Learning non-holonomic object models for mobile manipulation

For a mobile manipulator to interact with large everyday objects, such as office tables, it is often important to have dynamic models of these objects. However, as it is infeasible to provide the robot with models for every possible object it may encounter, it is desirable that the robot can identify common object models autonomously. Existing methods for addressing this challenge are limited by being either purely kinematic, or inefficient due to a lack of physical structure. In this paper, we present a physics-based method for estimating the dynamics of common non-holonomic objects using a mobile manipulator, and demonstrate its efficiency compared to existing approaches.

Authors

• Jonathan Scholz
• Martin Levihn
• Charles Isbell
• Henrik I. Christensen
• Mike Stilman

Keywords

• Mobile robots
• Wheels
• Robot sensing systems
• Friction
• Trajectory
• Manipulators
• Mobile Manipulator
• Common Objects
• Dynamical
• Bayesian Inference
• Friction Coefficient
• Adaptive Control
• Target Object
• Physical Constraints
• State Trajectories
• Range Of Objects
• Physical Simulation
• Real Robot
• Static Friction
• Behavior Of Objects
• Constraint Forces
• Single Constraint