108 research outputs found
Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior
Bayesian optimization usually assumes that a Bayesian prior is given.
However, the strong theoretical guarantees in Bayesian optimization are often
regrettably compromised in practice because of unknown parameters in the prior.
In this paper, we adopt a variant of empirical Bayes and show that, by
estimating the Gaussian process prior from offline data sampled from the same
prior and constructing unbiased estimators of the posterior, variants of both
GP-UCB and probability of improvement achieve a near-zero regret bound, which
decreases to a constant proportional to the observational noise as the number
of offline data and the number of online evaluations increase. Empirically, we
have verified our approach on challenging simulated robotic problems featuring
task and motion planning.Comment: Proceedings of the Thirty-second Conference on Neural Information
Processing Systems, 201
Local object crop collision network for efficient simulation of non-convex objects in GPU-based simulators
Our goal is to develop an efficient contact detection algorithm for
large-scale GPU-based simulation of non-convex objects. Current GPU-based
simulators such as IsaacGym and Brax must trade-off speed with fidelity,
generality, or both when simulating non-convex objects. Their main issue lies
in contact detection (CD): existing CD algorithms, such as
Gilbert-Johnson-Keerthi (GJK), must trade off their computational speed with
accuracy which becomes expensive as the number of collisions among non-convex
objects increases. We propose a data-driven approach for CD, whose accuracy
depends only on the quality and quantity of offline dataset rather than online
computation time. Unlike GJK, our method inherently has a uniform computational
flow, which facilitates efficient GPU usage based on advanced compilers such as
XLA (Accelerated Linear Algebra). Further, we offer a data-efficient solution
by learning the patterns of colliding local crop object shapes, rather than
global object shapes which are harder to learn. We demonstrate our approach
improves the efficiency of existing CD methods by a factor of 5-10 for
non-convex objects with comparable accuracy. Using the previous work on contact
resolution for a neural-network-based contact detector, we integrate our CD
algorithm into the open-source GPU-based simulator, Brax, and show that we can
improve the efficiency over IsaacGym and generality over standard Brax. We
highly recommend the videos of our simulator included in the supplementary
materials.Comment: RSS 2023 https://sites.google.com/view/locc-rss2023/hom
Pre- and post-contact policy decomposition for non-prehensile manipulation with zero-shot sim-to-real transfer
We present a system for non-prehensile manipulation that require a
significant number of contact mode transitions and the use of environmental
contacts to successfully manipulate an object to a target location. Our method
is based on deep reinforcement learning which, unlike state-of-the-art planning
algorithms, does not require apriori knowledge of the physical parameters of
the object or environment such as friction coefficients or centers of mass. The
planning time is reduced to the simple feed-forward prediction time on a neural
network. We propose a computational structure, action space design, and
curriculum learning scheme that facilitates efficient exploration and
sim-to-real transfer. In challenging real-world non-prehensile manipulation
tasks, we show that our method can generalize over different objects, and
succeed even for novel objects not seen during training. Project website:
https://sites.google.com/view/nonprenehsile-decompositionComment: Accepted to the 2023 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS
Learning Whole-body Manipulation for Quadrupedal Robot
We propose a learning-based system for enabling quadrupedal robots to
manipulate large, heavy objects using their whole body. Our system is based on
a hierarchical control strategy that uses the deep latent variable embedding
which captures manipulation-relevant information from interactions,
proprioception, and action history, allowing the robot to implicitly understand
object properties. We evaluate our framework in both simulation and real-world
scenarios. In the simulation, it achieves a success rate of 93.6 % in
accurately re-positioning and re-orienting various objects within a tolerance
of 0.03 m and 5 {\deg}. Real-world experiments demonstrate the successful
manipulation of objects such as a 19.2 kg water-filled drum and a 15.3 kg
plastic box filled with heavy objects while the robot weighs 27 kg. Unlike
previous works that focus on manipulating small and light objects using
prehensile manipulation, our framework illustrates the possibility of using
quadrupeds for manipulating large and heavy objects that are ungraspable with
the robot's entire body. Our method does not require explicit object modeling
and offers significant computational efficiency compared to optimization-based
methods. The video can be found at https://youtu.be/fO_PVr27QxU
Thermal analysis, design and fabrication of microfluidic device with local temperature controls
This paper reports a microfluidic device with local temperature controls to stimulate bio-cells by heat. Thermal analysis on temperature distributions of microheaters as well as inside of fluidic channels was carried out. Moreover, temperature in microfluidic device was measured by on-chip resistive thermometers
Temperature-evolution of spectral function and optical conductivity in heavy fermion compound CeIrIn under crystalline electric field
We investigate the role of the crystalline electric field (CEF) in the
temperature ()-evolution of the Kondo resonance states and its effect on
optical conductivity. We perform the combined first principles calculation of
the density functional theory and dynamical mean field theory on
CeIrIn. The calculated spectral function reproduces the
experimental observed CEF states at low , while it shows a drastic change of
the Fermi surface upon increasing . The effect of the CEF states on the
Fermi surface as a function of is elucidated through the first principles
calculations as well as the analysis on the Anderson impurity model.
Consequently, we suggest the importance of the CEF-driven orbital anisotropy in
the low-energy states of optical experiments.Comment: 6 pages, 4 figure
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