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Sensor models, which specify the distribution of sensor observations, are a widely used and integral part of robotics algorithms. Observation distributions are commonly approximated by parametric models, which are limited in their expressiveness, and may require careful design to suit an application. In this paper, we propose nonparametric distribution regression as a procedure to model sensors. It...
There is growing interest in applying machine learning to motion planning. Potential applications are predicting an initial seed for trajectory optimization, predicting an effective heuristic for search based planning, and even predicting a planning algorithm for adaptive motion planning systems. In these situations, providing only a single prediction is unsatisfactory. It leads to many scenarios...
We study the problem of building a sensor model for the purpose of simulation. Our work is motivated by the potential impact of realistic simulators on the development cycle of software for real robots. The case is made for building models from approximate state information, relieving the burden of ground truth. Unlike calibration, where the goal is to identify and remove error from a signal, our...
We investigate the problem of a robot searching for an object. This requires reasoning about both perception and manipulation: some objects are moved because the target may be hidden behind them, while others are moved because they block the manipulator’s access to other objects. We contribute a formulation of the object search by manipulation problem using visibility and accessibility relations between...
We investigate the problem of a robot searching for an object. This requires reasoning about both perception and manipulation: certain objects are moved because the target may be hidden behind them and others are moved because they block the manipulator's access to other objects. We contribute a formulation of the object search by manipulation problem using visibility and accessibility relations between...
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