance found in nature, great progress has been made by both soft and rigid roboticists, alike. In this talk, Connor McCann will present his research at the intersection of these two fields, showcasing strategies to embed intelligent behavior into both rigid robotic hands and soft wearable rehabilitative robots using a combination of experimental, theoretical, and numerical techniques. Looking toward future work, he will propose a new paradigm to achieve mechanical intelligence through “hybrid-stiffness” robots that combine soft and rigid components in tandem—much like is found in nature—to leverage the benefits of each. In this way, he hopes to push the bounds of what is possible with robotic hardware, seeking to imbue it with the greatest degree of intelligence possible to achieve more rich and complex robotic behavior than is currently feasible.
Bio: Connor McCann is a Ph.D. candidate in Mechanical Engineering at Harvard University, where he has developed soft wearable robots for shoulder joint assistance, creating new techniques for the experimental characterization, reduced-order modeling, and numerical simulation of these complex systems. He has also studied the mechanics of stingray pectoral fins, seeking to understand how their intricate skeletal structures help to optimize their swimming efficiency. Prior to working at Harvard, he completed his bachelor’s degree in Mechanical Engineering at Yale University, where he developed novel robotic hands based on parallel mechanisms to improve within-hand dexterity.