Learning and Reproduction of Gestures by Imitation

By Sylvain Calinon, Florent D'halluin, Eric L. Sauser, Darwin G. Caldwell, Aude Gemma Billard. 2010

469 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering, Meta & Continual Learning, Human-Computer Interaction, Robotics & Control

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In Robotics & Control

Citations in the corpus

Generalization Through Hand-Eye Coordination: An Action Space for Learning Spatially-Invariant Visuomotor Control (2021, background): “Imitation learning (IL) has been widely studied in the context of robotic manipulation tasks [2, 4, 5, 11, 31].”
Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation (2017, neutral): “It has been applied successfully to a wide range of domains in robotics, for example to autonomous driving [4], [5], [6], autonomous helicopter ﬂight [7], gesturing [8], and manipulation [9], [10].”
An Algorithmic Perspective on Imitation Learning (2018, methodology): “To overcome the discreteness of HMMs, recent work uses other techniques in combination with HMMs, such as state specific Gaussian models [Calinon et al., 2010] to represent continuous values such as velocity, spatial position, or force [Racca et al.”
Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-to-End Learning from Demonstration (2017, methodology): “In recent years, LfD was successfully used in the field of robotics for applications as varied as autonomous helicopter maneuvers [1], playing table tennis [2], object manipulation [3], making coffee [4], grasping novel objects [5], carrot grating [6], and eggplant cutting [7].”
Dual arm manipulation - A survey (2012, methodology): “The system in [104] uses Hidden Markov Models and Gaussian Mixture Regression to deal with the variability across several demonstrations and extract redundancies.”
SWIRL: A sequential windowed inverse reinforcement learning algorithm for robot tasks with delayed rewards (2018, methodology): “Often these segments are manually designed or derived from a dictionary of motion primitives, but recently, there are several approaches for learning segmentation criteria automatically by identifying locally similar structure in demonstration data (Barbič et al. 2004; Chiappa and Peters 2010; Alvar”
Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning (2019, background): “E.g., authors used Linear Regression (Schaal et al., 2002; Haruno et al., 2001), Gaussian Mixture Regression (Calinon et al., 2010; Khansari-Zadeh & Billard, 2011), Gaussian Process Regression (Kocijan et al., 2004; Nguyen-Tuong et al., 2009; Nguyen-Tuong & Peters, 2010), Support Vector Regression (”
MimicPlay: Long-Horizon Imitation Learning by Watching Human Play (2023, methodology): “Imitation Learning (IL) has enabled robots to successfully perform various manipulation tasks [1, 4, 9, 14, 15, 22, 26, 40].”
Motion Planning Networks (2018, methodology): “ch environments. Imitation learning is another emerging ﬁeld in which the models are trained from expert demonstrations. Many interesting problems have been addressed through imitation learning [13], [14], [15]. A recent method [16] uses deep neural networks trained via imitation to adaptively sampl”