Learning and Reasoning for Robot Dialog and Navigation Tasks
This work addresses robot efficiency in dialog and navigation tasks, but it appears incremental as it builds on existing techniques without claiming major breakthroughs.
The research tackled robot task completion by combining reinforcement learning and probabilistic reasoning, showing that robots improved performance by using human knowledge and learning from experience, and notably learned from navigation tasks to enhance dialog strategies.
Reinforcement learning and probabilistic reasoning algorithms aim at learning from interaction experiences and reasoning with probabilistic contextual knowledge respectively. In this research, we develop algorithms for robot task completions, while looking into the complementary strengths of reinforcement learning and probabilistic reasoning techniques. The robots learn from trial-and-error experiences to augment their declarative knowledge base, and the augmented knowledge can be used for speeding up the learning process in potentially different tasks. We have implemented and evaluated the developed algorithms using mobile robots conducting dialog and navigation tasks. From the results, we see that our robot's performance can be improved by both reasoning with human knowledge and learning from task-completion experience. More interestingly, the robot was able to learn from navigation tasks to improve its dialog strategies.