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Sampling-Based Model Predictive control Leveraging Parallelizable Physics Simulations

IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第3期10卷 2750-2757页

作者： Pezzato, Corrado Salmi, Chadi Trevisan, Elia Spahn, Max Alonso-Mora, Javier Corbato, Carlos Hernandez Delft Univ Technol Cognit Robot Dept NL-2628 CD Delft Netherlands

We present a sampling-based model predictive control method that uses a generic physics simulator as the dynamical model. In particular, we propose a Model Predictive Path Integral controller (MPPI) that employs the GPU-parallelizable IsaacGym simulator to compute the forward dynamics of the robot and environment. Since the simulator implicitly defines the dynamic model, our method is readily extendable to different objects and robots, allowing one to solve complex navigation and contact-rich tasks. We demonstrate the effectiveness of this method in several simulated and real-world settings, including mobile navigation with collision avoidance, non-prehensile manipulation, and whole-body control for high-dimensional configuration spaces. This is a powerful and accessible open-source tool to solve many contact-rich motion planning tasks.

关键词： Robots Collision avoidance Predictive models Computational modeling Physics Trajectory Planning Cost function Real-time systems optimal control optimization and optimal control Contact Modeling Whole-Body Motion Planning and control Model Predictive Path Integral control

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How-to Augmented Lagrangian on Factor Graphs

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第3期9卷 2806-2813页

作者： Bazzana, Barbara Andreasson, Henrik Grisetti, Giorgio Sapienza Univ Rome Dept Comp Control & Management Engn Antonio Rubert I-00185 Rome Italy Orebro Univ Ctr Appl Autonomous Sensor Syst AASS S-70182 Orebro Sweden

Factor graphs are a very powerful graphical representation, used to model many problems in robotics. They are widely spread in the areas of Simultaneous Localization and Mapping (SLAM), computer vision, and localization. However, the physics of many real-world problems is better modeled through constraints, e.g., estimation in the presence of inconsistent measurements, or optimal control. Constraints handling is hard because the solution cannot be found by following the gradient descent direction as done by traditional factor graph solvers. The core idea of our method is to encapsulate the Augmented Lagrangian (AL) method in factors that can be integrated straightforwardly in existing factor graph solvers. Besides being a tool to unify different robotics areas, the modularity of factor graphs allows to easily combine multiple objectives and effectively exploiting the problem structure for efficiency. We show the generality of our approach by addressing three applications, arising from different areas: pose estimation, rotation synchronization and Model Predictive control (MPC) of a pseudo-omnidirectional platform. We implemented our approach using C++ and ROS. Application results show that we can favorably compare against domain specific approaches.

关键词： optimization Robots Computational modeling Trajectory Simultaneous localization and mapping Synchronization optimal control Localization integrated planning and control optimization and optimal control

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Topology-Driven Parallel Trajectory optimization in Dynamic Environments

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IEEE TRANSACTIONS ON ROBOTICS 2025年 41卷 110-126页

作者： de Groot, Oscar Ferranti, Laura Gavrila, Dariu M. Alonso-Mora, Javier Delft Univ Technol Dept Cognit Robot NL-2628 CD Delft Netherlands

Ground robots navigating in complex, dynamic environments must compute collision-free trajectories to avoid obstacles safely and efficiently. Nonconvex optimization is a popular method to compute a trajectory in real time. However, these methods often converge to locally optimal solutions and frequently switch between different local minima, leading to inefficient and unsafe robot motion. In this work, we propose a novel topology-driven trajectory optimization strategy for dynamic environments that plans multiple distinct evasive trajectories to enhance the robot's behavior and efficiency. A global planner iteratively generates trajectories in distinct homotopy classes. These trajectories are then optimized by local planners working in parallel. While each planner shares the same navigation objectives, they are locally constrained to a specific homotopy class, meaning each local planner attempts a different evasive maneuver. The robot then executes the feasible trajectory with the lowest cost in a receding horizon manner. We demonstrate on a mobile robot navigating among pedestrians that our approach leads to faster trajectories than existing planners.

