Nonparametric Inverse Dynamic Models for Multimodal Interactive Robots
This work addresses the challenge of enabling semi-autonomous robots to handle complex tasks with intermittent dynamic changes, though it appears incremental as it builds on existing nonparametric modeling techniques.
The paper tackled the problem of modeling multimodal inverse dynamics for interactive robots, which encounter varying dynamic modes due to tools or external inputs, by developing a multimodal Gaussian process regression approach that clusters modes from unlabeled data and ensures passivity, with experiments demonstrating performance and safety on a test actuator.
Direct design of a robot's rendered dynamics, such as in impedance control, is now a well-established control mode in uncertain environments. When the physical interaction port variables are not measured directly, dynamic and kinematic models are required to relate the measured variables to the interaction port variables. A typical example is serial manipulators with joint torque sensors, where the interaction occurs at the end-effector. As interactive robots perform increasingly complex tasks, they will be intermittently coupled with additional dynamic elements such as tools, grippers, or workpieces, some of which should be compensated and brought to the robot side of the interaction port, making the inverse dynamics multimodal. Furthermore, there may also be unavoidable and unmeasured external input when the desired system cannot be totally isolated. Towards semi-autonomous robots, capable of handling such applications, a multimodal Gaussian process regression approach to manipulator dynamic modelling is developed. A sampling-based approach clusters different dynamic modes from unlabelled data, also allowing the seperation of perturbed data with significant, irregular external input. The passivity of the overall approach is shown analytically, and experiments examine the performance and safety of this approach on a test actuator.