242 research outputs found
Task Transfer by Preference-Based Cost Learning
The goal of task transfer in reinforcement learning is migrating the action
policy of an agent to the target task from the source task. Given their
successes on robotic action planning, current methods mostly rely on two
requirements: exactly-relevant expert demonstrations or the explicitly-coded
cost function on target task, both of which, however, are inconvenient to
obtain in practice. In this paper, we relax these two strong conditions by
developing a novel task transfer framework where the expert preference is
applied as a guidance. In particular, we alternate the following two steps:
Firstly, letting experts apply pre-defined preference rules to select related
expert demonstrates for the target task. Secondly, based on the selection
result, we learn the target cost function and trajectory distribution
simultaneously via enhanced Adversarial MaxEnt IRL and generate more
trajectories by the learned target distribution for the next preference
selection. The theoretical analysis on the distribution learning and
convergence of the proposed algorithm are provided. Extensive simulations on
several benchmarks have been conducted for further verifying the effectiveness
of the proposed method.Comment: Accepted to AAAI 2019. Mingxuan Jing and Xiaojian Ma contributed
equally to this wor
The asymmetric effect of film and drama industry, energy efficiency and economic growth on green innovation: Empirical evidence from quantile estimation
The popularity of green innovation has dramatically increased in
the recent times because of the potential benefits attached with
it. Therefore, in order to make the technology more affordable,
green innovation is the key to enhancing the affordability factor.
On the other hand, in order to safeguard the environment, the
role of media is one of fundamental importance. In contrast,
energy consumption is often regarded as a key indicator of economic prosperity, mostly at the cost of the environment. Hence,
the present study attempts to explore the asymmetric effect of
the film and drama industry, energy efficiency, and economic
growth on green innovation, with the help of the latest quantile
autoregressive distributed lag (QARDL) method for the period
2000Q1 to 2019Q4. The results have reported a positive and significant association of the Film and Drama Industry, Energy
Efficiency, and economic growth on the quantiles of Green
Innovation. Based on the findings, it is recommended that there
is a dire need to develop content that promotes the green innovation, whereas, more investments are to be sought after, so as to
enhance the level of energy efficiency
Open-World Multi-Task Control Through Goal-Aware Representation Learning and Adaptive Horizon Prediction
We study the problem of learning goal-conditioned policies in Minecraft, a
popular, widely accessible yet challenging open-ended environment for
developing human-level multi-task agents. We first identify two main challenges
of learning such policies: 1) the indistinguishability of tasks from the state
distribution, due to the vast scene diversity, and 2) the non-stationary nature
of environment dynamics caused by partial observability. To tackle the first
challenge, we propose Goal-Sensitive Backbone (GSB) for the policy to encourage
the emergence of goal-relevant visual state representations. To tackle the
second challenge, the policy is further fueled by an adaptive horizon
prediction module that helps alleviate the learning uncertainty brought by the
non-stationary dynamics. Experiments on 20 Minecraft tasks show that our method
significantly outperforms the best baseline so far; in many of them, we double
the performance. Our ablation and exploratory studies then explain how our
approach beat the counterparts and also unveil the surprising bonus of
zero-shot generalization to new scenes (biomes). We hope our agent could help
shed some light on learning goal-conditioned, multi-task agents in challenging,
open-ended environments like Minecraft.Comment: This paper is accepted by CVPR202
Numerical investigation of harbor oscillations induced by focused transient wave groups
Focused wave groups are traveling waves characterized by extremely-large transient wave amplitudes and very short durations. These waves usually cause serious damage to marine/offshore structures and coastal infrastructures, and can even result in human casualties (Nikolkina and Didenkulova, 2011). The studies on natural disasters related to the focused wave groups near the coastal zone have been mostly confined to wave evolution over beaches, wave runup, overtopping, and their impact forces acting on the coastal infrastructures (e.g., the seawall and the circular cylinder); the influence of focused transient wave groups on harbors has not yet been studied. In this study, the generation and propagation of focused transient wave groups and their interactions with the harbor are simulated using a fully nonlinear Boussinesq model, FUNWAVE 2.0. To this end, four elongated harbors with constant depth and a series of focused wave groups with various focused wave amplitudes, spectral width parameters, and incident directions are considered. Based on the Morlet wavelet transform and discrete Fourier transform techniques, the capability of focused transient wave groups to trigger the harbor resonance phenomenon is revealed for the first time. Subsequently, the influences of spectral width parameter, incident wave direction, and resonant mode on different resonant wave parameters (including maximum runup and resonant intensity of various resonant modes inside a harbor) are comprehensively investigated, and it is found that these three factors have significant effects on resonant wave parameters.</p
