Authors:
Mikhail Frank
;
Jürgen Leitner
;
Marijn Stollenga
;
Simon Harding
;
Alexander Förster
and
Jürgen Schmidhuber
Affiliation:
Dalle Molle Institute for Artificial Intelligence (IDSIA), Università della Svizzera Italiana and Scuola Universitaria Professionale della Svizzera Italiana, Switzerland
Keyword(s):
Robotics, Modelling, Simulation, Architecture, Framework, Humanoid, Adaptive Roadmap Planning, Machine Learning, Cooperative Robots, Shared Workspace, Autonomous Adaptive Behavior, Unstructured Environment.
Related
Ontology
Subjects/Areas/Topics:
Engineering Applications
;
Humanoid Robots
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Modeling, Simulation and Architectures
;
Robotics and Automation
;
Signal Processing, Sensors, Systems Modeling and Control
Abstract:
To produce even the simplest human-like behaviors, a humanoid robot must be able to see, act, and react, within a tightly integrated behavioral control system. Although there exists a rich body of literature in Computer Vision, Path Planning, and Feedback Control, wherein many critical subproblems are addressed individually, most demonstrable behaviors for humanoid robots do not effectively integrate elements from all three disciplines. Consequently, tasks that seem trivial to us humans, such as pick-and-place in an unstructured environment, remain far beyond the state-of-the-art in experimental robotics. We view this primarily as a software engineering problem, and have therefore developed MoBeE, a novel behavioral framework for humanoids and other complex robots, which integrates elements from vision, planning, and control, facilitating the synthesis of autonomous, adaptive behaviors. We communicate the efficacy of MoBeE through several demonstrative experiments. We first develop A
daptive Roadmap Planning by integrating a reactive feedback controller into a roadmap planner. Then, an industrial manipulator teaches a humanoid to localize objects as the two robots operate autonomously in a shared workspace. Finally, an integrated vision, planning, control system is applied to a real-world reaching task using the humanoid robot.
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