Bootstrap learning for object discovery

We show how a robot can autonomously learn an ontology of objects to explain aspects of its sensor input from an unknown dynamic world. Unsupervised learning about objects is an important conceptual step in developmental learning, whereby the agent clusters observations across space and time to construct stable perceptual representations of objects. Our proposed unsupervised learning method uses the properties of allocentric occupancy grids to classify individual sensor readings as static or dynamic. Dynamic readings are clustered and the clusters are tracked over time to identify objects, separating them both from the background of the environment and from the noise of unexplainable sensor readings. Once trackable clusters of sensor readings (i. e. , objects) have been identified, we build shape models where they are stable and consistent properties of these objects. However, the representation can tolerate, represent, and track amorphous objects as well as those that have well-defined shape. In the end, the learned ontology makes it possible for the robot to describe a cluttered dynamic world with symbolic object descriptions along with a static environment model, both models grounded in sensory experience, and learned without external supervision.

Authors

• Joseph Modayil
• Benjamin Kuipers

Keywords

• Robot sensing systems
• Ontologies
• Orbital robotics
• Shape
• Unsupervised learning
• Simultaneous localization and mapping
• Mobile robots
• Computer science
• Background noise
• Working environment noise
• Bootstrap Learning
• Time And Space
• Shape Model
• Environmental Background
• Sensor Readings
• Individual Sensors
• Occupancy Grid
• Learning Process
• Learning Algorithms
• Center Of Mass
• Reference Frame
• Pedestrian
• Grid Cells
• Individual Processes
• Background Knowledge
• Object Properties
• Mobile Robot
• Object Tracking
• Learning Agent
• Image Descriptors
• Range Of Sensors
• Permanent Cell
• Dynamic Objects
• Dynamic Sensor
• Laser Ranging
• Standard Imaging
• Representation Of Space