Predictive Control with Indirect Adaptive Laws for Payload Transportation by Quadrupedal Robots

• URL: http://arxiv.org/abs/2603.08831v1
• Date: Mon, 09 Mar 2026 18:43:31 GMT
• Title: Predictive Control with Indirect Adaptive Laws for Payload Transportation by Quadrupedal Robots
• Abstract summary: 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.
• Score: 36.62645789321095
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper formally develops a novel hierarchical planning and control framework for robust payload transportation by quadrupedal robots, integrating a model predictive control (MPC) algorithm with a gradient-descent-based adaptive updating law. At the framework's high level, an indirect adaptive law estimates the unknown parameters of the reduced-order (template) locomotion model under varying payloads. These estimated parameters feed into an MPC algorithm for real-time trajectory planning, incorporating a convex stability criterion within the MPC constraints to ensure the stability of the template model's estimation error. The optimal reduced-order trajectories generated by the high-level adaptive MPC (AMPC) are then passed to a low-level nonlinear whole-body controller (WBC) for tracking. Extensive numerical investigations validate the framework's capabilities, showcasing the robot's proficiency in transporting unmodeled, unknown static payloads up to 109% in experiments on flat terrains and 91% on rough experimental terrains. The robot also successfully manages dynamic payloads with 73% of its mass on rough terrains. Performance comparisons with a normal MPC and an L1 MPC indicate a significant improvement. Furthermore, comprehensive hardware experiments conducted in indoor and outdoor environments confirm the method's efficacy on rough terrains despite uncertainties such as payload variations, push disturbances, and obstacles.

