The central nervous system stabilizes unstable dynamics by learning optimal impedance

By Etienne Burdet, Rieko Osu, David W. Franklin, Theodore E. Milner, Mitsuo Kawato. Nature, 2001

1,160 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cognitive Neuroscience, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/a8bd98a38339685d39241de7d4053c47ebc96775

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In Biomedical Engineering

In Cognitive Neuroscience

In Control and Systems Engineering

Citations in the corpus

Principles of sensorimotor learning (2011, background): “Not only can people scale their overall hand stiffness, or compliance, but they can also shape the pattern of stiffness either by varying muscle activations or the posture of the arm 36,37 .”
Evidence for the Flexible Sensorimotor Strategies Predicted by Optimal Feedback Control (2007, background): “However, other studies have provided more direct evidence: increased muscle cocontraction has been found to yield more accurate movements (Burdet et al., 2001; Gribble et al., 2003), and yet this is not a strategy that subjects normally use, suggesting that they care about energetic efficiency in…”
Computational principles of sensorimotor control that minimize uncertainty and variability (2007, background): “Adapted with permission from Burdet et al. (2001). have observed increased climbing ﬁbre activity during initial movements under a novel load or changed visual feedback (Gilbert & Thach, 1977; Ojakangas & Ebner, 1992).”
Adaptive Fuzzy Neural Network Control for a Constrained Robot Using Impedance Learning (2018, background): “The main challenge in this research field is to investigate physical robot–environment interaction problems [44].”
Computational mechanisms of sensorimotor control. (2011, neutral): “However, such adaptive responses, delayed by 70 ms, may be too late to prevent a task failure, especially in an unstable environment (Burdet et al., 2001).”
Short- and long-term changes in joint co-contraction associated with motor learning as revealed from surface EMG. (2002, methodology): “1Kawato Dynamic Brain Project, Japan Science and Technology Corporation and 2ATR Human Information Science Laboratories, Department 3, 2-2-2 Hikaridai, Soraku-gun, Kyoto 619-0288, Japan; 3School of Kinesiology, Simon Fraser University, Burnaby, British V5A 1S6, Canada; 4Intelligent Communication…”
Role of cocontraction in arm movement accuracy. (2003, positive): “Devices that are capable of perturbing the limb in different directions and measuring restoring forces have been used in the past to characterize the impedance of the limb (Burdet et al. 2001; Flash and Mussa-Ivaldi 1990; Gomi and Haggard 2001; Shadmehr et al. 1993; Tsuji et al. 1995) and other arti”
Optimal feedback control and the neural basis of volitional motor control (2004, background): “Furthermore, this long-latency response is modified to incorporate the influence of mechanical loads during motor learnin”
A Framework to Describe, Analyze and Generate Interactive Motor Behaviors (2012, background): “It is only in the last 40 years that robotic interfaces have been used to investigate how humans interact with the environment (e.g., [6–8]) and with each other (e.g., [9,10]) to perform a variety of tasks.”
Motor Adaptation as a Process of Reoptimization (2008, positive): “The trajectory or feedback response that was optimum in one environment is unlikely to remain optimum in the new environment (Wang et al., 2001; Burdet et al., 2001; Diedrichsen, 2007; Emken et al., 2007).”
Adaptive Neural Impedance Control of a Robotic Manipulator With Input Saturation (2016, background): “The main challenge in this field is to investigate the physical robot–environment interaction problem intensively, which has become popular on the development side [6], [7].”
Progress and prospects of the human–robot collaboration (2017, positive): “This is important to note here that the CNS is capable of learning and adaptation to various tasks demands and disturbances, hence contributing to a reduction in the overall physical and cognitive loading while performing tasks with dynamic uncertainties (Burdet et al. 2001; Franklin et al. 2003).”
Optimality principles in sensorimotor control (2004, background): “can completely subsume smoothness optimization, it is clear that energetics also factor into many tasks—including arm movements (ref”
CNS Learns Stable, Accurate, and Efficient Movements Using a Simple Algorithm (2008, background): “For both the VF (dashed) and DF (solid), the stiffness (comprising both intrinsic and reflexive stiffness) changed during learning, resulting in the same characteristics as found experimentally (Burdet et al., 2001; Franklin et al., 2003b) (Fig.”
Variable Impedance Control of Redundant Manipulators for Intuitive Human–Robot Physical Interaction (2015, background): “More accurate measurements methods of human impedance have been adopted by neuroscientists to analyze human movement control [9] and, in particular, the strategy used by the human central nervous system to deal with instability [10].”
Endpoint Stiffness of the Arm Is Directionally Tuned to Instability in the Environment (2007, methodology): “However, our previous work suggests that although this strategy may occur early in learning (Franklin et al., 2004), after training, subjects selectively increase the stiffness in the direction of the instability (Burdet et al., 2001; Franklin et al., 2003a, 2004).”