Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey

Reinforcement learning (RL) is a popular paradigm for addressing sequential decision tasks in which the agent has only limited environmental feedback. Despite many advances over the past three decades, learning in many domains still requires a large amount of interaction with the environment, which can be prohibitively expensive in realistic scenarios. To address this problem, transfer learning has been applied to reinforcement learning such that experience gained in one task can be leveraged when starting to learn the next, harder task. More recently, several lines of research have explored how tasks, or data samples themselves, can be sequenced into a curriculum for the purpose of learning a problem that may otherwise be too difficult to learn from scratch. In this article, we present a framework for curriculum learning (CL) in reinforcement learning, and use it to survey and classify existing CL methods in terms of their assumptions, capabilities, and goals. Finally, we use our framework to find open problems and suggest directions for future RL curriculum learning research

BWIBots: A platform for bridging the gap between AI and human–robot interaction research

Recent progress in both AI and robotics have enabled the development of general purpose robot platforms that are capable of executing a wide variety of complex, temporally extended service tasks in open environments. This article introduces a novel, custom-designed multi-robot platform for research on AI, robotics, and especially human–robot interaction for service robots. Called BWIBots, the robots were designed as a part of the Building-Wide Intelligence (BWI) project at the University of Texas at Austin. The article begins with a description of, and justification for, the hardware and software design decisions underlying the BWIBots, with the aim of informing the design of such platforms in the future. It then proceeds to present an overview of various research contributions that have enabled the BWIBots to better (a) execute action sequences to complete user requests, (b) efficiently ask questions to resolve user requests, (c) understand human commands given in natural language, and (d) understand human intention from afar. The article concludes with a look forward towards future research opportunities and applications enabled by the BWIBot platform

Glycosylation site prediction using ensembles of Support Vector Machine classifiers

Article discussing the performance of different computational methods for prediction of glycosylation sites from amino acid sequences

Improving robot manipulation with data-driven object-centric models of everyday forces

© The Author(s) 2013. This article is published with open access at Springerlink.comDOI: 10.1007/s10514-013-9344-1Based on a lifetime of experience, people anticipate the forces associated with performing a manipulation task. In contrast, most robots lack common sense about the forces involved in everyday manipulation tasks. In this paper, we present data-driven methods to inform robots about the forces that they are likely to encounter when performing specific tasks. In the context of door opening, we demonstrate that data-driven object-centric models can be used to haptically recognize specific doors, haptically recognize classes of door (e.g., refrigerator vs. kitchen cabinet), and haptically detect anomalous forces while opening a door, even when opening a specific door for the first time.We also demonstrate that two distinct robots can use forces captured from people opening doors to better detect anomalous forces. These results illustrate the potential for robots to use shared databases of forces to bettermanipulate theworld and attain common sense about everyday forces

Reports on the 2017 AAAI Spring Symposium Series

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, presented the 2017 Spring Symposium Series, held Monday through Wednesday, March 27–29, 2017 on the campus of Stanford University. The eight symposia held were Artificial Intelligence for the Social Good (SS-17-01); Computational Construction Grammar and Natural Language Understanding (SS-17-02); Computational Context: Why It's Important, What It Means, and Can It Be Computed? (SS-17-03); Designing the User Experience of Machine Learning Systems (SS-17-04); Interactive Multisensory Object Perception for Embodied Agents (SS-17-05); Learning from Observation of Humans (SS-17-06); Science of Intelligence: Computational Principles of Natural and Artificial Intelligence (SS-17-07); and Wellbeing AI: From Machine Learning to Subjectivity Oriented Computing (SS-17-08). This report, compiled from organizers of the symposia, summarizes the research that took place