56 research outputs found
Extending visual perception with haptic exploration for improved scene understanding
Scene understanding has been investigated from a mainly visual information point of view. Recently depth has been provided an extra wealth of information, allowing more geometric knowledge to fuse into scene understanding. Yet to form a holistic view, especially in robotic applications, one can create even more data by interacting with the world. In fact humans, when growing up, seem to heavily investigate the world around them by haptic exploration. We show an application of haptic exploration on a humanoid robot in cooperation with a learning method for object segmentation. The actions performed consecutively improve the segmentation of objects in the scene
Cloud WorkBench - Infrastructure-as-Code Based Cloud Benchmarking
To optimally deploy their applications, users of Infrastructure-as-a-Service
clouds are required to evaluate the costs and performance of different
combinations of cloud configurations to find out which combination provides the
best service level for their specific application. Unfortunately, benchmarking
cloud services is cumbersome and error-prone. In this paper, we propose an
architecture and concrete implementation of a cloud benchmarking Web service,
which fosters the definition of reusable and representative benchmarks. In
distinction to existing work, our system is based on the notion of
Infrastructure-as-Code, which is a state of the art concept to define IT
infrastructure in a reproducible, well-defined, and testable way. We
demonstrate our system based on an illustrative case study, in which we measure
and compare the disk IO speeds of different instance and storage types in
Amazon EC2
Traversing the Reality Gap via Simulator Tuning
The large demand for simulated data has made the reality gap a problem on the
forefront of robotics. We propose a method to traverse the gap by tuning
available simulation parameters. Through the optimisation of physics engine
parameters, we show that we are able to narrow the gap between simulated
solutions and a real world dataset, and thus allow more ready transfer of
leaned behaviours between the two. We subsequently gain understanding as to the
importance of specific simulator parameters, which is of broad interest to the
robotic machine learning community. We find that even optimised for different
tasks that different physics engine perform better in certain scenarios and
that friction and maximum actuator velocity are tightly bounded parameters that
greatly impact the transference of simulated solutions.Comment: 8 Pages, Submitted to IROS202
Learning When to Switch: Composing Controllers to Traverse a Sequence of Terrain Artifacts
Legged robots often use separate control policiesthat are highly engineered
for traversing difficult terrain suchas stairs, gaps, and steps, where
switching between policies isonly possible when the robot is in a region that
is commonto adjacent controllers. Deep Reinforcement Learning (DRL)is a
promising alternative to hand-crafted control design,though typically requires
the full set of test conditions to beknown before training. DRL policies can
result in complex(often unrealistic) behaviours that have few or no
overlappingregions between adjacent policies, making it difficult to
switchbehaviours. In this work we develop multiple DRL policieswith Curriculum
Learning (CL), each that can traverse asingle respective terrain condition,
while ensuring an overlapbetween policies. We then train a network for each
destinationpolicy that estimates the likelihood of successfully switchingfrom
any other policy. We evaluate our switching methodon a previously unseen
combination of terrain artifacts andshow that it performs better than heuristic
methods. Whileour method is trained on individual terrain types, it
performscomparably to a Deep Q Network trained on the full set ofterrain
conditions. This approach allows the development ofseparate policies in
constrained conditions with embedded priorknowledge about each behaviour, that
is scalable to any numberof behaviours, and prepares DRL methods for
applications inthe real worl
Взаємодія напівпровідників типу АІІІВV з розчинами Н2О2 - НВr
To plan complex motions of robots with many degrees of freedom, our novel, very flexible framework builds task-relevant roadmaps (TRMs), using a new sampling-based optimizer called Natural Gradient Inverse Kinematics (NGIK) based on natural evolution strategies (NES). To build TRMs, NGIK iteratively optimizes postures covering task-spaces expressed by arbitrary task-functions, subject to constraints expressed by arbitrary cost-functions, transparently dealing with both hard and soft constraints. TRMs are grown to maximally cover the task-space while minimizing costs. Unlike Jacobian-based methods, our algorithm does not rely on calculation of gradients, making application of the algorithm much simpler. We show how NGIK outperforms recent related sampling algorithms. A <font color="blue"><a href="http://youtu.be/N6x2e1Zf_yg">video demo</a></font> successfully applies TRMs to an iCub humanoid robot with 41 DOF in its upper body, arms, hands, head, and eyes. To our knowledge, no similar methods exhibit such a degree of flexibility in defining movements
Learning Arbitrary-Goal Fabric Folding with One Hour of Real Robot Experience
Manipulating deformable objects, such as fabric, is a long standing problem
in robotics, with state estimation and control posing a significant challenge
for traditional methods. In this paper, we show that it is possible to learn
fabric folding skills in only an hour of self-supervised real robot experience,
without human supervision or simulation. Our approach relies on fully
convolutional networks and the manipulation of visual inputs to exploit learned
features, allowing us to create an expressive goal-conditioned pick and place
policy that can be trained efficiently with real world robot data only. Folding
skills are learned with only a sparse reward function and thus do not require
reward function engineering, merely an image of the goal configuration. We
demonstrate our method on a set of towel-folding tasks, and show that our
approach is able to discover sequential folding strategies, purely from
trial-and-error. We achieve state-of-the-art results without the need for
demonstrations or simulation, used in prior approaches. Videos available at:
https://sites.google.com/view/learningtofol
ALife in humanoids: Developing a framework to employ artificial life techniques for high-level perception and cognition tasks on humanoid robots
We describe our recent research and advances in building a framework enabling artifical life (ALife) systems on real robotic hardware. Our framework allows our iCub humanoid to build better visual perception, improve its motion capabilities and even provide a sense of proprioception. This paper presents how we can use various techniques, such as, e.g., genetic programming, to build subsystems for these specific areas. Our framework runs in parallel with the hardware system and is updated with new information from the real robot. We plan to use this framework in the future for developing higher cognitive tasks, such as, scene understanding, prediction of action outcomes, and reasoning on our robot
Sondeo arqueológico Cueva Pintada corte 0 cierre sur [Material gráfico]
Copia digital. Madrid : Ministerio de Educación, Cultura y Deporte. Subdirección General de Coordinación Bibliotecaria, 201
First Community-Wide, Comparative Cross-Linking Mass Spectrometry Study
The number of publications in the field of chemical cross-linking combined with mass spectrometry (XL-MS) to derive constraints for protein three-dimensional structure modeling and to probe protein-protein interactions has increased during the last years. As the technique is now becoming routine for in vitro and in vivo applications in proteomics and structural biology there is a pressing need to define protocols as well as data analysis and reporting formats. Such consensus formats should become accepted in the field and be shown to lead to reproducible results. This first, community-based harmonization study on XL-MS is based on the results of 32 groups participating worldwide. The aim of this paper is to summarize the status quo of XL-MS and to compare and evaluate existing cross-linking strategies. Our study therefore builds the framework for establishing best practice guidelines to conduct cross-linking experiments, perform data analysis, and define reporting formats with the ultimate goal of assisting scientists to generate accurate and reproducible XL-MS results
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