Learning Robot Manipulation Tasks With Task-Parameterized Semitied Hidden Semi-Markov Model

Citations Over TimeTop 10% of 2016 papers

Abstract

In this letter, we investigate the semitied Gaussian mixture models for robust learning and adaptation of robot manipulation tasks. We make use of the spatial and temporal correlation in the data by tying the covariance matrices of the mixture model with common synergistic directions/basis vectors, instead of estimating full covariance matrices for each cluster in the mixture. This allows the reuse of the discovered synergies in different parts of the task having similar coordination patterns. We extend the approach to task-parameterized and hidden semi-Markov models for autonomous adaptation to changing environmental situations. The planned movement sequence from the model is smoothly followed with a finite horizon linear quadratic tracking controller. Experiments to encode whole body motion data in simulation, followed by valve opening and pick-and-place via obstacle avoidance tasks with the Baxter robot, show improvement over standard Gaussian mixture models with much less parameters and better generalization ability.

Related Papers

• → Learning Task-Parameterized Skills From Few Demonstrations(2022)31 cited
• → Gaussian mixture model with an extended ultrametric covariance structure(2022)12 cited
• → Toward robust learning of the Gaussian mixture state emission densities for hidden Markov models(2010)15 cited
• → Combination of two programming by demonstration techniques to improve the quality of a grasp(2016)1 cited
• → Learning Task-Parameterized Skills from Few Demonstrations(2022)3 cited