6 accepted papers
In this paper, we present an integrated, model-based system for state estimation and control in dynamic manipulation tasks with partial observability. We track a belief over the system state using a particle filter from which we extract a Gaussian Mixture Model (GMM). This compressed representation…
In this paper we propose a new model for sequential manipulation tasks that also considers robot dynamics and time-variant environments. From this model we automatically derive constraint-based controllers and use them as steering functions in a kinodynamic manipulation planner. The resulting plan i…
When robots perform manipulation tasks, they need to determine their own movement, as well as how to make and break contact with objects in their environment. Reasoning about the motions of robots and objects simultaneously leads to a constrained planning problem in a high-dimensional state-space. A…
In this paper, we present an approach to generate robot motions for robust parts assembly. The computation of motions for parts assembly usually requires an exact model of all relevant objects. Generating detailed object models, including friction and dynamics, is often complex and time-consuming, e…
This paper introduces a Bayesian state estimator for contact-rich manipulation tasks with application in non-prehensile manipulation, industrial assembly or in-hand localization. The core idea of our approach is to explicitly model both the contact dynamics and a torque-based robot controller as par…
In automated manufacturing, robots must reliably assemble parts of various geometries and low tolerances. Ideally, they plan the required motions autonomously. This poses a substantial challenge due to high-dimensional state spaces and non-linear contact-dynamics. Furthermore, object poses and model…