Active articulation model estimation through interactive perception

We introduce a particle filter-based approach to representing and actively reducing uncertainty over articulated motion models. The presented method provides a probabilistic model that integrates visual observations with feedback from manipulation actions to best characterize a distribution of possible articulation models. We evaluate several action selection methods to efficiently reduce the uncertainty about the articulation model. The full system is experimentally evaluated using a PR2 mobile manipulator. Our experiments demonstrate that the proposed system allows for intelligent reasoning about sparse, noisy data in a number of common manipulation scenarios.

Authors

• Karol Hausman
• Scott Niekum
• Sarah Osentoski
• Gaurav S. Sukhatme

Keywords

• Robot sensing systems
• Visualization
• Entropy
• Joints
• Uncertainty
• Probabilistic logic
• Joint Model
• Probabilistic Model
• Noisy Data
• Mobile Manipulator
• Model Parameters
• High-dimensional
• Data Visualization
• Actual Results
• Workspace
• Object Recognition
• Information Gain
• Correction Model
• Prismatic
• Visual Model
• Particle Filter
• Pose Estimation
• System Noise
• Sensor Model
• Fiducial Markers
• Entropy Of Distribution
• Rigid Model
• Probabilistic Graphical Models
• Object Pose
• World Frame
• Experimental Scenarios
• Active Vectors
• Sensor Measurements