KnowRobSIM - Game Engine-Enabled Knowledge Processing Towards Cognition-Enabled Robot Control

AI knowledge representation and reasoning methods consider actions to be blackboxes that abstract away from how they are executed. This abstract view does not suffice for the decision making capabilities required by robotic agents that are to accomplish manipulation tasks. Such robots have to reason about how to pour without spilling, where to grasp a pot, how to open different containers, and so on. To enable such reasoning it is necessary to consider how objects are perceived, how motions can be executed and parameterized, and how motion parameterization affects the physical effects of actions. To this end, we propose to complement and extend symbolic reasoning methods with KnowRob SIM, an additional reasoning infrastructure based on modern game engine technology, including the subsymbolic world modeling through data structures, action simulation based on physics engine, and world scene rendering. We demonstrate how KnowRob SIM can perform powerful reasoning, prediction, and learning tasks that are required for informed decision making in object manipulation.

Authors

• Andrei Haidu
• Daniel Beßler
• Asil Kaan Bozcuoglu
• Michael Beetz

Keywords

• Cognition
• Games
• Engines
• Robots
• Force
• Data structures
• Robot Control
• Infrastructure
• Data Structure
• Physical Effects
• Manipulation Tasks
• Knowledge Representation
• Reasoning Tasks
• Physics Engine
• Game Engine
• Robotic Agents
• Knowledge Base
• Global Status
• Virtual World
• Joint Model
• End-effector
• Image Synthesis
• Cognitive Capabilities
• Physical Simulation
• First-order Logic
• Motion Phase
• Real Robot
• Virtual Camera
• Mind’s Eye
• High Volume Of Data
• Mental Simulation
• Object Pose
• Motor Cognition
• Game Environment