20,992 research outputs found
Robobarista: Learning to Manipulate Novel Objects via Deep Multimodal Embedding
There is a large variety of objects and appliances in human environments,
such as stoves, coffee dispensers, juice extractors, and so on. It is
challenging for a roboticist to program a robot for each of these object types
and for each of their instantiations. In this work, we present a novel approach
to manipulation planning based on the idea that many household objects share
similarly-operated object parts. We formulate the manipulation planning as a
structured prediction problem and learn to transfer manipulation strategy
across different objects by embedding point-cloud, natural language, and
manipulation trajectory data into a shared embedding space using a deep neural
network. In order to learn semantically meaningful spaces throughout our
network, we introduce a method for pre-training its lower layers for multimodal
feature embedding and a method for fine-tuning this embedding space using a
loss-based margin. In order to collect a large number of manipulation
demonstrations for different objects, we develop a new crowd-sourcing platform
called Robobarista. We test our model on our dataset consisting of 116 objects
and appliances with 249 parts along with 250 language instructions, for which
there are 1225 crowd-sourced manipulation demonstrations. We further show that
our robot with our model can even prepare a cup of a latte with appliances it
has never seen before.Comment: Journal Versio
Robobarista: Object Part based Transfer of Manipulation Trajectories from Crowd-sourcing in 3D Pointclouds
There is a large variety of objects and appliances in human environments,
such as stoves, coffee dispensers, juice extractors, and so on. It is
challenging for a roboticist to program a robot for each of these object types
and for each of their instantiations. In this work, we present a novel approach
to manipulation planning based on the idea that many household objects share
similarly-operated object parts. We formulate the manipulation planning as a
structured prediction problem and design a deep learning model that can handle
large noise in the manipulation demonstrations and learns features from three
different modalities: point-clouds, language and trajectory. In order to
collect a large number of manipulation demonstrations for different objects, we
developed a new crowd-sourcing platform called Robobarista. We test our model
on our dataset consisting of 116 objects with 249 parts along with 250 language
instructions, for which there are 1225 crowd-sourced manipulation
demonstrations. We further show that our robot can even manipulate objects it
has never seen before.Comment: In International Symposium on Robotics Research (ISRR) 201
Data-based prediction and causality inference of nonlinear dynamics
Natural systems are typically nonlinear and complex, and it is of great
interest to be able to reconstruct a system in order to understand its
mechanism, which can not only recover nonlinear behaviors but also predict
future dynamics. Due to the advances of modern technology, big data becomes
increasingly accessible and consequently the problem of reconstructing systems
from measured data or time series plays a central role in many scientific
disciplines. In recent decades, nonlinear methods rooted in state space
reconstruction have been developed, and they do not assume any model equations
but can recover the dynamics purely from the measured time series data. In this
review, the development of state space reconstruction techniques will be
introduced and the recent advances in systems prediction and causality
inference using state space reconstruction will be presented. Particularly, the
cutting-edge method to deal with short-term time series data will be focused.
Finally, the advantages as well as the remaining problems in this field are
discussed.Comment: 19 pages, 9 figures, it has been accepted for publication in SCIENCE
CHINA Mathematic
An ASP-Based Architecture for Autonomous UAVs in Dynamic Environments: Progress Report
Traditional AI reasoning techniques have been used successfully in many
domains, including logistics, scheduling and game playing. This paper is part
of a project aimed at investigating how such techniques can be extended to
coordinate teams of unmanned aerial vehicles (UAVs) in dynamic environments.
Specifically challenging are real-world environments where UAVs and other
network-enabled devices must communicate to coordinate---and communication
actions are neither reliable nor free. Such network-centric environments are
common in military, public safety and commercial applications, yet most
research (even multi-agent planning) usually takes communications among
distributed agents as a given. We address this challenge by developing an agent
architecture and reasoning algorithms based on Answer Set Programming (ASP).
ASP has been chosen for this task because it enables high flexibility of
representation, both of knowledge and of reasoning tasks. Although ASP has been
used successfully in a number of applications, and ASP-based architectures have
been studied for about a decade, to the best of our knowledge this is the first
practical application of a complete ASP-based agent architecture. It is also
the first practical application of ASP involving a combination of centralized
reasoning, decentralized reasoning, execution monitoring, and reasoning about
network communications. This work has been empirically validated using a
distributed network-centric software evaluation testbed and the results provide
guidance to designers in how to understand and control intelligent systems that
operate in these environments.Comment: Proceedings of the 15th International Workshop on Non-Monotonic
Reasoning (NMR 2014
MortonNet: Self-Supervised Learning of Local Features in 3D Point Clouds
We present a self-supervised task on point clouds, in order to learn
meaningful point-wise features that encode local structure around each point.
