no code implementations • 26 Jun 2019 • Madhura Thosar, Christian A. Mueller, Georg Jaeger, Johannes Schleiss, Narender Pulugu, Ravi Mallikarjun Chennaboina, Sai Vivek Jeevangekar, Andreas Birk, Max Pfingsthorn, Sebastian Zug
However, due to different sensing and acting capabilities of robots, their conceptual understanding of objects must be generated from a robot's perspective entirely, which asks for robot-centric conceptual knowledge about objects.
Underwater robot interventions require a high level of safety and reliability.
Experiments show that extracted persistent commonality groups can feature semantically meaningful shape concepts; the generalization of the proposed approach is evaluated by different real-world datasets.
We propose a robust gesture-based communication pipeline for divers to instruct an Autonomous Underwater Vehicle (AUV) to assist them in performing high-risk tasks and helping in case of emergency.
Robots require knowledge about objects in order to efficiently perform various household tasks involving objects.
When a robot is operating in a dynamic environment, it cannot be assumed that a tool required to solve a given task will always be available.
Therein surface description and representation of object shape structure have significant influence on shape categorization accuracy, when dealing with real-world scenes featuring noisy, partial and occluded object observations.