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

arXiv: https://arxiv.org/abs/2110.11130 · HTML: https://mlsanity.com/paper/2110.11130/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 65 references that are in the map.
- First posted on arXiv 21 Oct 2021.

## Builds on (most cited first, 11 of 11)

- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [End-to-End Training of Deep Visuomotor Policies](https://mlsanity.com/paper/1504.00702/) (2015)
- [Learning Robust Rewards with Adversarial Inverse Reinforcement Learning](https://mlsanity.com/paper/1710.11248/) (2017)
- [An Algorithmic Perspective on Imitation Learning](https://mlsanity.com/paper/1811.06711/) (2018)
- [Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization](https://mlsanity.com/paper/1603.00448/) (2016)
- [Transfer Learning in Deep Reinforcement Learning: A Survey](https://mlsanity.com/paper/2009.07888/) (2020)
- [Online Bayesian Goal Inference for Boundedly-Rational Planning Agents](https://mlsanity.com/paper/2006.07532/) (2020)
- [Scalable Bayesian Inverse Reinforcement Learning](https://mlsanity.com/paper/2102.06483/) (2021)
- [Where Do You Think You're Going?: Inferring Beliefs about Dynamics from Behavior](https://mlsanity.com/paper/1805.08010/) (2018)
- [Improving the Flexibility and Robustness of Model-Based Derivative-Free Optimization Solvers](https://mlsanity.com/paper/1804.00154/) (2018)
- [Active Sensing as Bayes-Optimal Sequential Decision Making](https://mlsanity.com/paper/1408.2056/) (2014)

## Built on by (most cited first, 4 of 4)

- [Highly Efficient Self-Adaptive Reward Shaping for Reinforcement Learning](https://mlsanity.com/paper/2408.03029/) (2024)
- [Reinforcement Learning with Non-Exponential Discounting](https://mlsanity.com/paper/2209.13413/) (2022)
- [Probabilistic inverse optimal control for non-linear partially observable systems disentangles perceptual uncertainty and behavioral costs](https://mlsanity.com/paper/2303.16698/) (2023)
- [Inverse decision-making using neural amortized Bayesian actors](https://mlsanity.com/paper/2409.03710/) (2024)

## Abstract (arXiv)

Computational level explanations based on optimal feedback control with signal-dependent 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 signal-dependent noise, which allows inferring the cost function from observed behavior. To do so, we formalize the problem as a partially observable Markov decision process and distinguish between the agent's and the experimenter's inference problems. Specifically, we derive a probabilistic formulation of the evolution of states and belief states and an approximation to the propagation equation in the linear-quadratic Gaussian problem with signal-dependent noise. We extend the model to the case of partial observability of state variables from the point of view of the experimenter. We show the feasibility of the approach through validation on synthetic data and application to experimental data. Our approach enables recovering the costs and benefits implicit in human sequential sensorimotor behavior, thereby reconciling normative and descriptive approaches in a computational framework.