We describe the effective use of online learning to enhance the conversational capabilities of a concierge robot that we have been developing over the last two years. The robot was designed to interact naturally with visitors and uses a speech recognition system in conjunction with a natural language classifier. The online learning component monitors interactions and collects explicit and implicit user feedback from a conversation and feeds it back to the classifier in the form of new class instances and adjusted threshold values for triggering the classes. In addition, it enables a trusted master to teach it new question-answer pairs via question-answer paraphrasing, and solicits help with maintaining question-answer-class relationships when needed, obviating the need for explicit programming. The system has been completely implemented and demonstrated using the SoftBank Robotics humanoid robots Pepper and NAO, and the telepresence robot known as Double from Double Robotics.

The Cell Broadband Engine (Cell/B.E.) is a novel multi-core microprocessor designed to provide high-performance processing capabilities for a wide range of applications. In this paper, we describe the world's first JPEG 2000 and Motion JPEG 2000 encoder on Cell/B.E. Novel parallelization techniques for a Motion JPEG 2000 encoder that unleash the performance of the Cell/B.E. are proposed. Our Motion JPEG 2000 encoder consists of multiple video frame encoding servers on a cluster system for high-level parallelization. Each video frame encoding server runs on a heterogeneous multi-core Cell/B.E. processor, and utilizes its 8 Synergistic Processor Elements (SPEs) for low-level parallelization of the time consuming parts of the JPEG 2000 encoding process, such as the wavelet transform, the bit modeling, and the arithmetic coding. The effectiveness of high-level parallelization by the cluster system is also described, not only for the parallel encoding, but also for scalable performance improvement for real-time encoding and future enhancements. We developed all of the code from scratch for effective multilevel parallelization. Our results show that the Cell/B.E. is extremely efficient for this workload compared with commercially available processors, and thus we conclude that the Cell/B.E. is quite suitable for encoding next generation large pixel formats, such as 4K/2K-Digital Cinema.