4 accepted papers
This paper tackles the challenges of automating battery removal from small electronic devices, such as heat cost allocators and smoke detectors. The process is critical for mitigating fire hazards caused by lithium batteries in recycling facilities and for supporting a circular economy. We focus on …
The accumulation of a sufficient amount of data for training deep neural networks is a major hindrance in the application of deep learning in robotics. Acquiring real-world data requires considerable time and effort, yet it might still not capture the full range of potential environmental variations
The choice of an appropriate representation is important when reconstructing motion from images. In this letter we propose a new type of deep neural network (DNN) that maps images to spatial paths represented by a recently introduced motion representation called arc-length dynamic movement primitive
Continuous and on-line adaptation of robotic trajectories is one of the key properties of robotic control policies that make robots useful in unstructured environments, where the conditions of the external world are not predefined or stationary. Modification of robotic trajectories through human int