Optimal Parameter Identification for Discrete Mechanical Systems with Application to Flexible Object Manipulation
This work addresses system identification for flexible object manipulation, which is incremental as it builds on existing methods by extending to closed-loop systems.
The authors tackled the problem of system identification for flexible objects by modeling them as chains of rigid bodies with torsional springs and using an optimal control approach with variational integrators to identify closed loops including the robot arm, enabling planning in the robot's configuration space; they demonstrated feasibility through physics simulation and data from a 7-DOF series elastic robot arm.
We present a method for system identification of flexible objects by measuring forces and displacement during interaction with a manipulating arm. We model the object's structure and flexibility by a chain of rigid bodies connected by torsional springs. Unlike previous work, the proposed optimal control approach using variational integrators allows identification of closed loops, which include the robot arm itself. This allows using the resulting models for planning in configuration space of the robot. In order to solve the resulting problem efficiently, we develop a novel method for fast discrete-time adjoint-based gradient calculation. The feasibility of the approach is demonstrated using full physics simulation in trep and using data recorded from a 7-DOF series elastic robot arm.