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This paper addresses adaptive control architectures for systems that respond autonomously to changing tasks. Such systems often have many sensory and motor alternatives and behavior drawn from these produces varying quality solutions. The objective is then to ground behavior in control laws which, combined with resources , enumerate closed-loop behavioral(More)
Citation Hsiao, Kaijen et al. " Reactive grasping using optical proximity sensors. Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story(More)
This paper describes research towards a system for locating wireless nodes in a home environment requiring merely a single access point. The only sensor reading used for the location estimation is the received signal strength indication (RSSI) as given by an RF interface, e.g., Wi-Fi. Wireless signal strength maps for the positioning filter are obtained by(More)
This paper describes a probabilistic approach to global localization within an indoor environment with minimum infrastructure requirements. Global localization is a flavor of localization in which the device is unaware of its initial position and has to determine the same from scratch. Localization is performed based on the Received Signal Strength(More)
Autonomous robot systems operating in an uncertain environment have to be reactive and adaptive in order to cope with changing environment conditions and task requirements. To achieve this, the hybrid control architecture presented in this paper uses reinforcement learning on top of a Discrete Event Dynamic System (DEDS) framework to learn to supervise a(More)
T HE GOAL OF THIS PROJECT is to develop a unique simulation environment that can be used to increase students' in terest and expertise in Computer Science curriculum. Hands-on experience with physical or simulated equipment is an essential ingredient for learning, but many approaches to training develop a separate piece of equipment or software for each(More)
Autonomous robots hold the possibility of performing a variety of assistive tasks in intelligent environments. However, widespread use of robot assistants in these environments requires ease of use by individuals who are generally not skilled robot operators. In this paper we present a method of training robots that bridges the gap between user programming(More)
This paper presents a distributed control approach to legged locomotion that constructs behavior on-line by activating combinations of reusable feedback control laws drawn from a control basis. Sequences of such controller activations result in exible aperiodic step sequences based on local sensory information. Different tasks are achieved by varying the(More)
Reinforcement learning addresses the problem of learning to select actions in order to maximize an agent's performance in unknown environments. To scale reinforcement learning to complex real-world tasks, agent must be able to discover hierarchical structures within their learning and control systems. This paper presents a method by which a reinforcement(More)