Some tasks are hard; soft robotics can help
Doomsayers have been predicting for decades that robots are coming for our jobs, but Assistant Professor Chen Feng is proving that they can actually be of great use to those who might not otherwise be able to obtain employment.
Feng — who is jointly appointed to Tandon’s Department of Civil and Urban Engineering and Department of Mechanical and Aerospace Engineering, as well as to the Center for Urban Science and Progress (CUSP) — recently won a grant from the National Science Foundation to research how soft, wearable robots could improve the future for workers with upper limb disabilities. Collaborating with Tandon co-principal investigators Vikram Kapila (mechanical and aerospace engineering) and Ludovic Righetti (mechanical and aerospace engineering; electrical and computer engineering), as well as lead colleagues from City University of New York and Rutgers New Jersey Medical School, the team is developing new perceptive and adaptive soft, wearable robots and investigating how these robots could provide physical assistance and skill training for older workers and workers with physical disabilities in jobs involving picking, placing, and assembly tasks — common in the retail, warehouse, and manufacturing sectors. (The funding was provided by the NSF’s Future of Work at the Human-Technology Frontier cross-directorate program.)
“We estimate that this technology can directly benefit nearly 20 million people in the U.S. with upper limb impairments due to neurological and musculoskeletal disorders,” Feng says. “The long-term goal of the project is to improve the quality of work, productivity, and employment opportunities of people with disabilities, who are the nation’s largest minority and untapped labor force.”
The multidisciplinary team of researchers is deploying artificial intelligence-powered, soft assistive robots to support workers and increase understanding of the resulting impact on economics and policy making, so the project has the potential to contribute to national economic growth and health. The assessment of economic impacts, they assert, will provide the first econometric data-driven understanding of the productivity and labor market effects of AI- and robotics-driven augmentation, with a specific focus on the underrepresented population of individuals with disabilities.
Concurrently, Feng is engaged in another NSF-funded project involving soft robotics, this one in collaboration with Carnegie Mellon University and aimed at developing scalable sensing and modeling methods for soft grippers. The ultimate goal is to create intelligent soft grippers — tools made with flexible materials that passively adapt to external forces, making them intrinsically safe for human use and for handling delicate objects — with significantly improved ability to handle objects in complicated environments.
Because soft materials deform easily in response to applied forces, Feng explains, it is challenging to efficiently and accurately estimate the detailed shapes of soft grippers, which is critical for their delicate control and motion planning, and he intends to address the issue using embedded cameras and deep learning algorithms. Such self-sensing soft grippers won’t be limited to a preset passive response but could actively modify their operation, giving them applicability in areas such as the food industry, agriculture, and assisted living for senior citizens or people with disabilities.
“Effective, self-sensing soft grippers could increase productivity and improve the quality of human life,” Feng says, “exactly what we hope for any robotic technology.”