Probabilistic inverse optimal control for non-linear partially observable systems disentangles perceptual uncertainty and behavioral costs

• URL: http://arxiv.org/abs/2303.16698v2
• Date: Mon, 30 Oct 2023 09:36:44 GMT
• Title: Probabilistic inverse optimal control for non-linear partially observable systems disentangles perceptual uncertainty and behavioral costs
• Authors: Dominik Straub, Matthias Schultheis, Heinz Koeppl, Constantin A. Rothkopf
• Abstract summary: We introduce a probabilistic approach to inverse optimal control for partially observable non-linear systems with unobserved action signals. We show that our method can disentangle perceptual factors and behavioral costs despite the fact that neuroscience and pragmatic actions are intertwined in sequential decision-making under uncertainty.
• Score: 33.690374799743076
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Inverse optimal control can be used to characterize behavior in sequential decision-making tasks. Most existing work, however, is limited to fully observable or linear systems, or requires the action signals to be known. Here, we introduce a probabilistic approach to inverse optimal control for partially observable stochastic non-linear systems with unobserved action signals, which unifies previous approaches to inverse optimal control with maximum causal entropy formulations. Using an explicit model of the noise characteristics of the sensory and motor systems of the agent in conjunction with local linearization techniques, we derive an approximate likelihood function for the model parameters, which can be computed within a single forward pass. We present quantitative evaluations on stochastic and partially observable versions of two classic control tasks and two human behavioral tasks. Importantly, we show that our method can disentangle perceptual factors and behavioral costs despite the fact that epistemic and pragmatic actions are intertwined in sequential decision-making under uncertainty, such as in active sensing and active learning. The proposed method has broad applicability, ranging from imitation learning to sensorimotor neuroscience.

Related papers

• Inverse decision-making using neural amortized Bayesian actors [19.128377007314317]
  We amortize the Bayesian actor using a neural network trained on a wide range of parameter settings in an unsupervised fashion. We show how our method allows for principled model comparison and how it can be used to disentangle factors that may lead to unidentifiabilities between priors and costs.
  arXiv  Detail & Related papers  (2024-09-04T10:31:35Z)
• ACE : Off-Policy Actor-Critic with Causality-Aware Entropy Regularization [52.5587113539404]
  We introduce a causality-aware entropy term that effectively identifies and prioritizes actions with high potential impacts for efficient exploration. Our proposed algorithm, ACE: Off-policy Actor-critic with Causality-aware Entropy regularization, demonstrates a substantial performance advantage across 29 diverse continuous control tasks.
  arXiv  Detail & Related papers  (2024-02-22T13:22:06Z)
• Representation Surgery: Theory and Practice of Affine Steering [72.61363182652853]
  Language models often exhibit undesirable behavior, e.g., generating toxic or gender-biased text. One natural (and common) approach to prevent the model from exhibiting undesirable behavior is to steer the model's representations. This paper investigates the formal and empirical properties of steering functions.
  arXiv  Detail & Related papers  (2024-02-15T00:20:30Z)
• Function-Space Regularization in Neural Networks: A Probabilistic Perspective [51.133793272222874]
  We show that we can derive a well-motivated regularization technique that allows explicitly encoding information about desired predictive functions into neural network training. We evaluate the utility of this regularization technique empirically and demonstrate that the proposed method leads to near-perfect semantic shift detection and highly-calibrated predictive uncertainty estimates.
  arXiv  Detail & Related papers  (2023-12-28T17:50:56Z)
• Model Predictive Control with Gaussian-Process-Supported Dynamical Constraints for Autonomous Vehicles [82.65261980827594]
  We propose a model predictive control approach for autonomous vehicles that exploits learned Gaussian processes for predicting human driving behavior. A multi-mode predictive control approach considers the possible intentions of the human drivers.
  arXiv  Detail & Related papers  (2023-03-08T17:14:57Z)
• Non-Gaussian Uncertainty Minimization Based Control of Stochastic Nonlinear Robotic Systems [9.088960941718]
  We design a state feedback controller that minimizes deviations of the states of the system from the nominal state trajectories due to uncertainties and disturbances. We use moments and characteristic functions to propagate uncertainties throughout the nonlinear motion model of robotic systems.
  arXiv  Detail & Related papers  (2023-03-02T23:31:32Z)
• Active Uncertainty Learning for Human-Robot Interaction: An Implicit Dual Control Approach [5.05828899601167]
  We present an algorithmic approach to enable uncertainty learning for human-in-the-loop motion planning based on the implicit dual control paradigm. Our approach relies on sampling-based approximation of dynamic programming model predictive control problem. The resulting policy is shown to preserve the dual control effect for generic human predictive models with both continuous and categorical uncertainty.
  arXiv  Detail & Related papers  (2022-02-15T20:40:06Z)
• Inverse Optimal Control Adapted to the Noise Characteristics of the Human Sensorimotor System [5.629161809575013]
  We introduce inverse optimal control with signal-dependent noise, which allows inferring the cost function from observed behavior. We derive a probabilistic formulation of the evolution of states and belief states. We extend the model to the case of partial observability of state variables from the point of view of the experimenter.
  arXiv  Detail & Related papers  (2021-10-21T13:30:14Z)
• Probabilistic robust linear quadratic regulators with Gaussian processes [73.0364959221845]
  Probabilistic models such as Gaussian processes (GPs) are powerful tools to learn unknown dynamical systems from data for subsequent use in control design. We present a novel controller synthesis for linearized GP dynamics that yields robust controllers with respect to a probabilistic stability margin.
  arXiv  Detail & Related papers  (2021-05-17T08:36:18Z)
• Combining Gaussian processes and polynomial chaos expansions for stochastic nonlinear model predictive control [0.0]
  We introduce a new algorithm to explicitly consider time-invariant uncertainties in optimal control problems. The main novelty in this paper is to use this combination in an efficient fashion to obtain mean and variance estimates of nonlinear transformations. It is shown how to formulate both chance-constraints and a probabilistic objective for the optimal control problem.
  arXiv  Detail & Related papers  (2021-03-09T14:25:08Z)
• Risk-Sensitive Sequential Action Control with Multi-Modal Human Trajectory Forecasting for Safe Crowd-Robot Interaction [55.569050872780224]
  We present an online framework for safe crowd-robot interaction based on risk-sensitive optimal control, wherein the risk is modeled by the entropic risk measure. Our modular approach decouples the crowd-robot interaction into learning-based prediction and model-based control. A simulation study and a real-world experiment show that the proposed framework can accomplish safe and efficient navigation while avoiding collisions with more than 50 humans in the scene.
  arXiv  Detail & Related papers  (2020-09-12T02:02:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.