Adaptation to Robotic User’s Changing Needs

The need for system learning and adaptation is especially evident in human robot interaction domains. Each user has specific characteristics, preferences and needs can change over time as the user get accustomed to the system and as the health state of the user changes, both over the short term, medium term, an long term. Usable and effective to be accepted, robot system interacting with human users must be able to adapt and learn in new context and at extended time scales, in a variety of environments and contexts.

Challenge in long term learning includes the integration of multi-modal information about the user over time, in light of consistencies and changes in behavior, and unexpected experiences. Machine learning, including robot learning has been adopting increasingly principled statistical methods. However, the work has no addressed the complexities of the real world uncertain data such as noisy, inconsistent and incomplete, multimodal data about a user such as ranging from signal level information from tests, electrodes, probes, wearable devices and long term data.

The ability to interact with the user through intuitive interfaces such as gestures, speech, wands, and learn from demonstration and imitation have been topics of active research for some time. They present a novel challenge for in home long term interaction where the system is subject to user learning and habituation, as well as diminishing patience and novelty effects. Robotic learning systems have not been tested yet on truly long term studies and life long learning is not more than concept yet.

At the end, because learning systems are typically difficult to asses and analyze, it is particularly important that such personalized, adaptive technologies be equipped with intuitive visualization tools of their system state as well as the health state of the user. These challenges taking into account, an ideal adaptive, learning health care robot system would be able to predict changes in the health state of the user or patient and adjust the delivery of its services accordingly.