NSF Award
2329795
The broader impact/commercial potential of this Partnerships for Innovation - Technology Translation (PFI-TT) project consists of steps towards a more effective and robust supply chain for our economy. Recent years have highlighted the critical importance of the material handling industry to our national economy: a snarled supply chain reverberates everywhere in society. The number one challenge in this industry is that the difficult ergonomics and repetitive nature make many tasks injury-prone and unsuitable for workers. Nevertheless, automation has alleviated this problem only to a small degree: robotics has achieved only small market penetration in material handling, and one of the key reasons is the absence of critical technology pertaining to versatile and dexterous robotic material handling. Currently, flexible robotic manipulators able to handle dexterous tasks are largely lacking from the field. Developing such manipulators would both enhance our scientific understanding of motor control tasks and motor learning and have an immediate impact in increasing supply chain efficiency.<br/><br/>The proposed project aims to develop and translate such technology to the marketplace. We build on recent work from the principal investigator’s team, which demonstrated complex robotic manipulation tasks, such as large in-hand object reorientation with finger gaiting, while simultaneously securing the manipulated object. This result was achieved by combining novel exploration methods for deep reinforcement learning of motor skills with the optics-based tactile fingers previously developed by the PFI team. The project aims to further develop learning-based methods for extrinsic manipulation, where the robot uses external surfaces to impart the desired movement, a strategy that will help reduce the kinematic requirements on the hand itself. This will in turn enable hands to be mounted on commercial robot arms, resulting in a complete automation station. While the previous work informing the PFI project relied exclusively on tactile and proprioceptive sensing to demonstrate dexterity, deployment for complete tasks requires the addition of vision sensors, and thus methods for learning multimodal, visuotactile control policies for dexterous extrinsic manipulation. The PFI team will apply this research to the concrete task of automating the sortation system induction process with dexterous package re-orientation, a commonly found task in the distribution centers that are the backbone of our supply chain.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