Our self-supervised network, named MortonNet, operates directly on
unstructured/unordered point clouds. Using a multi-layer RNN, MortonNet
predicts the next point in a point sequence created by a popular and fast Space
Filling Curve, the Morton-order curve. The final RNN state (coined Morton
feature) is versatile and can be used in generic 3D tasks on point clouds. In
fact, we show how Morton features can be used to significantly improve
performance (+3% for 2 popular semantic segmentation algorithms) in the task of
semantic segmentation of point clouds on the challenging and large-scale S3DIS
dataset. We also show how MortonNet trained on S3DIS transfers well to another
large-scale dataset, vKITTI, leading to an improvement over state-of-the-art of
3.8%. Finally, we use Morton features to train a much simpler and more stable
model for part segmentation in ShapeNet. Our results show how our
self-supervised task results in features that are useful for 3D segmentation
tasks, and generalize well to other datasets
Parking Sensing and Information System: Sensors, Deployment, and Evaluation
This paper describes a smart parking sensing and information system that
disseminates the parking availability information for public users in a
cost-effective and efficient manner. The hardware framework of the system is
built on advanced wireless sensor networks and cloud service over the Internet,
and the system is highly scalable. The parking information provided to the
users is set in the form of occupancy rates and expected cruising time. Both
are obtained from our analytical algorithm processing both historical and
real-time data, and are thereafter visualized in a color theme. The entire
parking system is deployed and extensively evaluated at Stanford University
Parking Structure-1
Social Behavior in Bacterial Nanonetworks: Challenges and Opportunities
Molecular communication promises to enable communication between nanomachines
with a view to increasing their functionalities and open up new possible
applications. Due to some of the biological properties, bacteria have been
proposed as a possible information carrier for molecular communication, and the
corresponding communication networks are known as \textit{bacterial
nanonetworks}. The biological properties include the ability for bacteria to
mobilize between locations and carry the information encoded in
Deoxyribonucleic Acid (DNA) molecules. However, similar to most organisms,
bacteria have complex social properties that govern their colony. These social
characteristics enable the bacteria to evolve through various fluctuating
environmental conditions by utilizing cooperative and non-cooperative
behaviors. This article provides an overview of the different types of
cooperative and non-cooperative social behavior of bacteria. The challenges
(due to non-cooperation) and the opportunities (due to cooperation) these
behaviors can bring to the reliability of communication in bacterial
nanonetworks are also discussed. Finally, simulation results on the impact of
bacterial cooperative social behavior on the end-to-end reliability of a
single-link bacterial nanonetwork are presented. The article concludes with
highlighting the potential future research opportunities in this emerging
field.Comment: Accepted for publication in IEEE Network Magazine as an open call
articl
Blade: A Data Center Garbage Collector
An increasing number of high-performance distributed systems are written in
garbage collected languages. This removes a large class of harmful bugs from
these systems. However, it also introduces high tail-latency do to garbage
collection pause times. We address this problem through a new technique of
garbage collection avoidance which we call Blade. Blade is an API between the
collector and application developer that allows developers to leverage existing
failure recovery mechanisms in distributed systems to coordinate collection and
bound the latency impact. We describe Blade and implement it for the Go
programming language. We also investigate two different systems that utilize
Blade, a HTTP load-balancer and the Raft consensus algorithm. For the
load-balancer, we eliminate any latency introduced by the garbage collector,
for Raft, we bound the latency impact to a single network round-trip, (48
{\mu}s in our setup). In both cases, latency at the tail using Blade is up to
three orders of magnitude better.Comment: 14 pages, 9 figure
Incorporating Privileged Information to Unsupervised Anomaly Detection
We introduce a new unsupervised anomaly detection ensemble called SPI which
can harness privileged information - data available only for training examples
but not for (future) test examples. Our ideas build on the Learning Using
Privileged Information (LUPI) paradigm pioneered by Vapnik et al. [19,17],
which we extend to unsupervised learning and in particular to anomaly
detection. SPI (for Spotting anomalies with Privileged Information) constructs
a number of frames/fragments of knowledge (i.e., density estimates) in the
privileged space and transfers them to the anomaly scoring space through
"imitation" functions that use only the partial information available for test
examples. Our generalization of the LUPI paradigm to unsupervised anomaly
detection shepherds the field in several key directions, including (i) domain
knowledge-augmented detection using expert annotations as PI, (ii) fast
detection using computationally-demanding data as PI, and (iii) early detection
using "historical future" data as PI. Through extensive experiments on
simulated and real datasets, we show that augmenting privileged information to
anomaly detection significantly improves detection performance. We also
demonstrate the promise of SPI under all three settings (i-iii); with PI
capturing expert knowledge, computationally expensive features, and future data
on three real world detection tasks
GreyFiber: A System for Providing Flexible Access to Wide-Area Connectivity
Access to fiber-optic connectivity in the Internet is traditionally offered
either via lit circuits or dark fiber. Economic (capex vs. opex) and
operational considerations (latency, capacity) dictate the choice between these
two offerings, but neither may effectively address the specific needs of
modern-day enterprises or service providers over a range of use scenarios. In
this paper, we describe a new approach for fiber-optic connectivity in the
Internet that we call GreyFiber. The core idea of GreyFiber is to offer
flexible access to fiber-optic paths between end points (e.g., datacenters or
colocation facilities) over a range of timescales. We identify and discuss
operational issues and systems challenges that need to be addressed to make
GreyFiber a viable and realistic option for offering flexible access to
infrastructure (similar to cloud computing). We investigate the efficacy of
GreyFiber with a prototype implementation deployed in the GENI and CloudLab
testbeds. Our scaling experiments show that 50 circuits can be provisioned
within a minute. We also show that backup paths can be provisioned 28 times
faster than an OSPF-based solution during failure/maintenance events. Our
experiments also examine GreyFiber overhead demands and show that the time
spent in circuit creation is dependent on the network infrastructure,
indicating avenues for future improvements
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