1,769 research outputs found
Coordination of Mobile Mules via Facility Location Strategies
In this paper, we study the problem of wireless sensor network (WSN)
maintenance using mobile entities called mules. The mules are deployed in the
area of the WSN in such a way that would minimize the time it takes them to
reach a failed sensor and fix it. The mules must constantly optimize their
collective deployment to account for occupied mules. The objective is to define
the optimal deployment and task allocation strategy for the mules, so that the
sensors' downtime and the mules' traveling distance are minimized. Our
solutions are inspired by research in the field of computational geometry and
the design of our algorithms is based on state of the art approximation
algorithms for the classical problem of facility location. Our empirical
results demonstrate how cooperation enhances the team's performance, and
indicate that a combination of k-Median based deployment with closest-available
task allocation provides the best results in terms of minimizing the sensors'
downtime but is inefficient in terms of the mules' travel distance. A
k-Centroid based deployment produces good results in both criteria.Comment: 12 pages, 6 figures, conferenc
Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences
We propose a neural sequence-to-sequence model for direction following, a
task that is essential to realizing effective autonomous agents. Our
alignment-based encoder-decoder model with long short-term memory recurrent
neural networks (LSTM-RNN) translates natural language instructions to action
sequences based upon a representation of the observable world state. We
introduce a multi-level aligner that empowers our model to focus on sentence
"regions" salient to the current world state by using multiple abstractions of
the input sentence. In contrast to existing methods, our model uses no
specialized linguistic resources (e.g., parsers) or task-specific annotations
(e.g., seed lexicons). It is therefore generalizable, yet still achieves the
best results reported to-date on a benchmark single-sentence dataset and
competitive results for the limited-training multi-sentence setting. We analyze
our model through a series of ablations that elucidate the contributions of the
primary components of our model.Comment: To appear at AAAI 2016 (and an extended version of a NIPS 2015
Multimodal Machine Learning workshop paper
VirtualHome: Simulating Household Activities via Programs
In this paper, we are interested in modeling complex activities that occur in
a typical household. We propose to use programs, i.e., sequences of atomic
actions and interactions, as a high level representation of complex tasks.
Programs are interesting because they provide a non-ambiguous representation of
a task, and allow agents to execute them. However, nowadays, there is no
database providing this type of information. Towards this goal, we first
crowd-source programs for a variety of activities that happen in people's
homes, via a game-like interface used for teaching kids how to code. Using the
collected dataset, we show how we can learn to extract programs directly from
natural language descriptions or from videos. We then implement the most common
atomic (inter)actions in the Unity3D game engine, and use our programs to
"drive" an artificial agent to execute tasks in a simulated household
environment. Our VirtualHome simulator allows us to create a large activity
video dataset with rich ground-truth, enabling training and testing of video
understanding models. We further showcase examples of our agent performing
tasks in our VirtualHome based on language descriptions.Comment: CVPR 2018 (Oral
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
Explorations in engagement for humans and robots
This paper explores the concept of engagement, the process by which
individuals in an interaction start, maintain and end their perceived
connection to one another. The paper reports on one aspect of engagement among
human interactors--the effect of tracking faces during an interaction. It also
describes the architecture of a robot that can participate in conversational,
collaborative interactions with engagement gestures. Finally, the paper reports
on findings of experiments with human participants who interacted with a robot
when it either performed or did not perform engagement gestures. Results of the
human-robot studies indicate that people become engaged with robots: they
direct their attention to the robot more often in interactions where engagement
gestures are present, and they find interactions more appropriate when
engagement gestures are present than when they are not.Comment: 31 pages, 5 figures, 3 table
A Robot Team that Can Search, Rescue, and Serve Cookies: Experiments in Multi-modal Person Identification and Multi-robot Sound Localization.
A team of three robots placed second in both the Urban Search and Rescue and Hors d’Oeuvres Anyone? events at the 2000 American Association of Artificial Intelligence au- tonomous robot competitions. This paper describes the multi- robot, low-cost sound localization technique, and the multi- sensor, person recognition system used in the AAAI contests by the robot team
A Robot Team that Can Search, Rescue, and Serve Cookies: Experiments in Multi-modal Person Identification and Multi-robot Sound Localization.
A team of three robots placed second in both the Urban Search and Rescue and Hors d’Oeuvres Anyone? events at the 2000 American Association of Artificial Intelligence au- tonomous robot competitions. This paper describes the multi- robot, low-cost sound localization technique, and the multi- sensor, person recognition system used in the AAAI contests by the robot team
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