关键词： Trajectory Dynamics Planning Robots Collision avoidance Navigation Vehicle dynamics Cost function Trajectory optimization Topology Collision avoidance constrained motion planning motion and path planning optimization and optimal control

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Experimental Validation of Sensitivity-Aware Trajectory Planning for a Redundant Robotic Manipulator Under Payload Uncertainty

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第2期10卷 1561-1568页

作者： Srour, Ali Franchi, Antonio Giordano, Paolo Robuffo Cognetti, Marco Univ Rennes CNRS INRIA IRISA Campus Beaulieu F-35042 Rennes France Univ Twente Robot & Mechatron Dept Elect Engn Math & Comp Sci EEMCS Fac NL-7500 AE Enschede Netherlands Sapienza Univ Rome Dept Comp Control & Management Engn I-00185 Rome Italy Univ Toulouse LAAS CNRS UPS F-31031 Toulouse France

In this letter, we experimentally validate the recent concepts of closed-loop state and input sensitivity in the context of robust manipulation control for a robot manipulator. Our objective is to assess how optimizing trajectories with respect to sensitivity metrics can enhance the closed-loop system's performance w.r.t. model uncertainties, such as those arising from payload variations during precise manipulation tasks. We conduct a series of experiments to validate our optimization approach across different trajectories, focusing primarily on evaluating the precision of the manipulator's end-effector at critical moments where high accuracy is essential. Our findings offer valuable insights into improving the closed-loop robustness of the robot's state and inputs against physical parametric uncertainties that could otherwise degrade the system's performance.

关键词： Robot sensing systems Sensitivity Payloads Uncertainty Trajectory Manipulator dynamics End effectors Tensors Robustness Accuracy optimization and optimal control planning under uncertainty manipulation planning

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Active Learning of Discrete-Time Dynamics for Uncertainty-Aware Model Predictive control

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IEEE TRANSACTIONS ON ROBOTICS 2024年 40卷 1273-1291页

作者： Saviolo, Alessandro Frey, Jonathan Rathod, Abhishek Diehl, Moritz Loianno, Giuseppe NYU Tandon Sch Engn New York NY 11201 USA Univ Freiburg D-79110 Freiburg Germany

Model-based control requires an accurate model of the system dynamics for precisely and safely controlling the robot in complex and dynamic environments. Moreover, in presence of variations in the operating conditions, the model should be continuously refined to compensate for dynamics changes. In this article, we present a self-supervised learning approach that actively models the dynamics of nonlinear robotic systems. We combine offline learning from past experience and online learning from current robot interaction with the unknown environment. These two ingredients enable a highly sample-efficient and adaptive learning process, capable of accurately inferring model dynamics in real-time even in operating regimes that greatly differ from the training distribution. Moreover, we design an uncertainty-aware model predictive controller that is heuristically conditioned to the aleatoric (data) uncertainty of the learned dynamics. This controller actively chooses the optimal control actions that i) optimize the control performance, and ii) improve the efficiency of online learning sample collection. We demonstrate the effectiveness of our method through a series of challenging real-world experiments using a quadrotor system. Our approach showcases high resilience and generalization capabilities by consistently adapting to unseen flight conditions, while it significantly outperforms classical and adaptive control baselines.

关键词： Adaptation models Robots Data models Vehicle dynamics Uncertainty Training Aerodynamics Model learning for control aerial systems mechanics and control learning and adaptive systems optimization and optimal control

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Continuous-Time Line-of-Sight Constrained Trajectory Planning for 6-Degree of Freedom Systems

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第5期10卷 4332-4339页

作者： Hayner, Christopher R. Carson Iii, John M. Acikmese, Behcet Leung, Karen Univ Washington Dept Aeronaut & Astronaut Seattle WA 98195 USA NASA Johnson Space Ctr Houston TX 77058 USA

Perception algorithms are ubiquitous in modern autonomy stacks, providing necessary environmental information to operate in the real world. Many of these algorithms depend on the visibility of keypoints, which must remain within the robot's line-of-sight (LoS) for reliable operation. This letter tackles the challenge of maintaining LoS on such keypoints during robot movement. We propose a novel method that addresses these issues by ensuring applicability to various sensor footprints, adaptability to arbitrary nonlinear system dynamics, and constant enforcement of LoS throughout the robot's path. Our experiments show that the proposed approach achieves significantly reduced LoS violation and runtime compared to existing state-of-the-art methods in several representative and challenging scenarios.

关键词： Robot sensing systems Vectors Vehicle dynamics Line-of-sight propagation Trajectory planning Trajectory optimization Quadrotors Nonlinear dynamical systems Heuristic algorithms Convergence Constrained motion planning optimization and optimal control aerial systems: perception and autonomy

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Robust Pivoting Manipulation Using Contact Implicit Bilevel optimization

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IEEE TRANSACTIONS ON ROBOTICS 2024年 40卷 3425-3444页

作者： Shirai, Yuki Jha, Devesh K. Raghunathan, Arvind U. Mitsubishi Elect Res Labs MERL Cambridge MA 02139 USA

