Cognitive Systems Introduction

Christensen, Henrik I.; Sloman, Aaron; Kruijff, Geert-Jan M.; Wyatt, Jeremy L.

doi:10.1007/978-3-642-11694-0_1

Cited by 11 publications

(17 citation statements)

References 58 publications

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“…A new generation of robots have emerged in recent years which serve as humans' assistants and companions [9]. However, due to significantly mismatched capabilities (e.g., perception and actions) of humans and robots, natural language based communication between them will be difficult.…”

Section: Introductionmentioning

confidence: 99%

Teaching Robots New Actions through Natural Language Instructions

She

Cheng

Chai

et al. 2014

The 23rd IEEE International Symposium on Robot and Human Interactive Communication

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Robots often have limited knowledge and need to continuously acquire new knowledge and skills in order to collaborate with its human partners. To address this issue, this paper describes an approach which allows human partners to teach a robot (i.e., a robotic arm) new high-level actions through natural language instructions. In particular, built upon the traditional planning framework, we propose a representation of high-level actions that only consists of the desired goal states rather than step-by-step operations (although these operations may be specified by the human in their instructions). Our empirical results have shown that, given this representation, the robot can reply on automated planning and immediately apply the newly learned action knowledge to perform actions under novel situations.

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Section: Introductionmentioning

confidence: 99%

Teaching Robots New Actions through Natural Language Instructions

She

Cheng

Chai

et al. 2014

The 23rd IEEE International Symposium on Robot and Human Interactive Communication

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“…Pattern events occur when a network performs strings of component transitions matching patterns which are specific to the application domain. Complex patterns are also considered in research on cognitive systems [24,5,23].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Complex Active Systems with Uncertain Temporal Observations

Lamperti

Zhao

2016

Lecture Notes in Computer Science

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Complex active systems have been proposed as a formalism for modeling real dynamic systems that are organized in a hierarchy of behavioral abstractions. As such, they constitute a conceptual evolution of active systems, a class of discrete-event systems introduced into the literature two decades ago. A complex active system is a hierarchy of active systems, each one characterized by its own behavior expressed by the interaction of several communicating automata. The interaction between active systems within the hierarchy is based on special events, which are generated when specific behavioral patterns occur. Recently, the task of diagnosis of complex active systems has been studied, with an efficient diagnosis technique being proposed. However, the observation of the system is assumed to be linear and certain, which turns out to be an over-assumption in real, large, and distributed systems. This paper extends diagnosis of complex active systems to cope with uncertain temporal observations. An uncertain temporal observation is a DAG where nodes are marked by candidate labels (logical uncertainty), whereas arcs denote partial temporal ordering between nodes (temporal uncertainty). By means of indexing techniques, despite the uncertainty of temporal observations, the intrinsic efficiency of the diagnosis task is retained in both time and space.

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“…When a new generation of robots start to work side-by-side with their human partners in joint tasks (Christensen et al, 2010), they will often encounter new objects or are required to perform new actions. It is important for the robots to automatically learn new knowledge about the environment and the tasks from their human partners.…”

Section: Introductionmentioning

confidence: 99%

Back to the Blocks World: Learning New Actions through Situated Human-Robot Dialogue

She¹,

Yang²,

Cheng³

et al. 2014

Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

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This paper describes an approach for a robotic arm to learn new actions through dialogue in a simplified blocks world. In particular, we have developed a threetier action knowledge representation that on one hand, supports the connection between symbolic representations of language and continuous sensorimotor representations of the robot; and on the other hand, supports the application of existing planning algorithms to address novel situations. Our empirical studies have shown that, based on this representation the robot was able to learn and execute basic actions in the blocks world. When a human is engaged in a dialogue to teach the robot new actions, step-by-step instructions lead to better learning performance compared to one-shot instructions.

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Cognitive Systems Introduction

Cited by 11 publications

References 58 publications

Teaching Robots New Actions through Natural Language Instructions

Teaching Robots New Actions through Natural Language Instructions

Diagnosis of Complex Active Systems with Uncertain Temporal Observations

Back to the Blocks World: Learning New Actions through Situated Human-Robot Dialogue

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