Nancie Gunson, Daniel Hernandez Garcia, Weronika Sieińska, Angus Addlesee, Christian Dondrup, Oliver Lemon, Jose L. Part, Yanchao Yu. Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue. 2022.

We will demonstrate a prototype multimodal conversational AI system that will act as a receptionist in a hospital waiting room, combining visually-grounded dialogue with social conversation. The system supports visual object conversation in the waiting room (e.g. looking for available seats or personal belongings), task-based dialogues regarding navigation and check-in procedures in the hospital, as well as access to the latest news, and a quiz game about coronavirus. The prototype system therefore demonstrates how to weave together a wide range of natural, daily conversations with end users that vary in complexity; from complex visual dialogue to chitchat and quiz games, to task-oriented domain-specific conversations. We are currently able to demonstrate the system via a web-based interface. It will soon be deployed on the ARI robot in a hospital waiting room.

The ability to acquire knowledge incrementally and after deployment is of utmost importance for robots operating in the real world. Moreover, robots that have to operate alongside people need to be able to interact in a way that is intuitive for the users, e.g., by understanding and producing natural language. In this paper we present a first prototype of a robot architecture developed for situated lifelong object learning. The system is able to communicate with its users through natural language and perform object learning and recognition on the spot through situated interactions. In this first stage, we evaluate the system in terms of recognition accuracy which gives an indirect measure of the quality of the collected data with the proposed pipeline. Our results show that the robot can use this data for both learning and recognition with acceptable incremental performance. We also discuss limitations and steps that are necessary in order to improve performance as well as to shed some light on system usability.

Humans and animals have the ability to continually acquire, fine-tune, and transfer knowledge and skills throughout their lifespan. This ability, referred to as lifelong learning, is mediated by a rich set of neurocognitive mechanisms that together contribute to the development and specialization of our sensorimotor skills as well as to long-term memory consolidation and retrieval. Consequently, lifelong learning capabilities are crucial for computational learning systems and autonomous agents interacting in the real world and processing continuous streams of information. However, lifelong learning remains a long-standing challenge for machine learning and neural network models since the continual acquisition of incrementally available information from non-stationary data distributions generally leads to catastrophic forgetting or interference. This limitation represents a major drawback for state-of-the-art deep neural network models that typically learn representations from stationary batches of training data, thus without accounting for situations in which information becomes incrementally available over time. In this review, we critically summarize the main challenges linked to lifelong learning for artificial learning systems and compare existing neural network approaches that alleviate, to different extents, catastrophic forgetting. Although significant advances have been made in domain-specific learning with neural networks, extensive research efforts are required for the development of robust lifelong learning on autonomous agents and robots. We discuss well-established and emerging research motivated by lifelong learning factors in biological systems such as structural plasticity, memory replay, curriculum and transfer learning, intrinsic motivation, and multisensory integration.

The ability to quickly adapt to new environments and incorporate new knowledge is of great importance for robots operating in unstructured environments and interacting with non-expert users. This paper reports on our current progress in tackling this problem. We propose the development of a framework for teaching robots to perform tasks using natural language instructions, visual demonstrations and interactive dialogue. Moreover, we present a module for learning objects incrementally and on-the-fly that would enable robots to ground referents in the natural language instructions and reason about the state of the world.