Exploiting domain knowledge for Object Discovery

In this paper, we consider the problem of Lifelong Robotic Object Discovery (LROD) as the long-term goal of discovering novel objects in the environment while the robot operates, for as long as the robot operates. As a first step towards LROD, we automatically process the raw video stream of an entire workday of a robotic agent to discover objects. We claim that the key to achieve this goal is to incorporate domain knowledge whenever available, in order to detect and adapt to changes in the environment. We propose a general graph-based formulation for LROD in which generic domain knowledge is encoded as constraints. Our formulation enables new sources of domain knowledge—metadata—to be added dynamically to the system, as they become available or as conditions change. By adding domain knowledge, we discover 2. 7· more objects and decrease processing time 190 times. Our optimized implementation, HerbDisc, processes 6 h 20 min of RGBD video of real human environments in 18 min 30 s, and discovers 121 correct novel objects with their 3D models.

Authors

• Alvaro Collet
• Bo Xiong
• Corina Gurau
• Martial Hebert
• Siddhartha S. Srinivasa

Keywords

• Visualization
• Three-dimensional displays
• Solid modeling
• Robot sensing systems
• Streaming media
• Shape
• Cognitive Domains
• Raw Video
• Robotic Agents
• Min Video
• Correct Object
• Partial Model
• Undirected
• Data Streams
• Nodes In The Graph
• Boolean Logic
• 3D Point
• Pairwise Similarity
• Disjunction
• 3D Point Cloud
• Hard Constraints
• Visual Similarity
• Single Candidate
• Soft Constraints
• Similarity Graph
• Candidate Generation
• Candidate Pairs
• Candidate Objects
• Ground Truth Object
• Service Robots
• Boolean Algebra
• Robot Localization
• Voxel Grid
• Color Histogram
• Valid Objective
• Graph Partitioning