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A cognitive architecture for learning in reactive environments
Previous research in machine learning has viewed the process of empirical discovery as search through a space of 'theoretical' terms. In this paper, we propose a problem space for empirical discovery, specifying six complementary operators for defining new terms that ease the statement of empirical laws. The six types of terms include: numeric attributes (such as PV/T); intrinsic properties (such as mass); composite objects (such as pairs of colliding balls); classes of objects (such as acids and alkalis); composite relations (such as chemical reactions); and classes of relations (such as combustion/oxidation). We review existing machine discovery systems in light of this framework, examining which parts of the problem space were, covered by these systems. Finally, we outline an integrated discovery system (IDS) we are constructing that includes all six of the operators and which should be able to discover a broad range of empirical laws
Learning Feedback Terms for Reactive Planning and Control
With the advancement of robotics, machine learning, and machine perception,
increasingly more robots will enter human environments to assist with daily
tasks. However, dynamically-changing human environments requires reactive
motion plans. Reactivity can be accomplished through replanning, e.g.
model-predictive control, or through a reactive feedback policy that modifies
on-going behavior in response to sensory events. In this paper, we investigate
how to use machine learning to add reactivity to a previously learned nominal
skilled behavior. We approach this by learning a reactive modification term for
movement plans represented by nonlinear differential equations. In particular,
we use dynamic movement primitives (DMPs) to represent a skill and a neural
network to learn a reactive policy from human demonstrations. We use the well
explored domain of obstacle avoidance for robot manipulation as a test bed. Our
approach demonstrates how a neural network can be combined with physical
insights to ensure robust behavior across different obstacle settings and
movement durations. Evaluations on an anthropomorphic robotic system
demonstrate the effectiveness of our work.Comment: 8 pages, accepted to be published at ICRA 2017 conferenc
Reasoning about goal-directed real-time teleo-reactive programs
The teleo-reactive programming model is a high-level approach to developing real-time systems that supports hierarchical composition and durative actions. The model is different from frameworks such as action systems, timed automata and TLA+, and allows programs to be more compact and descriptive of their intended behaviour. Teleo-reactive programs are particularly useful for implementing controllers for autonomous agents that must react robustly to their dynamically changing environments. In this paper, we develop a real-time logic that is based on Duration Calculus and use this logic to formalise the semantics of teleo-reactive programs. We develop rely/guarantee rules that facilitate reasoning about a program and its environment in a compositional manner. We present several theorems for simplifying proofs of teleo-reactive programs and present a partially mechanised method for proving progress properties of goal-directed agents. © 2013 British Computer Society
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