Making Sense of Audio Vibration for Liquid Height Estimation in Robotic Pouring
In this paper, we focus on the challenging perception problem in robotic
pouring. Most of the existing approaches either leverage visual or haptic
information. However, these techniques may suffer from poor generalization
performances on opaque containers or concerning measuring precision. To tackle
these drawbacks, we propose to make use of audio vibration sensing and design a
deep neural network PouringNet to predict the liquid height from the audio
fragment during the robotic pouring task. PouringNet is trained on our
collected real-world pouring dataset with multimodal sensing data, which
contains more than 3000 recordings of audio, force feedback, video and
trajectory data of the human hand that performs the pouring task. Each record
represents a complete pouring procedure. We conduct several evaluations on
PouringNet with our dataset and robotic hardware. The results demonstrate that
our PouringNet generalizes well across different liquid containers, positions
of the audio receiver, initial liquid heights and types of liquid, and
facilitates a more robust and accurate audio-based perception for robotic
pouring.Comment: Checkout project page for video, code and dataset:
https://lianghongzhuo.github.io/AudioPourin
Biomechanical comparison of multilevel lateral interbody fusion with and without supplementary instrumentation: a three-dimensional finite element study
Abstract
Background
Lateral lumbar interbody fusion (LLIF) is a popular, minimally invasive technique that is used to address challenging multilevel degenerative spinal diseases. It remains controversial whether supplemental instrumentation should be added for multilevel LLIF. In this study, we compared the kinematic stability afforded by stand-alone lateral cages with those supplemented by bilateral pedicle screws and rods (PSR), unilateral PSR, or lateral plate (LP) fixation using a finite-element (FE) model of a multi-level LLIF construct with simulated osteoporosis. Additionally, to evaluate the prospect of cage subsidence, the stress change characteristics were surveyed at cage-endplate interfaces.
Methods
A nonlinear 3-dimensional FE model of the lumbar spine (L2 to sacrum) was used. After validation, four patterns of instrumented 3-level LLIF (L2-L5) were constructed for this analysis: (a) 3 stand-alone lateral cages (SLC), (b) 3 lateral cages with lateral plate and two screws (parallel to endplate) fixated separately (LPC), (c) 3 lateral cages with bilateral pedicle screw and rod fixation (LC + BPSR), and (d) 3 lateral cages with unilateral pedicle and rod fixation (LC + UPSR). The segmental and overall range of motion (ROM) of each implanted condition were investigated and compared with the intact model. The peak von Mises stresses upon each (superior) endplate and the stress distribution were used for analysis.
Results
BPSR provided the maximum reduction of ROM among the configurations at every plane of motion (66.7–90.9% of intact spine). UPSR also provided significant segmental ROM reduction (45.0–88.3%). SLC provided a minimal restriction of ROM (10.0–75.1%), and LPC was found to be less stable than both posterior fixation (23.9–86.2%) constructs. The construct with stand-alone lateral cages generated greater endplate stresses than did any of the other multilevel LLIF models. For the L3, L4 and L5 endplates, peak endplate stresses caused by the SLC construct exceeded the BPSR group by 52.7, 63.8, and 54.2% in flexion, 22.3, 40.1, and 31.4% in extension, 170.2, 175.1, and 134.0% in lateral bending, and 90.7, 45.5, and 30.0% in axial rotation, respectively. The stresses tended to be more concentrated at the periphery of the endplates.
Conclusions
SLC and LPC provided inadequate ROM restriction for the multilevel LLIF constructs, whereas lateral cages with BPSR or UPSR fixation provided favorable biomechanical stability. Moreover, SLC generated significantly higher endplate stress compared with supplemental instrumentation, which may have increased the risk of cage subsidence. Further biomechanical and clinical studies are required to validate our FEA findings.http://deepblue.lib.umich.edu/bitstream/2027.42/136058/1/12891_2017_Article_1387.pd
3D-VisTA: Pre-trained Transformer for 3D Vision and Text Alignment
3D vision-language grounding (3D-VL) is an emerging field that aims to
connect the 3D physical world with natural language, which is crucial for
achieving embodied intelligence. Current 3D-VL models rely heavily on
sophisticated modules, auxiliary losses, and optimization tricks, which calls
for a simple and unified model. In this paper, we propose 3D-VisTA, a
pre-trained Transformer for 3D Vision and Text Alignment that can be easily
adapted to various downstream tasks. 3D-VisTA simply utilizes self-attention
layers for both single-modal modeling and multi-modal fusion without any
sophisticated task-specific design. To further enhance its performance on 3D-VL
tasks, we construct ScanScribe, the first large-scale 3D scene-text pairs
dataset for 3D-VL pre-training. ScanScribe contains 2,995 RGB-D scans for 1,185
unique indoor scenes originating from ScanNet and 3R-Scan datasets, along with
paired 278K scene descriptions generated from existing 3D-VL tasks, templates,
and GPT-3. 3D-VisTA is pre-trained on ScanScribe via masked language/object
modeling and scene-text matching. It achieves state-of-the-art results on
various 3D-VL tasks, ranging from visual grounding and dense captioning to
question answering and situated reasoning. Moreover, 3D-VisTA demonstrates
superior data efficiency, obtaining strong performance even with limited
annotations during downstream task fine-tuning
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