Imagination-enabled Robot Perception

Many of today’s robot perception systems aim at accomplishing perception tasks that are too simplistic and too hard. They are too simplistic because they do not require the perception systems to provide all the information needed to accomplish manipulation tasks. Typically the perception results do not include information about the part structure of objects, articulation mechanisms and other attributes needed for adapting manipulation behavior. On the other hand, the perception problems stated are also too hard because — unlike humans— the perception systems cannot leverage the expectations about what they will see to their full potential. Therefore, we investigate a variation of robot perception tasks suitable for robots accomplishing everyday manipulation tasks, such as household robots or a robot in a retail store. In such settings it is reasonable to assume that robots know most objects and have detailed models of them. We propose a perception system that maintains its beliefs about its environment as a scene graph with physics simulation and visual rendering. When detecting objects, the perception system retrieves the model of the object and places it at the corresponding place in a VR-based environment model. The physics simulation ensures that object detections that are physically not possible are rejected and scenes can be rendered to generate expectations at the image level. The result is a perception system that can provide useful information for manipulation tasks.

Authors

• Patrick Mania
• Franklin Kenghagho Kenfack
• Michael Neumann
• Michael Beetz

Keywords

• Visualization
• Object detection
• Rendering (computer graphics)
• Task analysis
• Robots
• Physics
• Intelligent robots
• Perception Of The Robot
• Perceptual Task
• Manipulation Tasks
• Perceptual System
• Retail Stores
• Physical Simulation
• Scene Graph
• Specific Tasks
• Sensor Data
• Object Recognition
• Generative Adversarial Networks
• RGB Images
• Income Data
• Pose Estimation
• Semantic Knowledge
• Potential Subjects
• Virtual Objects
• CAD Model
• Image-based Methods
• Belief State
• Current Beliefs
• Mental Simulation
• Hellinger Distance
• Object Pose
• Color Histogram
• Human Pose Estimation
• Robot State
• Action Plan
• Joint Model