The development of flexible mobile manipulation robots is widely envisioned as a large breakthrough in technology and is expected to have a significant impact on our economy and society in the future. Mobile manipulation robots that are equipped with one or more gripper arms could fulfill various useful services in private homes such as cleaning, tidying up, or cooking, which would result in a significant time benefit to their owners. By supporting elderly and mobility-impaired people in the activities of daily life, such robots can reduce the dependency on external caregivers and can support them to live a self-determined and autonomous life. Small and medium-sized enterprises would profit enormously from robotic co-workers that they can easily reconfigure to new production tasks. This technology would significantly reduce the production costs of smaller companies and thus provide them with a significant competitive advantage. The challenge in these applications is that robots operating in unstructured environments have to cope with less prior knowledge about themselves and their surroundings. Therefore, they need to be able to autonomously learn suitable models from their own sensor data to robustly fulfill their tasks.
KeywordsBayesian Network Body Schema Kinematic Structure Legged Robot Unstructured Environment
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