A syntactic approach to robot imitation learning using probabilistic activity grammars

By Kyuhwa Lee, Yanyu Su, Tae-Kyun Kim, Yiannis Demiris. 2013

67 citations recorded by the source corpus.

This paper describes a syntactic approach to imitation learning that captures important task structures in the form of probabilistic activity grammars from a reasonably small number of samples under noisy conditions. We show that these learned grammars can be recursively applied to help recognize unforeseen, more complicated tasks that share underlying structures. The grammars enforce an observation to be consistent with the previously observed behaviors which can correct unexpected, out-of-context actions due to errors of the observer and/or demonstrator. To achieve this goal, our method (1) actively searches for frequently occurring action symbols that are subsets of input samples to uncover the hierarchical structure of the demonstration, and (2) considers the uncertainties of input symbols due to imperfect low-level detectors. We evaluate the proposed method using both synthetic data and two sets of real-world humanoid robot experiments. In our Towers of Hanoi experiment, the robot learns the important constraints of the puzzle after observing demonstrators solving it. In our Dance Imitation experiment, the robot learns 3 types of dances from human demonstrations. The results suggest that under reasonable amount of noise, our method is capable of capturing the reusable task structures and generalizing them to cope with recursions.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Control and Systems Engineering

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Citations in the corpus

Combining self-supervised learning and imitation for vision-based rope manipulation (2017, background): “Many past works have proposed good solutions to learning from visual demonstrations [29], [30], [4].”
One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning (2018, background): “problem by hand, for example, by manually specifying how human grasp poses correspond to robot grasps [20] or by manually defining how human activities or commands translate into robot actions [58, 23, 30, 37, 40].”
AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human Videos (2019, methodology): “To handle human videos, some prior meth-ods have used explicit pose and object detection [11]–[13], predictive modeling [14], [15], context translation [16], [17], and learning reward representations [18], [19].”
Robot Learning Manipulation Action Plans by "Watching" Unconstrained Videos from the World Wide Web (2015, methodology): “Many of the approaches rely on RGBD sensors (Summers-Stay et al. 2013), motion sensors (Guerra-Filho, Fermüller, and Aloimonos 2005; Li et al. 2010) or specific color markers (Lee et al. 2013).”