Balancing Flexibility and Precision in Robot-assisted Feeding

Abstract

Assistive robots can empower those with mobility impairments, but they must manage the trade-off between safety, efficacy, and comfort. For some task dimensions, there is flexibility: humans can shake robot hands anywhere within reach. For others, precision is key: too hard of a handshake can lead to injury. This distinction is critical for particularly intimate tasks like feeding. A robot feeding system needs to explore when there is flexibility, optimizing for success and user preferences, while maintaining the precision necessary to avoid destroying food or harming the user. Here, we propose a hierarchical approach. We design strategies and heuristics based on user feedback to abstract away the precise dimensions of bite acquisition and transfer. We can deploy learning algorithms relatively safely the resulting curated action subspace. Within the next year, we expect this work to culminate in a week-long in-home deployment with a user and co-designer.