The creation of intelligent autonomous robots has been the research focus on many AI researchers. An autonomous robot perceives its environment, selects actions to achieve its goals based on the perceived state of the environment, and then executes the selected actions. This overall cycle is clearly of great relevance to the planning research community.
Symbiotic-autonomous robots are intelligent robots that are capable of complementing their perceptual, reasoning, and actuation capabilities by resourcing to external sources, such as humans, the web, and other artifacts. The robots further learn from their symbiotic interactions with the goal of improving their future tasks.
In this tutorial, I will introduce the core planning challenges for such symbiotic-autonomous robots, and present effective solutions for the underlying task-based planning, execution, and learning. The tutorial will be based on the concrete experience with our CoBot service mobile robots, which move in our multi-floor building performing a variety of pick and delivery tasks. The CoBot robots plan for their navigation and their interaction with humans and with the web. They have moved in our buildings for more than 200km.
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