Related papers

• ASTRIL-MPC: Autonomous Traversal Framework of Articulated Tracked Robots with Language-Guided Neural-Kinematic MPC [10.434774696873793]
  In urban search and rescue, articulated tracked robots (ATRs) must traverse structured but contact-rich environments.<n>We present ASTRIL-MPC, a language-guided neural kinematics model predictive control framework.
  arXiv  Detail & Related papers  (2026-09-11T17:17:52Z)
• An Adaptive Control Architecture for Slope and Terrain Compensation in Autonomous Navigation in Mediterranean Greenhouses [38.96391848836269]
  This article presents a novel terrain-adaptation strategy based on the carried payload, ensuring accurate and robust trajectory tracking.<n>The proposed approach is based on: (i) the experimental characterization of the most common types of greenhouse soil, concrete, compacted sand, and gravel, and (ii) the direct measurement of terrain slope using the IMU.<n>Based on this information, a cascade trajectory-tracking scheme has been designed, consisting of a model-based predictive controller (MPC) in the outer loop and a PI controller in the inner loop.
  arXiv  Detail & Related papers  (2026-09-02T11:55:47Z)
• A Neural Network Based Teleoperation for Remote Controlled Vehicles [98.38733381483934]
  Direct teleoperation of vehicles faces critical technical bottlenecks.<n> communication latency and the operator's inability to physically perceive unmodeled environmental disturbances.<n>We propose a tailored unilateral teleoperation framework.
  arXiv  Detail & Related papers  (2026-08-11T01:51:08Z)
• SkyJEPA: Learning Long-Horizon World Models for Zero-Shot Sim-to-Real Control of Quadrotors [48.60897189520386]
  We introduce the JEPA-style model for real-time quadrotor control.<n>We combine a latent dynamics model with a novel physics-inspired prober that maps frozen latents to interpretable state.<n>To reduce the dependence on expensive and unsafe real-world data collection, we develop a structured pipeline for automated dataset generation.
  arXiv  Detail & Related papers  (2026-06-22T15:00:59Z)
• Bayesian Optimization for Learning Nonlinear MPC in Autonomous Agent Navigation [0.0]
  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.
  arXiv  Detail & Related papers  (2026-06-08T16:08:09Z)
• 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)
• Adaptive Linear Path Model-Based Diffusion [52.84663832658799]
  We introduce Linear Path Model-Based Diffusion (LP-MBD), which replaces the variance-preserving schedule with a flow-matching linear probability path.<n>We also propose Adaptive LP-MBD (ALP-MBD), which leverages reinforcement learning to adjust diffusion steps and noise levels according to task complexity and environmental conditions.
  arXiv  Detail & Related papers  (2026-02-02T21:33:03Z)
• Fine-Tuning of Neural Network Approximate MPC without Retraining via Bayesian Optimization [81.35990332700389]
  Approximate model-predictive control (AMPC) aims to imitate an MPC's behavior with a neural network.<n>During deployment, the parameters of the underlying MPC must usually be fine-tuned.<n>Recent work addresses this problem by adapting AMPC without retraining using approximated sensitivities of the MPC's optimization problem.
  arXiv  Detail & Related papers  (2025-12-16T12:24:08Z)
• Multi-Waypoint Path Planning and Motion Control for Non-holonomic Mobile Robots in Agricultural Applications [0.0]
  There is a growing demand for autonomous mobile robots capable of navigating unstructured agricultural environments.<n>Tasks such as weed control in meadows require efficient path planning through an unordered set of coordinates.<n>This paper presents an integrated navigation framework combining a global path planner based on the Dubins Traveling Salesman Problem.
  arXiv  Detail & Related papers  (2025-07-31T08:56:24Z)
• DATT: Deep Adaptive Trajectory Tracking for Quadrotor Control [62.24301794794304]
  Deep Adaptive Trajectory Tracking (DATT) is a learning-based approach that can precisely track arbitrary, potentially infeasible trajectories in the presence of large disturbances in the real world. DATT significantly outperforms competitive adaptive nonlinear and model predictive controllers for both feasible smooth and infeasible trajectories in unsteady wind fields. It can efficiently run online with an inference time less than 3.2 ms, less than 1/4 of the adaptive nonlinear model predictive control baseline.
  arXiv  Detail & Related papers  (2023-10-13T12:22:31Z)
• End-to-End Reinforcement Learning of Koopman Models for Economic Nonlinear Model Predictive Control [45.84205238554709]
  We present a method for reinforcement learning of Koopman surrogate models for optimal performance as part of (e)NMPC. We show that the end-to-end trained models outperform those trained using system identification in (e)NMPC.
  arXiv  Detail & Related papers  (2023-08-03T10:21:53Z)
• Tuning Legged Locomotion Controllers via Safe Bayesian Optimization [47.87675010450171]
  This paper presents a data-driven strategy to streamline the deployment of model-based controllers in legged robotic hardware platforms. We leverage a model-free safe learning algorithm to automate the tuning of control gains, addressing the mismatch between the simplified model used in the control formulation and the real system.
  arXiv  Detail & Related papers  (2023-06-12T13:10:14Z)
• Robust, High-Rate Trajectory Tracking on Insect-Scale Soft-Actuated Aerial Robots with Deep-Learned Tube MPC [0.0]
  We present an approach for agile and computationally efficient trajectory tracking on the MIT SoftFly, a sub-gram MAV (0.7 grams) Our strategy employs a cascaded control scheme, where an adaptive attitude controller is combined with a neural network policy trained to imitate a trajectory tracking robust tube model predictive controller (RTMPC) We experimentally evaluate our approach, achieving position Root Mean Square Errors lower than 1.8 cm even in the more challenging maneuvers, obtaining a 60% reduction in maximum position error compared to our previous work, and robustness demonstrating to large external disturbances.
  arXiv  Detail & Related papers  (2022-09-20T21:30:16Z)
• Neural Moving Horizon Estimation for Robust Flight Control [6.023276947115864]
  Estimating and reacting to external disturbances is crucial for robust flight control of quadrotors. We propose a neural moving horizon estimator (NeuroMHE) that can automatically tune the MHE parameters modeled by a neural network. NeuroMHE outperforms the state-of-the-art estimator with force estimation error reductions of up to 49.4%.
  arXiv  Detail & Related papers  (2022-06-21T13:43:24Z)
• Adaptive Model Predictive Control by Learning Classifiers [26.052368583196426]
  We propose an adaptive MPC variant that automatically estimates control and model parameters. We leverage recent results showing that BO can be formulated as a density ratio estimation. This is then integrated into a model predictive path integral control framework yielding robust controllers for a variety of challenging robotics tasks.
  arXiv  Detail & Related papers  (2022-03-13T23:22:12Z)

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.