Optimal feedback control as a theory of motor coordination

By E. Todorov, Michael I. Jordan. Nature Neuroscience, 2002

3,165 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Physical Therapy, Sports Therapy and Rehabilitation, Social Psychology

View Paper: https://www.semanticscholar.org/paper/705348322eaa7cf29c5d275cbcd3b5377c14315e

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Citations in the corpus

Computational principles of sensorimotor control that minimize uncertainty and variability (2007, background): “…that minimize endpoint variability under signal-dependent noise accurately reproduce the observed temporal proﬁles of both arm reaching movements and saccadic eye movements (Harris & Wolpert, 1998), and predict the way in which feedback is used to correct movements online (Todorov & Jordan, 2002).”
Theory of cortical function (2017, background): “, squaring), any invertible linear transform of the basis set can be substituted (37-39).”
Learning position and orientation dynamics from demonstrations via contraction analysis (2018, background): “A particular variant of the standard RL approach, in which the reward function is learned from the demonstrations, is called inverse optimal control (IOC) or inverse reinforcement learning (IRL) (Todorov and Jordan 2002; Abbeel and Ng 2004; Laumond et al. 2014; Priess 2014; Laumond et al. 2015).”
The Dynamics of Legged Locomotion: Models, Analyses, and Challenges (2006, background): “The observation that certain degrees of freedom exhibit significantly higher variability than others can be interpreted in the framework of stochastic optimal feedback control as a hedge against noise [320].”
The Bliss of Motor Abundance (2012, positive): “Among more recent approaches to the MR problem is optimal feedback control (Todorov and Jordan 2002).”
Movement Timing and Invariance Arise from Several Geometries (2009, methodology): “The minimum variance principle [62], the optimal feedback control model [63], and the minimum time principle in the presence of signal-dependent noise [64] do predict movement duration, but so far only for point-to-point movements.”
Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. (2017, background): “Optimal feedback control is yet another popular motor control theory (263, 801, 802).”
CNS Learns Stable, Accurate, and Efficient Movements Using a Simple Algorithm (2008, methodology): “, 2001; Singh and Scott, 2003) and feedback (Todorov and Jordan, 2002; Franklin et al., 2007; Kurtzer et al., 2008) control.”
Action and behavior: a free-energy formulation (2010, background): “In conventional motor control theory (Wolpert and Miall 1996; Todorov and Jordan 2002; Shadmehr and Krakauer 2008), there are two models, an inverse model and a forward model.”
From Motor Learning to Interaction Learning in Robots (2010, background): “The chapter highlights the interesting robustness properties of the resulting framework and highlights deep relationships with the optimal control framework that provides convincing computational principles for motor control [22], [23], [24].”