32,322 research outputs found
A Tale of Two DRAGGNs: A Hybrid Approach for Interpreting Action-Oriented and Goal-Oriented Instructions
Robots operating alongside humans in diverse, stochastic environments must be
able to accurately interpret natural language commands. These instructions
often fall into one of two categories: those that specify a goal condition or
target state, and those that specify explicit actions, or how to perform a
given task. Recent approaches have used reward functions as a semantic
representation of goal-based commands, which allows for the use of a
state-of-the-art planner to find a policy for the given task. However, these
reward functions cannot be directly used to represent action-oriented commands.
We introduce a new hybrid approach, the Deep Recurrent Action-Goal Grounding
Network (DRAGGN), for task grounding and execution that handles natural
language from either category as input, and generalizes to unseen environments.
Our robot-simulation results demonstrate that a system successfully
interpreting both goal-oriented and action-oriented task specifications brings
us closer to robust natural language understanding for human-robot interaction.Comment: Accepted at the 1st Workshop on Language Grounding for Robotics at
ACL 201
A Tale of Two DRAGGNs: A Hybrid Approach for Interpreting Action-Oriented and Goal-Oriented Instructions
Robots operating alongside humans in diverse, stochastic environments must be
able to accurately interpret natural language commands. These instructions
often fall into one of two categories: those that specify a goal condition or
target state, and those that specify explicit actions, or how to perform a
given task. Recent approaches have used reward functions as a semantic
representation of goal-based commands, which allows for the use of a
state-of-the-art planner to find a policy for the given task. However, these
reward functions cannot be directly used to represent action-oriented commands.
We introduce a new hybrid approach, the Deep Recurrent Action-Goal Grounding
Network (DRAGGN), for task grounding and execution that handles natural
language from either category as input, and generalizes to unseen environments.
Our robot-simulation results demonstrate that a system successfully
interpreting both goal-oriented and action-oriented task specifications brings
us closer to robust natural language understanding for human-robot interaction.Comment: Accepted at the 1st Workshop on Language Grounding for Robotics at
ACL 201
Modelling the relationship between planning, control, perception and execution behaviours in interactive worksystems
This paper presents a model of planning carried out by interactive worksystems which attempts: 1. To describe the relationship between planning, control, perception and execution behaviours; 2. To make explicit how these may be distributed across the user and physically separate devices. Such a model, it is argued, is more suitable to support HCI design practice than theories of planning in cognitive science which focus on problem-solving methods and representations. To demonstrate the application of the model to work situations, it is illustrated by examples drawn from an observational study of secretarial office administration
Patenting Computer Data Structures: The Ghost, the Machine and the Federal Circuit
Courts view data structures, the mechanism by which computers store data in meaningful relationships, differently than do computer scientists. While computer scientists recognize that data structures have aspects that are both physical (how they are stored in memory) and logical (the relationships among the stored information), the Federal Circuit, in its attempts to set clear standards of the scope of patentability of data structures, has not fully appreciated their dualistic nature. This i-brief explains what data structures are, explores how courts have wrestled with setting a limiting principle to determine their patentability, and discusses the resultant impact on claim drafting
Your space or mine? : Mapping self in time
Peer reviewedPublisher PD
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