158 research outputs found
The Phoenix Drone: An Open-Source Dual-Rotor Tail-Sitter Platform for Research and Education
In this paper, we introduce the Phoenix drone: the first completely
open-source tail-sitter micro aerial vehicle (MAV) platform. The vehicle has a
highly versatile, dual-rotor design and is engineered to be low-cost and easily
extensible/modifiable. Our open-source release includes all of the design
documents, software resources, and simulation tools needed to build and fly a
high-performance tail-sitter for research and educational purposes. The drone
has been developed for precision flight with a high degree of control
authority. Our design methodology included extensive testing and
characterization of the aerodynamic properties of the vehicle. The platform
incorporates many off-the-shelf components and 3D-printed parts, in order to
keep the cost down. Nonetheless, the paper includes results from flight trials
which demonstrate that the vehicle is capable of very stable hovering and
accurate trajectory tracking. Our hope is that the open-source Phoenix
reference design will be useful to both researchers and educators. In
particular, the details in this paper and the available open-source materials
should enable learners to gain an understanding of aerodynamics, flight
control, state estimation, software design, and simulation, while experimenting
with a unique aerial robot.Comment: In Proceedings of the IEEE International Conference on Robotics and
Automation (ICRA'19), Montreal, Canada, May 20-24, 201
VITON: An Image-based Virtual Try-on Network
We present an image-based VIirtual Try-On Network (VITON) without using 3D
information in any form, which seamlessly transfers a desired clothing item
onto the corresponding region of a person using a coarse-to-fine strategy.
Conditioned upon a new clothing-agnostic yet descriptive person representation,
our framework first generates a coarse synthesized image with the target
clothing item overlaid on that same person in the same pose. We further enhance
the initial blurry clothing area with a refinement network. The network is
trained to learn how much detail to utilize from the target clothing item, and
where to apply to the person in order to synthesize a photo-realistic image in
which the target item deforms naturally with clear visual patterns. Experiments
on our newly collected Zalando dataset demonstrate its promise in the
image-based virtual try-on task over state-of-the-art generative models
Learning Fashion Compatibility with Bidirectional LSTMs
The ubiquity of online fashion shopping demands effective recommendation
services for customers. In this paper, we study two types of fashion
recommendation: (i) suggesting an item that matches existing components in a
set to form a stylish outfit (a collection of fashion items), and (ii)
generating an outfit with multimodal (images/text) specifications from a user.
To this end, we propose to jointly learn a visual-semantic embedding and the
compatibility relationships among fashion items in an end-to-end fashion. More
specifically, we consider a fashion outfit to be a sequence (usually from top
to bottom and then accessories) and each item in the outfit as a time step.
Given the fashion items in an outfit, we train a bidirectional LSTM (Bi-LSTM)
model to sequentially predict the next item conditioned on previous ones to
learn their compatibility relationships. Further, we learn a visual-semantic
space by regressing image features to their semantic representations aiming to
inject attribute and category information as a regularization for training the
LSTM. The trained network can not only perform the aforementioned
recommendations effectively but also predict the compatibility of a given
outfit. We conduct extensive experiments on our newly collected Polyvore
dataset, and the results provide strong qualitative and quantitative evidence
that our framework outperforms alternative methods.Comment: ACM MM 1
Efficient Bayesian inference using physics-informed invertible neural networks for inverse problems
In the paper, we propose a novel approach for solving Bayesian inverse
problems with physics-informed invertible neural networks (PI-INN). The
architecture of PI-INN consists of two sub-networks: an invertible neural
network (INN) and a neural basis network (NB-Net). The invertible map between
the parametric input and the INN output with the aid of NB-Net is constructed
to provide a tractable estimation of the posterior distribution, which enables
efficient sampling and accurate density evaluation. Furthermore, the loss
function of PI-INN includes two components: a residual-based physics-informed
loss term and a new independence loss term. The presented independence loss
term can Gaussianize the random latent variables and ensure statistical
independence between two parts of INN output by effectively utilizing the
estimated density function. Several numerical experiments are presented to
demonstrate the efficiency and accuracy of the proposed PI-INN, including
inverse kinematics, inverse problems of the 1-d and 2-d diffusion equations,
and seismic traveltime tomography
Decoding Social Sentiment in DAO: A Comparative Analysis of Blockchain Governance Communities
Blockchain technology is leading a revolutionary transformation across
diverse industries, with effective governance standing as a critical
determinant for the success and sustainability of blockchain projects.
