IntelligentAutonomous SystemsLearningSequential SkillsforRobot Manipulation Tasks

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Robot Learning from Human Demonstration: Interpretation, Adaptation, and Interaction

Robot Learning from Demonstration (LfD) is a research area that focuses on how robots can learn new skills by observing how people perform various activities. As humans, we have a remarkable ability to imitate other human’s behaviors and adapt to new situations. Endowing robots with these critical capabilities is a significant but very challenging problem considering the complexity and variation of human activities in highly dynamic environments. This research focuses on how robots can learn new skills by interpreting human activities, adapting the learned skills to new situations, and naturally interacting with humans. This dissertation begins with a discussion of challenges in each of these three problems. A new unified representation approach is introduced to enable robots to simultaneously interpret the high-level semantic meanings and generalize the low-level trajectories of a broad range of human activities. An adaptive framework based on feature space decomposition is then presented for robots to not only reproduce skills, but also autonomously and efficiently adjust the learned skills to new environments that are significantly different from demonstrations. To achieve natural Human Robot Interaction (HRI), this dissertation presents a Recurrent Neural Network based deep perceptual control approach, which is capable of integrating multi-modal perception sequences with actions for robots to interact with humans in long-term tasks. Overall, by combining the above approaches, an autonomous system is created for robots to acquire important skills that can be applied to human-centered applications. Finally, this dissertation concludes with a discussion of future directions that could accelerate the upcoming technological revolution of robot learning from human demonstration

Organisation, Repräsentation und Analyse menschlicher Ganzkörperbewegung für die datengetriebene Bewegungsgenerierung bei humanoiden Robotern

Diese Arbeit präsentiert einen Ansatz zur datengetriebenen Bewegungsgenerierung für humanoide Roboter, der auf der Beobachtung und Analyse menschlicher Ganzkörperbewegungen beruht. Hierzu wird untersucht, wie erfasste Bewegungen repräsentiert, klassifiziert und in einer großskaligen Bewegungsdatenbank organisiert werden können. Die statistische Modellierung der Transitionen zwischen charakteristischen Ganzkörperposen ermöglicht im Anschluss die Generierung von Multi-Kontakt-Bewegungen

A Hybrid Approach to Recognising Activities of Daily Living from Patterns of Objects Use

Over the years the cost of providing assistance and support to the ever-increasing population of the elderly and the cognitively impaired has become an economic epidemic. Therefore, the emergence of Ambient Assisted Living (AAL) has become imperative, as it encourages independent and autonomous living by providing assistance to the end user by conducting activity and behaviour recognition. Accurate recognition of Activities of Daily Living (ADL) play an important role in providing assistance and support to the elderly and cognitively impaired. Current knowledge-driven and ontology-based techniques model object concepts from assumptions and everyday common knowledge of object used for routine activities. Modelling activities from such information can lead to incorrect recognition of particular routine activities resulting in possible failure to detect abnormal activity trends. In cases, where such prior knowledge are not available, such techniques become virtually unemployable. A significant step in the recognition of activities is the accurate discovery of the object usage for specific routine activities. This thesis presents a hybrid approach for automatic consumption of sensor data and associating object usage to routine activities using Latent Dirichlet Allocation (LDA) topic modelling. This process enables the recognition of simple activities of daily living from object usage and interactions in the home environment. In relation to this, the work in this thesis addresses the problem of discovering object usage as events and contexts describing specific routine activities, especially where they have not been predefined. The main contribution is the development of a hybrid knowledge-driven activity recognition approach which acquires the knowledge of object usage through activity-object use discovery for the accurate specification of activities and object concepts. The evaluation of the proposed approach on the Kasteren and Ordonez datasets show that it yields better results compared to existing techniques

Erkennung menschlicher Aktivitäten zur Belehrung von Robotern

Für die Verwendung im Rahmen des Programmieren durch Vormachen-Paradigmas zur Programmierung von Robotern wurde ein Ansatz zur Klassifikation und Interpretation von menschlichen Bewegungen entwickelt. Dazu wurden erweiterte Methoden zur Beobachtung von Bewegungen untersucht und eine Prozesskette entwickelt, die unter Einsatz von Hintergrundwissen Bewegungssequenzen auf Aktivitäts-abhängig geeignete Merkmale abbildet und diese zur Erkennung von Aktivitäten nutzt

International Congress of Mathematicians: 2022 July 6–14: Proceedings of the ICM 2022

Following the long and illustrious tradition of the International Congress of Mathematicians, these proceedings include contributions based on the invited talks that were presented at the Congress in 2022. Published with the support of the International Mathematical Union and edited by Dmitry Beliaev and Stanislav Smirnov, these seven volumes present the most important developments in all fields of mathematics and its applications in the past four years. In particular, they include laudations and presentations of the 2022 Fields Medal winners and of the other prestigious prizes awarded at the Congress. The proceedings of the International Congress of Mathematicians provide an authoritative documentation of contemporary research in all branches of mathematics, and are an indispensable part of every mathematical library

The early historical roots of Lee-Yang theorem

A deep and detailed historiographical analysis of a particular case study concerning the so-called Lee-Yang theorem of theoretical statistical mechanics of phase transitions, has emphasized what real historical roots underlie such a case study. To be precise, it turned out that some well-determined aspects of entire function theory have been at the primeval origins of this important formal result of statistical physics.Comment: History of Physics case study. arXiv admin note: substantial text overlap with arXiv:1106.4348, arXiv:math/0601653, arXiv:0809.3087, arXiv:1311.0596 by other author