• Corpus ID: 239050542

Inverse Optimal Control Adapted to the Noise Characteristics of the Human Sensorimotor System

@article{Schultheis2021InverseOC,
  title={Inverse Optimal Control Adapted to the Noise Characteristics of the Human Sensorimotor System},
  author={Matthias Schultheis and Dominik Straub and Constantin A. Rothkopf},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.11130}
}
Computational level explanations based on optimal feedback control with signaldependent noise have been able to account for a vast array of phenomena in human sensorimotor behavior. However, commonly a cost function needs to be assumed for a task and the optimality of human behavior is evaluated by comparing observed and predicted trajectories. Here, we introduce inverse optimal control with signaldependent noise, which allows inferring the cost function from observed behavior. To do so, we… 
Putting perception into action: Inverse optimal control for continuous psychophysics
TLDR
A computational analysis framework for continuous psychophysics based on Bayesian inverse optimal control is introduced and it is shown that this not only recovers the perceptual thresholds but additionally estimates subjects’ action variability, internal behavioral costs, and subjective beliefs about the experimental stimulus dynamics.

References

SHOWING 1-10 OF 70 REFERENCES
Stochastic Optimal Control and Estimation Methods Adapted to the Noise Characteristics of the Sensorimotor System
  • E. Todorov
  • Computer Science, Medicine
    Neural Computation
  • 2005
TLDR
Under this extended noise model, a coordinate-descent algorithm guaranteed to converge to a feedback control law and a nonadaptive linear estimator optimal with respect to each other is derived.
Learning an Internal Dynamics Model from Control Demonstration
TLDR
A probabilistic framework and exact EM algorithm are developed to jointly estimate the internal model, internal state trajectories, and feedback delay of a nonhuman primate of brain-machine interface (BMI) control and it is discovered that the subject's internal model deviated from the true BMI plant dynamics and provided significantly better explanation of the recorded neural control signals.
Risk-Sensitive Optimal Feedback Control Accounts for Sensorimotor Behavior under Uncertainty
TLDR
It is shown that subjects change their movement strategy pessimistically in the face of increased uncertainty in accord with the predictions of a risk-averse optimal controller, suggesting that risk-sensitivity is a fundamental attribute that needs to be incorporated into optimal feedback control models.
Predictive Inverse Optimal Control for Linear-Quadratic-Gaussian Systems
TLDR
This work establishes close connections between optimal control laws for this setting and the probabilistic predictions under their approach and demonstrates the effectiveness and benefit in estimating control policies that are influenced by partial observability on both synthetic and real datasets.
I See What You See: Inferring Sensor and Policy Models of Human Real-World Motor Behavior
TLDR
This work presents the first general approach for joint inference of sensor and policy models, and considers its concrete implementation in the important class of sensor scheduling linear quadratic Gaussian problems.
Optimal feedback control as a theory of motor coordination
TLDR
This work shows that the optimal strategy in the face of uncertainty is to allow variability in redundant (task-irrelevant) dimensions, and proposes an alternative theory based on stochastic optimal feedback control, which emerges naturally from this framework.
Modular inverse reinforcement learning for visuomotor behavior
TLDR
A modular inverse reinforcement learning algorithm is introduced that estimates the relative reward contributions of the component tasks in navigation, consisting of following a path while avoiding obstacles and approaching targets.
Inferring human subject motor control intent using inverse MPC
TLDR
This work proposes an inverse model predictive control algorithm (iMPC) that can recover the true cost function weights, and outperform the iLQR approach for the illustrative example.
Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization
TLDR
This work explores how inverse optimal control (IOC) can be used to learn behaviors from demonstrations, with applications to torque control of high-dimensional robotic systems and an efficient sample-based approximation for MaxEnt IOC.
The Inverse Problem of Continuous-Time Linear Quadratic Gaussian Control With Application to Biological Systems Analysis
In this paper, we demonstrate two methods for solving the inverse problem of continuous-time LQG control. This problem can be defined as: given a known LTI system with feedback controller K and
...
1
2
3
4
5
...