Nonparametric Inverse Dynamic Models for Multimodal Interactive Robots
Kevin Haninger, Masayoshi Tomizuka

TL;DR
This paper introduces a nonparametric, multimodal Gaussian process regression method for modeling inverse dynamics in interactive robots, effectively handling complex, uncertain interactions and external disturbances.
Contribution
It proposes a novel clustering-based Gaussian process approach for multimodal inverse dynamic modeling, enabling better handling of unmeasured interactions and external inputs in robotic systems.
Findings
The method accurately models complex dynamic modes.
It effectively separates perturbed data with external inputs.
Experiments demonstrate improved safety and performance.
Abstract
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,…
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Taxonomy
TopicsGaussian Processes and Bayesian Inference · Control Systems and Identification · Advanced Multi-Objective Optimization Algorithms
