Parameter-Robust MPPI for Safe Online Learning of Unknown Parameters

• URL: http://arxiv.org/abs/2601.02948v1
• Date: Tue, 06 Jan 2026 11:44:11 GMT
• Title: Parameter-Robust MPPI for Safe Online Learning of Unknown Parameters
• Abstract summary: We propose a framework that integrates online parameter learning with safety constraints.<n>PRMPPI maintains a particle-based belief over parameters via Stein Variational Gradient Descent.<n>Experiments demonstrate higher success rates, lower tracking error, and more accurate parameter estimates than baselines.
• Score: 23.009285809189056
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robots deployed in dynamic environments must remain safe even when key physical parameters are uncertain or change over time. We propose Parameter-Robust Model Predictive Path Integral (PRMPPI) control, a framework that integrates online parameter learning with probabilistic safety constraints. PRMPPI maintains a particle-based belief over parameters via Stein Variational Gradient Descent, evaluates safety constraints using Conformal Prediction, and optimizes both a nominal performance-driven and a safety-focused backup trajectory in parallel. This yields a controller that is cautious at first, improves performance as parameters are learned, and ensures safety throughout. Simulation and hardware experiments demonstrate higher success rates, lower tracking error, and more accurate parameter estimates than baselines.

Related papers

• HJ-SafeDMP: Hamilton-Jacobi Reachability-Guided Dynamic Movement Primitives for Provably Safe Robot Motion [0.8594140167290097]
  This paper introduces HJ-SafeDMP, a framework that integrates Dynamic Movement Primitives with learned Reachability-based safety value functions.<n>We show that HJ-SafeDMP achieves formal safety guarantees with orders-of-magnitude faster execution than optimization-based baselines.
  arXiv  Detail & Related papers  (2026-06-27T16:15:07Z)
• Staggered Integral Online Conformal Prediction for Safe Dynamics Adaptation with Multi-Step Coverage Guarantees [5.395687668363633]
  We propose Staggered Integral Online Conformal Prediction (SI-OCP), an algorithm utilizing an integral score function to quantify the lumped effect of disturbance and learning error.<n>We validate the proposed approach through a numerical simulation of an all-layer deep neural network (DNN) adaptive quadcopter.
  arXiv  Detail & Related papers  (2026-04-07T16:44:06Z)
• Harmonious Parameter Adaptation in Continual Visual Instruction Tuning for Safety-Aligned MLLMs [49.76354497916853]
  Harmonious Adaptation (HPA) is a post-training framework composed of focusing-based parameter partition, harmonious balanced parameter selection, and parameter adjustment.<n>HPA better maintains high safety and mitigates forgetting than existing baselines.<n> experiments on the CVIT benchmark and safety evaluation datasets demonstrate that HPA better maintains high safety and mitigates forgetting than existing baselines.
  arXiv  Detail & Related papers  (2025-11-25T10:34:51Z)
• Rethinking Safety in LLM Fine-tuning: An Optimization Perspective [56.31306558218838]
  We show that poor optimization choices, rather than inherent trade-offs, often cause safety problems, measured as harmful responses to adversarial prompts.<n>We propose a simple exponential moving average (EMA) momentum technique in parameter space that preserves safety performance.<n>Our experiments on the Llama families across multiple datasets demonstrate that safety problems can largely be avoided without specialized interventions.
  arXiv  Detail & Related papers  (2025-08-17T23:46:36Z)
• Real-Time Adaptive Safety-Critical Control with Gaussian Processes in High-Order Uncertain Models [14.790031018404942]
  This paper presents an adaptive online learning framework for systems with uncertain parameters. We first integrate a forgetting factor to refine a variational sparse GP algorithm. In the second phase, we propose a safety filter based on high-order control barrier functions.
  arXiv  Detail & Related papers  (2024-02-29T08:25:32Z)
• Sensitivity-Aware Visual Parameter-Efficient Fine-Tuning [91.5113227694443]
  We propose a novel visual. sensuous-aware fine-Tuning (SPT) scheme. SPT allocates trainable parameters to task-specific important positions. Experiments on a wide range of downstream recognition tasks show that our SPT is complementary to the existing PEFT methods.
  arXiv  Detail & Related papers  (2023-03-15T12:34:24Z)
• Probabilities Are Not Enough: Formal Controller Synthesis for Stochastic Dynamical Models with Epistemic Uncertainty [68.00748155945047]
  Capturing uncertainty in models of complex dynamical systems is crucial to designing safe controllers. Several approaches use formal abstractions to synthesize policies that satisfy temporal specifications related to safety and reachability. Our contribution is a novel abstraction-based controller method for continuous-state models with noise, uncertain parameters, and external disturbances.
  arXiv  Detail & Related papers  (2022-10-12T07:57:03Z)
• Meta-Learning Priors for Safe Bayesian Optimization [72.8349503901712]
  We build on a meta-learning algorithm, F-PACOH, capable of providing reliable uncertainty quantification in settings of data scarcity. As core contribution, we develop a novel framework for choosing safety-compliant priors in a data-riven manner. On benchmark functions and a high-precision motion system, we demonstrate that our meta-learned priors accelerate the convergence of safe BO approaches.
  arXiv  Detail & Related papers  (2022-10-03T08:38:38Z)
• Learning Control Barrier Functions from Expert Demonstrations [69.23675822701357]
  We propose a learning based approach to safe controller synthesis based on control barrier functions (CBFs) We analyze an optimization-based approach to learning a CBF that enjoys provable safety guarantees under suitable Lipschitz assumptions on the underlying dynamical system. To the best of our knowledge, these are the first results that learn provably safe control barrier functions from data.
  arXiv  Detail & Related papers  (2020-04-07T12:29:06Z)
• Online Parameter Estimation for Safety-Critical Systems with Gaussian Processes [6.122161391301866]
  We present a Bayesian optimization framework based on Gaussian processes (GPs) for online parameter estimation. It uses an efficient search strategy over a response surface in the parameter space for finding the global optima with minimal function evaluations. We demonstrate our technique on an actuated planar pendulum and safety-critical quadrotor in simulation with changing parameters.
  arXiv  Detail & Related papers  (2020-02-18T20:38:00Z)

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.