Discovering optimal imitation strategies

By Aude Gemma Billard, Yann Epars, Sylvain Calinon, Stefan Schaal, Gordon Cheng. 2004

202 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering, Human-Computer Interaction

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Citations in the corpus

Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation (2017, background): “Behavioral cloning has led to many successes in robotics when a low-dimensional representation of environment state is available [31], [32], [33], [34].”
One-Shot Visual Imitation Learning via Meta-Learning (2017, background): “Efﬁcient imitation from a small number of demonstrations has been successful in scenarios where the state of the environment, such as the poses of objects, is known [8, 9, 10, 11].”
BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning (2022, positive): “Imitation learning has been successful in learning grasping and pick-place tasks from lowdimensional state [9, 10, 11, 12, 13, 14, 15].”
Correspondence Mapping Induced State and Action Metrics for Robotic Imitation (2007, background): “See [9] and [24] for robotic examples and [8], [12], and [14] for ethological and psychological aspects.”
Statistical Learning by Imitation of Competing Constraints in Joint Space and Task Space (2009, background): “Generic approaches to transfer new skills to a robot are those that allow the robot to extract automatically what are the important features characterizing the skill and to search for a controller that optimizes the reproduction of these characteristic features [3].”
Incremental learning of gestures by imitation in a humanoid robot (2007, background): “The system takes inspiration in a recent trend of research that aims at defining a formal mathematical framework for imitation learning [19, 15, 3].”
Imitation learning of globally stable non-linear point-to-point robot motions using nonlinear programming (2010, unknown): “Following [4], we assume that the relevant features of the movement, i.e. those to imitate, are the features that appear most frequently, i.e. the invariants across the demonstration.”
Discriminative and adaptive imitation in uni-manual and bi-manual tasks (2006, methodology): “The method that we used relied on computing the statistical variability of each element of the task; see [9,6].”
Evaluation of robot imitation attempts: comparison of the system's and the human's perspectives (2006, unknown): “The major directions of work have been using such behaviours with humans for adaptive learning [8, 12, 6, 3, 7] and also allowing the robots to be able to engage in richer, more natural social interactions [5, 11, 4].”
Learning Stable Nonlinear Dynamical Systems With Gaussian Mixture Models (2011, background): “Following [5], we assume that the relevant features of the movement, i.”
A Developmental Roadmap for Learning by Imitation in Robots (2007, background): “The problem of inferring the important parts of the task was addressed in [10] by casting it into an optimization framework.”
Motor primitives in vertebrates and invertebrates. (2005, background): “Thus, several robotic studies have focused on, for example, what is a good set of primitives, how they can be mathematically extracted [10] and formalized [8,9], and how they can be used for robot learning by imitating human movements [7,72,73].”
Incremental learning of full body motion primitives and their sequencing through human motion observation (2012, background): “…2006; Ito and Tani 2004; Andry et al. 2004), stochastic models such as hidden Markov models (HMMs) (Inamura et al. 2004; Takano et al. 2006, 2005; Billard et al. 2006, 2004; Asfour et al. 2006), phase oscillators for rhythmic motions (Nakanishi et al. 2004), dynamical models (Ijspeert et al.…”
Abstraction Levels for Robotic Imitation: Overview and Computational Approaches (2010, background): “In any case, it is often not clear whether imitation concerns the motor intention or the underlying goal of that motor intention [17, 23, 79, 127].”
The challenges of joint attention (2006, unknown): “(Schaal, 1999; Breazeal, 2002; Billard et al., 2004 )).”