Context-aware proactive and adaptive conversation for human–robot interaction

Social robots are coming into our daily life. Existing conversational robots are mostly reactive in that the interactions are usually initiated by the users. With the knowledge of the environmental context such as people’s daily activities, robots can be more intelligent and proactive. In this paper, we proposed a context-aware conversation adaptation system (CACAS) for human–robot interaction (HRI). First, a context recognition module and a language processing module are developed to obtain the context information, user intent and slots, which become part of the system state. Second, a reinforcement learning algorithm is utilized to train an initial policy in a simulated HRI environment. User feedback data is collected through HRI using the initial policy. Third, a new policy that combines the reinforcement learning-based policy and a supervised learning-based policy is adapted based on the user feedback. We conducted both simulated user tests and real human subject tests to evaluate the proposed CACAS. The results show that the CACAS achieved a success rate of 85% in the real human subject test and 87.5% of participants were satisfied with the adaptation results. For the simulation test, the CACAS had the highest success rate compared with the baseline methods.