Community forums, pivotal in engaging decentralized autonomous organizations
(DAOs), wield a substantial impact on blockchain governance decisions.
Concurrently, Natural Language Processing (NLP), particularly sentiment
analysis, provides powerful insights from textual data. While prior research
has explored the potential of NLP tools in social media sentiment analysis, a
gap persists in understanding the sentiment landscape of blockchain governance
communities. The evolving discourse and sentiment dynamics on the forums of top
DAOs remain largely unknown. This paper delves deep into the evolving discourse
and sentiment dynamics on the public forums of leading DeFi projects -- Aave,
Uniswap, Curve Dao, Aragon, Yearn.finance, Merit Circle, and Balancer --
placing a primary focus on discussions related to governance issues. Despite
differing activity patterns, participants across these decentralized
communities consistently express positive sentiments in their Discord
discussions, indicating optimism towards governance decisions. Additionally,
our research suggests a potential interplay between discussion intensity and
sentiment dynamics, indicating that higher discussion volumes may contribute to
more stable and positive emotions. The insights gained from this study are
valuable for decision-makers in blockchain governance, underscoring the pivotal
role of sentiment analysis in interpreting community emotions and its evolving
impact on the landscape of blockchain governance. This research significantly
contributes to the interdisciplinary exploration of the intersection of
blockchain and society, with a specific emphasis on the decentralized
blockchain governance ecosystem
Recent Advances of Deep Robotic Affordance Learning: A Reinforcement Learning Perspective
As a popular concept proposed in the field of psychology, affordance has been
regarded as one of the important abilities that enable humans to understand and
interact with the environment. Briefly, it captures the possibilities and
effects of the actions of an agent applied to a specific object or, more
generally, a part of the environment. This paper provides a short review of the
recent developments of deep robotic affordance learning (DRAL), which aims to
develop data-driven methods that use the concept of affordance to aid in
robotic tasks. We first classify these papers from a reinforcement learning
(RL) perspective, and draw connections between RL and affordances. The
technical details of each category are discussed and their limitations
identified. We further summarise them and identify future challenges from the
aspects of observations, actions, affordance representation, data-collection
and real-world deployment. A final remark is given at the end to propose a
promising future direction of the RL-based affordance definition to include the
predictions of arbitrary action consequences.Comment: This paper is under revie
Abstract Demonstrations and Adaptive Exploration for Efficient and Stable Multi-step Sparse Reward Reinforcement Learning
Although Deep Reinforcement Learning (DRL) has been popular in many
disciplines including robotics, state-of-the-art DRL algorithms still struggle
to learn long-horizon, multi-step and sparse reward tasks, such as stacking
several blocks given only a task-completion reward signal. To improve learning
efficiency for such tasks, this paper proposes a DRL exploration technique,
termed A^2, which integrates two components inspired by human experiences:
Abstract demonstrations and Adaptive exploration. A^2 starts by decomposing a
complex task into subtasks, and then provides the correct orders of subtasks to
learn. During training, the agent explores the environment adaptively, acting
more deterministically for well-mastered subtasks and more stochastically for
ill-learnt subtasks. Ablation and comparative experiments are conducted on
several grid-world tasks and three robotic manipulation tasks. We demonstrate
that A^2 can aid popular DRL algorithms (DQN, DDPG, and SAC) to learn more
efficiently and stably in these environments.Comment: Accepted by The 27th IEEE International Conference on Automation and
Computing (ICAC2022
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