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D.S. van der Heijden

4 records found

Reinforcement learning has emerged as a promising approach for enabling robots to learn from interactions with their environments, without relying on predefined behaviors. However, robots face significant challenges when learning directly from real-world interactions. Real-world ...

EValueAction

A proposal for policy evaluation in simulation to support interactive imitation learning

The up-and-coming concept of Industry 5.0 fore-sees human-centric flexible production lines, where collaborative robots support human workforce. In order to allow a seamless collaboration between intelligent robots and human workers, designing solutions for non-expert users is cr ...

DeepKoCo

Efficient latent planning with a task-relevant Koopman representation

This paper presents DeepKoCo, a novel modelbased agent that learns a latent Koopman representation from images. This representation allows DeepKoCo to plan efficiently using linear control methods, such as linear model predictive control. Compared to traditional agents, DeepKoCo ...
An iterative bias estimation framework is presented that mitigates position-dependent ranging errors often present in ultra-wideband localization systems. State estimation and control are integrated, such that the positioning accuracy improves over iterations. The framework is ex ...