Biologically-inspired dynamical systems for movement generation: Automatic real-time goal adaptation and obstacle avoidance

By Heiko Hoffmann, Peter Pastor, Dae-Hyung Park, Stefan Schaal. 2009

304 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, Control and Systems Engineering

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Citations in the corpus

Skill learning and task outcome prediction for manipulation (2011, methodology): “In this paper, the realization of the transformation system presented in [16], [ 17 ] has been chosen ‐ this is a special variant of the original DMP formulation that added robustness towards some numerical characteristics of the original DMPs, but has only reduced (localized) generalization as glob”
Latent spaces for dynamic movement primitives (2009, methodology): “Recently a reformulation of DMPs has been proposed [ 10 ] which is suited for applying DMPs in task space and allows for automatic obstacle avoidance.”
Robot motor skill coordination with EM-based Reinforcement Learning (2010, methodology): “The above formulation shares similarities with the Dynamic Movement Primitives (DMP) framework originally proposed by Ijspeert et al [22], and further extended in [13], [ 23 ] (see [24] for a discussion on the similarities of the proposed controller with DMP).”
Guiding Trajectory Optimization by Demonstrated Distributions (2017, background): “A typical way to achieve obstacle avoidance is to set repulsive motions around obstacles or to diminish motions towards obstacles [12], [13].”
Learning and generalization of motor skills by learning from demonstration (2009, methodology): “We replace the transformation system by the following equations [7]:”
Probabilistic model-based imitation learning (2013, background): “, real-time goal adaption [17] or generalizing movements with a mixture of DMPs [23].”
Imitation Learning of Positional and Force Skills Demonstrated via Kinesthetic Teaching and Haptic Input (2011, methodology): “The Dynamic Movement Primitives (DMP) framework originally proposed by Ijspeert et al [19], and further extended in [20,21] (see [22] for a discussion on the similarities of the proposed controller with DMP) is extended by considering a full matrix K P i associated with each of the K primitives (or…”
A Dynamical System-based Approach to Modeling Stable Robot Control Policies via Imitation Learning (2012, unknown): “, 2009), flight control (Perk & Slotine, 2006), obstacle avoidance (Park et al., 2008; Hoffmann et al., 2009), lifting (Bitzer & Vijayakumar, 2009), playing table-tennis (Kober, Mulling, et al.”
Dynamical Movement Primitives: Learning Attractor Models for Motor Behaviors (2013, background): “Fajen and Warren (2003) suggested a model for human obstacle avoidance that is nicely suited to demonstrate the power of coupling terms in our approach (Hoffmann et al., 2009).”
Spatial adaption of robot trajectories based on laplacian trajectory editing (2015, unknown): “Instead of programming every single movement by hand, the robot imitates a demonstrated movement either through physical guidance also known as kinesthetic teaching (Lee and Ott 2011) or through suitable learning and adaption meth-ods (Schaal 2006; Hoffmann et al. 2009).”