Learning by observation using Qualitative Spatial Relations

We present an approach to the problem of learning by observation in spatially-situated tasks, whereby an agent learns to imitate the behaviour of an observed expert, with no direct interaction and limited observations. The form of knowledge representation used for these observations is crucial, and we apply Qualitative Spatial-Relational representations to compress continuous, metric state-spaces into symbolic states to maximise the generalisability of learned models and minimise knowledge engineering. Our system self-configures these representations of the world to discover configurations of features most relevant to the task, and thus build good predictive models. We then show how these models can be employed by situated agents to control their behaviour, closing the loop from observation to practical implementation. We evaluate our approach in the simulated RoboCup Soccer domain and the Real-Time Strategy game Starcraft, and successfully demonstrate how a system using our approach closely mimics the behaviour of both synthetic (AI controlled) players, and also human-controlled players through observation. We further evaluate our work in Reinforcement Learning tasks in these domains, and show that our approach improves the speed at which such models can be learned

Towards Lifelong Object Learning by Integrating Situated Robot Perception and Semantic Web Mining

International audienceAutonomous robots that are to assist humans in their daily lives are required, among other things, to recognize and understand the meaning of task-related objects. However, given an open-ended set of tasks, the set of everyday objects that robots will encounter during their lifetime is not foreseeable. That is, robots have to learn and extend their knowledge about previously unknown objects on-the-job. Our approach automatically acquires parts of this knowledge (e.g., the class of an object and its typical location) in form of ranked hypotheses from the Semantic Web using contextual information extracted from observations and experiences made by robots. Thus, by integrating situated robot perception and Semantic Web mining, robots can continuously extend their object knowledge beyond perceptual models which allows them to reason about task-related objects , e.g., when searching for them, robots can infer the most likely object locations. An evaluation of the integrated system on long-term data from real office observations, demonstrates that generated hypotheses can effectively constrain the meaning of objects. Hence, we believe that the proposed system can be an essential component in a lifelong learning framework which acquires knowledge about objects from real world observations

Machine Learning from Casual Conversation

Human social learning is an effective process that has inspired many existing machine learning techniques, such as learning from observation and learning by demonstration. In this dissertation, we introduce another form of social learning, Learning from a Casual Conversation (LCC). LCC is an open-ended machine learning system in which an artificially intelligent agent learns from an extended dialog with a human. Our system enables the agent to incorporate changes into its knowledge base, based on the human\u27s conversational text input. This system emulates how humans learn from each other through a dialog. LCC closes the gap in the current research that is focused on teaching specific tasks to computer agents. Furthermore, LCC aims to provide an easy way to enhance the knowledge of the system without requiring the involvement of a programmer. This system does not require the user to enter specific information; instead, the user can chat naturally with the agent. LCC identifies the inputs that contain information relevant to its knowledge base in the learning process. LCC\u27s architecture consists of multiple sub-systems combined to perform the task. Its learning component can add new knowledge to existing information in the knowledge base, confirm existing information, and/or update existing information found to be related to the user input. %The test results indicate that the prototype was successful in learning from a conversation. The LCC system functionality was assessed using different evaluation methods. This includes tests performed by the developer, as well as by 130 human test subjects. Thirty of those test subjects interacted directly with the system and completed a survey of 13 questions/statements that asked the user about his/her experience using LCC. A second group of 100 human test subjects evaluated the dialogue logs of a subset of the first group of human testers. The collected results were all found to be acceptable and within the range of our expectations

Unsupervised Human Activity Analysis for Intelligent Mobile Robots

The success of intelligent mobile robots in daily living environments depends on their ability to understand human movements and behaviours. One goal of recent research is to understand human activities performed in real human environments from long term observation. We consider a human activity to be a temporally dynamic configuration of a person interacting with key objects within the environment that provide some functionality. This can be a motion trajectory made of a sequence of 2-dimensional points representing a person’s position, as well as more detailed sequences of high-dimensional body poses, a collection of 3-dimensional points representing body joints positions, as estimated from the point of view of the robot. The limited field of view of the robot, restricted by the limitations of its sensory modalities, poses the challenge of understanding human activities from obscured, incomplete and noisy observations. As an embedded system it also has perceptual limitations which restrict the resolution of the human activity representations it can hope to achieve. In this thesis an approach for unsupervised learning of activities implemented on an autonomous mobile robot is presented. This research makes the following novel contributions: 1) A qualitative spatial-temporal vector space encoding of human activities as observed by an autonomous mobile robot. 2) Methods for learning a low dimensional representation of common and repeated patterns from multiple encoded visual observations. In order to handle the perceptual challenges, multiple abstractions are applied to the robot’s perception data. The human observations are first encoded using a leg-detector, an upper-body image classifier, and a convolutional neural network for pose estimation, while objects within the environment are automatically segmented from a 3-dimensional point cloud representation. Central to the success of the presented framework is mapping these encodings into an abstract qualitative space in order to generalise patterns invariant to exact quantitative positions within the real world. This is performed using a number of qualitative spatial-temporal representations which capture different aspects of the relations between the human subject and the objects in the environment. The framework auto-generates a vocabulary of discrete spatial-temporal descriptors extracted from the video sequences and each observation is represented as a vector over this vocabulary. Analogously to information retrieval on text corpora we use generative probabilistic techniques to recover latent, semantically meaningful, concepts in the encoded observations in an unsupervised manner. The relatively small number of concepts discovered are defined as multinomial distributions over the vocabulary and considered as human activity classes, granting the robot a high-level understanding of visually observed complex scenes. We validate the framework using, 1) A dataset collected from a physical robot autonomously patrolling and performing tasks in an office environment during a six week deployment, and 2) a high-dimensional “full body pose” dataset captured over multiple days by a mobile robot observing a kitchen area of an office environment from multiple view points. We show that the emergent categories from our framework align well with how humans interpret behaviours andsimple activities. Our presented framework models each extended observation as a probabilistic mixture over the learned activities, meaning it can learn human activity models even when embedded in continuous video sequences without the need for manual temporal segmentation, which can be time consuming and costly. Finally, we present methods for learning such human activity models in an incremental and continuous setting using variational inference methods to update the activity distribution online. This allows the mobile robot to efficiently learn and update its models of human activity over time, discarding the raw data, allowing for life-long learning