Class-incremental lifelong object learning for domestic robots

Traditionally, robots have been confined to settings where they operate in isolation and in highly controlled and structured environments to execute well-defined non-varying tasks. As a result, they usually operate without the need to perceive their surroundings or to adapt to changing stimuli. However, as robots start to move towards human-centred environments and share the physical space with people, there is an urgent need to endow them with the flexibility to learn and adapt given the changing nature of the stimuli they receive and the evolving requirements of their users. Standard machine learning is not suitable for these types of applications because it operates under the assumption that data samples are independent and identically distributed, and requires access to all the data in advance. If any of these assumptions is broken, the model fails catastrophically, i.e., either it does not learn or it forgets all that was previously learned. Therefore, different strategies are required to address this problem. The focus of this thesis is on lifelong object learning, whereby a model is able to learn from data that becomes available over time. In particular we address the problem of classincremental learning with an emphasis on algorithms that can enable interactive learning with a user. In class-incremental learning, models learn from sequential data batches where each batch can contain samples coming from ideally a single class. The emphasis on interactive learning capabilities poses additional requirements in terms of the speed with which model updates are performed as well as how the interaction is handled. The work presented in this thesis can be divided into two main lines of work. First, we propose two versions of a lifelong learning algorithm composed of a feature extractor based on pre-trained residual networks, an array of growing self-organising networks and a classifier. Self-organising networks are able to adapt their structure based on the input data distribution, and learn representative prototypes of the data. These prototypes can then be used to train a classifier. The proposed approaches are evaluated on various benchmarks under several conditions and the results show that they outperform competing approaches in each case. Second, we propose a robot architecture to address lifelong object learning through interactions with a human partner using natural language. The architecture consists of an object segmentation, tracking and preprocessing pipeline, a dialogue system, and a learning module based on the algorithm developed in the first part of the thesis. Finally, the thesis also includes an exploration into the contributions that different preprocessing operations have on performance when learning from both RGB and Depth images.James Watt Scholarshi

An Ensemble Model with Ranking for Social Dialogue

Open-domain social dialogue is one of the long-standing goals of Artificial Intelligence. This year, the Amazon Alexa Prize challenge was announced for the first time, where real customers get to rate systems developed by leading universities worldwide. The aim of the challenge is to converse "coherently and engagingly with humans on popular topics for 20 minutes". We describe our Alexa Prize system (called 'Alana') consisting of an ensemble of bots, combining rule-based and machine learning systems, and using a contextual ranking mechanism to choose a system response. The ranker was trained on real user feedback received during the competition, where we address the problem of how to train on the noisy and sparse feedback obtained during the competition.Comment: NIPS 2017 Workshop on Conversational A

Continual Lifelong Learning with Neural Networks: A Review

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 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. 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

Incrementally learning semantic attributes through dialogue interaction

Enabling a robot to properly interact with users plays a key role in the effective deployment of robotic platforms in domestic environments. Robots must be able to rely on interaction to improve their behaviour and adaptively understand their operational world. Semantic mapping is the task of building a representation of the environment, that can be enhanced through interaction with the user. In this task, a proper and effective acquisition of semantic attributes of targeted entities is essential for the task accomplishment itself. In this paper, we focus on the problem of learning dialogue policies to support semantic attribute acquisition, so that the effort required by humans in providing knowledge to the robot through dialogue is minimized. To this end, we design our Dialogue Manager as a multi-objective Markov Decision Process, solving the optimisation problem through Reinforcement Learning. The Dialogue Manager interfaces with an online incremental visual classifier, based on a Load-Balancing Self-Organizing Incremental Neural Network (LB-SOINN). Experiments in a simulated scenario show the effectiveness of the proposed solution, suggesting that perceptual information can be properly exploited to reduce human tutoring cost. Moreover, a dialogue policy trained on a small amount of data generalises well to larger datasets, and so the proposed online scheme, as well as the real-time nature of the processing, are suited for an extensive deployment in real scenarios. To this end, this paper provides a demonstration of the complete system on a real robot