298 research outputs found
Feedback-based Fabric Strip Folding
Accurate manipulation of a deformable body such as a piece of fabric is
difficult because of its many degrees of freedom and unobservable properties
affecting its dynamics. To alleviate these challenges, we propose the
application of feedback-based control to robotic fabric strip folding. The
feedback is computed from the low dimensional state extracted from a camera
image. We trained the controller using reinforcement learning in simulation
which was calibrated to cover the real fabric strip behaviors. The proposed
feedback-based folding was experimentally compared to two state-of-the-art
folding methods and our method outperformed both of them in terms of accuracy.Comment: Submitted to IEEE/RSJ IROS201
Deep Network Uncertainty Maps for Indoor Navigation
Most mobile robots for indoor use rely on 2D laser scanners for localization,
mapping and navigation. These sensors, however, cannot detect transparent
surfaces or measure the full occupancy of complex objects such as tables. Deep
Neural Networks have recently been proposed to overcome this limitation by
learning to estimate object occupancy. These estimates are nevertheless subject
to uncertainty, making the evaluation of their confidence an important issue
for these measures to be useful for autonomous navigation and mapping. In this
work we approach the problem from two sides. First we discuss uncertainty
estimation in deep models, proposing a solution based on a fully convolutional
neural network. The proposed architecture is not restricted by the assumption
that the uncertainty follows a Gaussian model, as in the case of many popular
solutions for deep model uncertainty estimation, such as Monte-Carlo Dropout.
We present results showing that uncertainty over obstacle distances is actually
better modeled with a Laplace distribution. Then, we propose a novel approach
to build maps based on Deep Neural Network uncertainty models. In particular,
we present an algorithm to build a map that includes information over obstacle
distance estimates while taking into account the level of uncertainty in each
estimate. We show how the constructed map can be used to increase global
navigation safety by planning trajectories which avoid areas of high
uncertainty, enabling higher autonomy for mobile robots in indoor settings.Comment: Accepted for publication in "2019 IEEE-RAS International Conference
on Humanoid Robots (Humanoids)
Subcontracting Product Development – Creating Competitiveness through Networking
Product development has become increasingly complex and resource-consuming. Consequently, internal development capabilities can prove insufficient for maintaining a firm’s competitive position. External cooperation and networking have been suggested as means for accessing necessary complementary knowledge or resources. In this paper, cooperation is studied as the key to improving competitiveness, especially in case of small firms. Product development distributed across organisational boundaries can also help companies mitigate the effect of uncertainty and turbulence. The empirical part of the study describes supplier cooperation in four case companies. The focus is on software product development cooperation with foreign suppliers. The paper contributes to better understanding of organising product development across a network of suppliers
Differential Dynamic Programming with Nonlinear Safety Constraints Under System Uncertainties
Safe operation of systems such as robots requires them to plan and execute
trajectories subject to safety constraints. When those systems are subject to
uncertainties in their dynamics, ensuring that the constraints are not violated
is challenging. In this paper, we propose a safe trajectory optimization and
control approach (Safe-CDDP) for systems under additive uncertainties and
non-linear safety constraints based on constrained differential dynamic
programming (DDP). The safety of the robot during its motion is formulated as
chance-constraints with user-chosen probabilities of constraint satisfaction.
The chance constraints are transformed into deterministic ones in DDP
formulation by constraint tightening. To avoid over conservatism during
constraint tightening, linear control gains of the feedback policy derived from
the constrained DDP are used in the approximation of closed-loop uncertainty
propagation in prediction. The proposed algorithm is empirically demonstrated
on three different robot dynamics with up to 12 states and the results show the
applicability of the approach for safety-aware applications.Comment: 7 pages, 4 figures, submitted to ICRA 202
The Key Success Factors in Distributed Product Development – Case Russia
Distribution of new product development encompasses both great opportunities and threats. In this paper we aim to identify both key success factors and common pitfalls for Western firms in the organisation of distributed product development in Russia. Russia’s national innovation system holds a lot of potential for foreign firms, but there are also many challenges to be addressed. By following general guidelines for co-development, the chances for success are likely to increase also in the case of joint development with Russian firms
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