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Jan Issac

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

ICRA Conference 2019 Conference Paper

Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience

Yevgen Chebotar
Ankur Handa
Viktor Makoviychuk
Miles Macklin
Jan Issac
Nathan D. Ratliff
Dieter Fox

We consider the problem of transferring policies to the real world by training on a distribution of simulated scenarios. Rather than manually tuning the randomization of simulations, we adapt the simulation parameter distribution using a few real world roll-outs interleaved with policy training. In doing so, we are able to change the distribution of simulations to improve the policy transfer by matching the policy behavior in simulation and the real world. We show that policies trained with our method are able to reliably transfer to different robots in two real world tasks: swing-peg-in-hole and opening a cabinet drawer. The video of our experiments can be found at https://sites.google.com/view/simopt.

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ICRA Conference 2016 Conference Paper

Depth-based object tracking using a Robust Gaussian Filter

Jan Issac
Manuel Wüthrich
Cristina Garcia Cifuentes
Jeannette Bohg
Sebastian Trimpe
Stefan Schaal

We consider the problem of model-based 3D-tracking of objects given dense depth images as input. Two difficulties preclude the application of a standard Gaussian filter to this problem. First of all, depth sensors are characterized by fat-tailed measurement noise. To address this issue, we show how a recently published robustification method for Gaussian filters can be applied to the problem at hand. Thereby, we avoid using heuristic outlier detection methods that simply reject measurements if they do not match the model. Secondly, the computational cost of the standard Gaussian filter is prohibitive due to the high-dimensional measurement, i. e. the depth image. To address this problem, we propose an approximation to reduce the computational complexity of the filter. In quantitative experiments on real data we show how our method clearly outperforms the standard Gaussian filter. Furthermore, we compare its performance to a particle-filter-based tracking method, and observe comparable computational efficiency and improved accuracy and smoothness of the estimates.

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ICRA Conference 2010 Conference Paper

Autonomous acquisition of visual multi-view object representations for object recognition on a humanoid robot

Kai Welke
Jan Issac
David Schiebener
Tamim Asfour
Rüdiger Dillmann

The autonomous acquisition of object representations which allow recognition, localization and grasping of objects in the environment is a challenging task, which has shown to be difficult. In this paper, we present a systems for autonomous acquisition of visual object representations, which endows a humanoid robot with the ability to enrich its internal object representation and allows the realization of complex visual tasks. More precisely, we present techniques for segmentation and modeling of objects held in the five-fingered robot hand. Multiple object views are generated by rotating the held objects in the robot's field of view. The acquired object representations are evaluated in the context of visual search and object recognition tasks in cluttered environments. Experimental results show successful implementation of the complete cycle from object exploration to object recognition on a humanoid robot.

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