Bayesian Optimization for Learning Nonlinear MPC in Autonomous Agent Navigation

• URL: http://arxiv.org/abs/2606.14763v1
• Date: Mon, 08 Jun 2026 16:08:09 GMT
• Title: Bayesian Optimization for Learning Nonlinear MPC in Autonomous Agent Navigation
• Abstract summary: The proposed framework is robot-agnostic and is evaluated on the Unitree Go2 quadruped in simulation using Gazebo.<n>The full system achieves up to a 90.0% navigation success rate when deployed, along with a 38.9% average improvement in the evaluation metrics across simulated environments.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Real-time autonomous navigation in dynamic, unknown environments remains a fundamental challenge for mobile robotics. We propose a map-free framework that tightly integrates reactive rolling-horizon planning with nonlinear Model Predictive Control (MPC). At each control cycle, a LiDAR-based Gaussian occupancy representation is constructed and used to generate collision-free trajectories via A* search, which are then tracked by a CasADi/IPOPT MPC formulation incorporating a smooth sigmoid obstacle barrier. To improve robustness to parameter sensitivity, we adopt an offline Bayesian optimization scheme based on Tree-structured Parzen Estimators (TPE), which identifies near-optimal controller parameters with respect to a composite navigation objective. In addition, a Gaussian Process surrogate is used to analyze parameter sensitivity and provide insight into the optimization landscape. The proposed framework is robot-agnostic and is evaluated on the Unitree Go2 quadruped in simulation using Gazebo, followed by deployment on the physical robot. Experimental results show that parameters tuned in simulation transfer effectively to hardware, maintaining comparable performance without additional tuning. The full system achieves up to a 90.0\% navigation success rate when deployed, along with a 38.9\% average improvement in the evaluation metrics across simulated environments.

Related papers

• Robust Adaptive Predictive Control for Hook-Based Aerial Transportation Between Moving Platforms [1.4592997284674396]
  This paper presents a novel model predictive control (MPC) approach for autonomous pick-and-place between moving platforms with a hook-equipped aerial manipulator.<n>The effectiveness of the proposed method is evaluated in complex simulated scenarios and in real-world flight experiments.
  arXiv  Detail & Related papers  (2026-05-04T09:14:34Z)
• Predictive Control with Indirect Adaptive Laws for Payload Transportation by Quadrupedal Robots [36.62645789321095]
  This paper develops a novel hierarchical planning and control framework for robust payload transportation by quadrupedal robots.<n>An indirect adaptive law estimates the unknown parameters of the reduced-order (template) locomotion model under varying payloads.<n>The robot successfully manages dynamic payloads with 73% of its mass on rough terrains.
  arXiv  Detail & Related papers  (2026-03-09T18:43:31Z)
• Spatially-Aware Adaptive Trajectory Optimization with Controller-Guided Feedback for Autonomous Racing [74.83272587893508]
  We present a framework for autonomous raceline optimization that combines NURBS-based trajectory representation, CMA-ES global trajectory optimization, and controller-guided spatial feedback.<n>In simulation, our approach achieves a 17.38% lap time reduction compared to a controller parametrized with maximum static acceleration.<n>On real hardware, tested with different tire compounds ranging from high to low friction, we obtain a 7.60% lap time improvement without explicitly parametrizing friction.
  arXiv  Detail & Related papers  (2026-02-17T15:10:44Z)
• Heteroscedastic Bayesian Optimization-Based Dynamic PID Tuning for Accurate and Robust UAV Trajectory Tracking [12.655695094805159]
  HBO-PID is a novel control algorithm that integrates the Heteroscedastic Bayesian Optimization framework with the classical controller.<n>It improves the position accuracy by 24.7% to 42.9%, and the angular accuracy by 40.9% to 78.4%.
  arXiv  Detail & Related papers  (2025-12-30T14:21:17Z)
• Bayesian Optimization for Automatic Tuning of Torque-Level Nonlinear Model Predictive Control [2.907225673486874]
  This paper presents an auto-tuning framework for torque-based Model Predictive Control (nMPC)<n>The MPC serves as a real-time controller for optimal joint torque commands.
  arXiv  Detail & Related papers  (2025-12-03T13:19:42Z)
• Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios [54.58186816693791]
  environments constantly change over time and space, posing significant challenges for object detectors trained based on a closed-set assumption.<n>We propose a new mechanism, converting the fine-tuning process to a specific- parameter generation.<n>In particular, we first design a dual-path LoRA-based domain-aware adapter that disentangles features into domain-invariant and domain-specific components.
  arXiv  Detail & Related papers  (2025-06-30T17:14:12Z)
• Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems [49.819436680336786]
  We propose an efficient transformed Gaussian process state-space model (ETGPSSM) for scalable and flexible modeling of high-dimensional, non-stationary dynamical systems.<n>Specifically, our ETGPSSM integrates a single shared GP with input-dependent normalizing flows, yielding an expressive implicit process prior that captures complex, non-stationary transition dynamics.<n>Our ETGPSSM outperforms existing GPSSMs and neural network-based SSMs in terms of computational efficiency and accuracy.
  arXiv  Detail & Related papers  (2025-03-24T03:19:45Z)
• Nonprehensile Riemannian Motion Predictive Control [57.295751294224765]
  We introduce a novel Real-to-Sim reward analysis technique to reliably imagine and predict the outcome of taking possible actions for a real robotic platform. We produce a closed-loop controller to reactively push objects in a continuous action space. We observe that RMPC is robust in cluttered as well as occluded environments and outperforms the baselines.
  arXiv  Detail & Related papers  (2021-11-15T18:50:04Z)
• Auto-Tuned Sim-to-Real Transfer [143.44593793640814]
  Policies trained in simulation often fail when transferred to the real world. Current approaches to tackle this problem, such as domain randomization, require prior knowledge and engineering. We propose a method for automatically tuning simulator system parameters to match the real world.
  arXiv  Detail & Related papers  (2021-04-15T17:59:55Z)
• Edge Federated Learning Via Unit-Modulus Over-The-Air Computation (Extended Version) [64.76619508293966]
  This paper proposes a unit-modulus over-the-air computation (UM-AirComp) framework to facilitate efficient edge federated learning. It uploads simultaneously local model parameters and updates global model parameters via analog beamforming. We demonstrate the implementation of UM-AirComp in a vehicle-to-everything autonomous driving simulation platform.
  arXiv  Detail & Related papers  (2021-01-28T15:10:22Z)
• 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.