CogMan
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The CogMan project (1) develops computational and control models of pick-and-place tasks in the context of
everyday manipulation activities in human environments, (2) implements the model into a control system for
the kitchen scenario, and (3) empirically analyzes the impact of this control model on the flexibility, robustness,
adaptability, and naturality of the robot behavior.
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Research Topics
- Human and Robot Manipulation
- Human Motion Capture, Tracking and Analysis
- Human Motions in Manipulation Tasks
- Human Motions as Model for Robot Controllers
- Motion Primitives
- Object Tracking for Manipulation Tasks
- Learning Grounded Models
- Motor Control
Application Domain
- Intelligent Kitchen
Project Details
A very impressive aspect of human manipulation of objects in their everyday environments is that they select and parametrize their reaching and grasping movements very skillfully based on the properties of the objects they are to pick up, on the situational circumstances, and on the end positions of the objects. Actions with two collaborators are also adjusted accordingly. The results are very smooth, predictable, and efficient compound movements. Not only do people perform these actions skillfully but they are also capable of learning these skills from little experience and adapt them automatically when needed. Robots that are to serve as robotic assistants and work together with humans need a similar level of manipulation skills and equally powerful means for skill acquisition and adaptation
Based on experiments analyzing human manipulation tasks from the cognitive psychology point of view, we develop a model of how humans select and parametrize grasps, reaching movements, standing positions, and destinations based on the task context, the object, and the goals of the manipulation activity. Using these information pieces we learn behavior models from the observed data and interpret the findings in the light of cognitive control models.
The novel aspect of the CogMan research approach is that we perform comprehensive experiments of human everyday manipulation activities taking the context into account. Therefore, we use more comprehensive sensor data including estimation of fullbody pose, hand pose, visual attention, various biosignals, model and pose of the manipulated object, force used for lifting, kind of grasp, grasp points, local scene around the object, activity context, etc.
Media
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