The COVID-19 pandemic has created numerous challenges in healthcare, but challenges can sometimes breed innovation. Technological innovation in particular is poised to change the way care is delivered, driving efficiency in the process. Efficiency will be key as hospitals and health systems look to recover from the initial, devastating wave of the pandemic. Ryan Hodgin, chief technology officer for IBM Global Healthcare, and Kate Huey, partner at IBM Healthcare, will speak about some of these technological innovations in their digital HIMSS21 session, "Innovation Driven Resiliency: Redefining What's Possible." The technology in question can encompass telehealth, artificial intelligence, automation, blockchain, chatbots, apps and other elements that have become mainstays of healthcare during the course of the pandemic.

A significant problem in designing mobile robot control systems involves coping with the uncertainty that arises in moving about in an unknown or partially unknown environment and relying on noisy or ambiguous sensor data to acquire knowledge about that environment. We describe a control system that chooses what activity to engage in next on the basis of expectations about how the information re- turned as a result of a given activity will improve 2 its knowledge about the spatial layout of its environment. Certain of the higher-level components of the control system are specified in terms of probabilistic decision models whose output is used to mediate the behavior of lower-level control components responsible for movement and sensing.

A significant problem in designing mobile robot control systems involves coping with the uncertainty that arises in moving about in an unknown or partially unknown environment and relying on noisy or ambiguous sensor data to acquire knowledge about that environment. We describe a control system that chooses what activity to engage in next on the basis of expectations about how the information returned as a result of a given activity will improve its knowledge about the spatial layout of its environment. Certain of the higher-level components of the control system are specified in terms of probabilistic decision models whose output is used to mediate the behavior of lower-level control components responsible for movement and sensing. The control system is capable of directing the behavior of the robot in the exploration and mapping of its environment, while attending to the real-time requirements of navigation and obstacle avoidance.