Robot Introspection with Bayesian Nonparametric Vector Autoregressive Hidden Markov Models [arxiv]
Hongmin Wu, Juan Rojas, Hongbin Lin, and Kensuke Harada
Abstract:
Robot introspection, as opposed to anomaly detection typical in process monitoring systems, is interested in helping the robot understand what it is doing at all times. The robot should be able to identify its actions not only when failure or novelty occurs, but also as it executes any number of sub-tasks. As robots continue their quest of functioning in unstructured environments, it is imperative they understand what is it that they are actually doing to render them more robust. This work investigates the modeling ability of Bayesian nonparametric techniques on Markov Switching Process to learn complex dynamics typical in robot contact tasks. We study whether the Markov switching process, together with Bayesian priors can outperform the modeling ability of its counterparts: an HMM with Bayesian priors and without. The work was tested in a snap assembly task characterized by high elastic forces. The task consists of an insertion subtask with very complex dynamics. Our approach showed a stronger ability to generalize and was able to better model the subtask with complex dynamics and in a computationally efficient way. The modeling technique is also used to learn a growing library of robot skills, one that when integrated with low-level control allows for robot online decision making.
Resources:
- Code
- Simulation Environment OpenHRP 3.0.8 used in Linux 10.04:
https://fkanehiro.github.io/openhrp3-doc/en/ - Manipulation Strategy for Assembly Task:
https://github.com/birlrobotics/PivotApproach - Introspection Algorithms:
- HMM (v0.1)
https://github.com/birlrobotics/HMM/archive/v0.1.zip - sHDP-VAR-HMM (v0.1)
https://github.com/birlrobotics/sHDP-AR-HMM/archive/v0.1.zip
- HMM (v0.1)
- Simulation Environment OpenHRP 3.0.8 used in Linux 10.04:
- DATA:
- Video
- 1) Nominal State Classification via sHDP-HMM here.
- 2) Nominal State Classification via sHDP-VAR(1)-HMM here.
- 3) Nominal State Classification via sHDP-VAR(2)-HMM here.
Copyright Notice:
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder.