Optimality principles in sensorimotor control

By E. Todorov. Nature Neuroscience, 2004

1,800 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cognitive Neuroscience, Social Psychology

View Paper: https://www.semanticscholar.org/paper/5eba92dd285d71b6278dc67715dc48a4aa946679

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Citations in the corpus

Motor learning. (2010, background): “Recently, the idea of optimally shaping the control mechanisms to task goals has been formalized in the theoretical framework of optimal feedback control [15] and this has led to a resurgence of interest in feedback control.”
Intuitive Theories (2020, background): “Not only can we do these things (Todorov, 2004) but also can we make remarkably accurate judgments about these events (see, e.g., Battaglia, Hamrick, & Tenenbaum, 2013; Gerstenberg, Goodman, Lagnado, & Tenenbaum, 2012), and explain why they happened (Lombrozo, 2012).”
Dynamical Movement Primitives: Learning Attractor Models for Motor Behaviors (2013, methodology): “As a final remark, there is a rich literature on using optimization approaches to generate goal-directed movement (Kawato, 1996; Todorov, 2004), also discussed in some areas of reinforcement learning (Peters & Schaal, 2008; Theodorou, Buchli, & Schaal, 2010).”
Speech Production as State Feedback Control (2011, background): “…control (SFC) theory to modeling the role of the higher central nervous system (i.e., cortex, the cerebellum, thalamus, and basal ganglia – hereafter referred to as “the CNS”) in motor control (Arbib, 1981; Todorov and Jordan, 2002; Todorov, 2004; Guigon et al., 2008; Shadmehr and Krakauer, 2008).”
Toward a new theory of motor synergies. (2007, methodology): “This back-coupling could be viewed as a neural mechanism that performs something akin to Todorov and Jordan's (2002) " minimum intervention " principle.”
Augmented visual, auditory, haptic, and multimodal feedback in motor learning: A review (2012, methodology): “The feedback should not force the learner to correct task-irrelevant errors (Liu & Todorov, 2007; Todorov, 2004; Todorov & Jordan, 2002; Wei & Körding, 2009; Wolpert et al., 2011).”
Stochastic Optimal Control and Estimation Methods Adapted to the Noise Characteristics of the Sensorimotor System (2005, background): “For stochastic partially observable plants such as the musculoskeletal system, however, open-loop approaches yield suboptimal performance (Todorov & Jordan, 2002b; Todorov, 2004).”
Structure learning in action (2010, background): “Such parametric control laws can be used to study movement generation within the framework of optimal feedback control, and have been successful in explaining a wide range of human motor behaviour [55–61].”
Inverse Optimal Control with Linearly-Solvable MDPs (2010, background): “The former is relevant to cognitive and neural science, where researches are interested in ”theories of mind” (Baker et al., 2007) as well as in identifying the cost functions being optimized by the sensorimotor system (Todorov, 2004; Körding & Wolpert, 2004).”
Efficient computation of optimal actions (2009, background): “Examples include a nervous system generating muscle activations to maximize movement performance (1), a foraging animal deciding which way to turn to maximize food (2), an internet router directing packets to minimize delays (3), an onboard computer controlling a jet engine to minimize fuel consumpt”
Evidence for the Flexible Sensorimotor Strategies Predicted by Optimal Feedback Control (2007, methodology): “Here, we show that the optimal feedback control theory that we (Todorov and Jordan, 2002a,b; Todorov, 2004, 2005; Todorov et al., 2005) and others (Meyer et al., 1988; Loeb et al., 1990; Hoff, 1992; Kuo, 1995; Scott, 2004) have pursued affords the flexibility apparent in behavioral data, in…”
Thermodynamics as a theory of decision-making with information-processing costs (2012, background): “Sensorimotor behaviours can often be described by optimality principles that take into account performance criteria such as energy requirements, endpoint accuracy, smoothness of movement trajectories and other task-related criteria [70,71].”
Compositionality of optimal control laws (2009, background): “One such idea is that biological movements are near-optimal [7, 8].”
Action understanding as inverse planning. (2009, background): “…can model domains and tasks that go well beyond a single agent moving in a simple two-dimensional maze, such as motor action (Körding, 1997; Todorov, 2004), games and other strategic interactions (Filar & Vrieze, 1997; Littman, 1994), and a range of cooperative or communicative activities…”