Generalizable manipulation requires that robots be able to interact with novel objects and environment. This requirement makes manipulation extremely challenging as a robot has to reason about complex frictional interactions with uncertainty in physical properties of the object and the environment. In this article, we study robust optimization for planning of pivoting manipulation in the presence of uncertainties. We present insights about how friction can be exploited to compensate for inaccuracies in the estimates of the physical properties during manipulation. Under certain assumptions, we derive analytical expressions for stability margin provided by friction during pivoting manipulation. This margin is then used in a contact implicit bilevel optimization framework to optimize a trajectory that maximizes this stability margin to provide robustness against uncertainty in several physical parameters of the object. We present analysis of the stability margin with respect to several parameters involved in the underlying bilevel optimization problem. We demonstrate our proposed method using a 6 DoF manipulator for manipulating several different objects. We also design and validate an MPC controller using the proposed algorithm which can track and regulate the position of the object during manipulation.

关键词： Contact modeling dexterous manipulation optimization and optimal control manipulation planning Contact modeling dexterous manipulation optimization and optimal control manipulation planning

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Learning a Tracking controller for Rolling μbot

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第2期9卷 1819-1826页

作者： Beaver, Logan E. Sokolich, Max Alsalehi, Suhail Weiss, Ron Das, Sambeeta Belta, Calin Boston Univ Div Syst Engn Boston MA 02446 USA Univ Delaware Dept Mech Engn Newark DE 29716 USA MIT Dept Biol Engn Cambridge MA 02142 USA

Micron-scale robots (mu bots) have recently shown great promise for emerging medical applications. Accurate control of mu bots, while critical to their successful deployment, is challenging. In this work, we consider the problem of tracking a reference trajectory using a $\mu$bot in the presence of disturbances and uncertainty. The disturbances primarily come from Brownian motion and other environmental phenomena, while the uncertainty originates from errors in the model parameters. We model the mu bot as an uncertain unicycle that is controlled by a global magnetic field. To compensate for disturbances and uncertainties, we develop a nonlinear mismatch controller. We define the model mismatch error as the difference between our model's predicted velocity and the actual velocity of the mu bot. We employ a Gaussian Process to learn the model mismatch error as a function of the applied control input. Then we use a least-squares minimization to select a control action that minimizes the difference between the actual velocity of the mu bot and a reference velocity. We demonstrate the online performance of our joint learning and control algorithm in simulation, where our approach accurately learns the model mismatch and improves tracking performance. We also validate our approach in an experiment and show that certain error metrics are reduced by up to 40%.

关键词： Chatbots Magnetic fields optimization Trajectory Three-dimensional displays Uncertainty Surface fitting Micro/nano robots machine learning for robot control optimization and optimal control

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Online Multicontact Receding Horizon Planning via Value Function Approximation

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IEEE TRANSACTIONS ON ROBOTICS 2024年 40卷 2791-2810页

作者： Wang, Jiayi Kim, Sanghyun Lembono, Teguh Santoso Du, Wenqian Shim, Jaehyun Samadi, Saeid Wang, Ke Ivan, Vladimir Calinon, Sylvain Vijayakumar, Sethu Tonneau, Steve Univ Edinburgh Sch Informat Edinburgh EH8 9YL Scotland Alan Turing Inst Artificial Intelligence Programme London NW1 2DB England Kyung Hee Univ Dept Mech Engn Suwon 17104 South Korea Idiap Res Inst CH-1920 Martigny Switzerland Ecole Polytech Fed Lausanne CH-1015 Lausanne Switzerland Dyson Ltd Malmesbury SN16 0RP England Touchlab Ltd Edinburgh EH9 3HJ Scotland

Planning multicontact motions in a receding horizon fashion requires a value function to guide the planning with respect to the future, e.g., building momentum to traverse large obstacles. Traditionally, the value function is approximated by computing trajectories in a prediction horizon (never executed) that foresees the future beyond the execution horizon. However, given the nonconvex dynamics of multicontact motions, this approach is computationally expensive. To enable online receding horizon planning (RHP) of multicontact motions, we find efficient approximations of the value function. Specifically, we propose a trajectory-based and a learning-based approach. In the former, namely RHP with multiple levels of model fidelity, we approximate the value function by computing the prediction horizon with a convex relaxed model. In the latter, namely locally guided RHP, we learn an oracle to predict local objectives for locomotion tasks, and we use these local objectives to construct local value functions for guiding a short-horizon RHP. We evaluate both approaches in simulation by planning centroidal trajectories of a humanoid robot walking on moderate slopes, and on large slopes where the robot cannot maintain static balance. Our results show that locally guided RHP achieves the best computation efficiency (95%-98.6% cycles converge online). This computation advantage enables us to demonstrate online RHP of our real-world humanoid robot Talos walking in dynamic environments that change on-the-fly.

关键词： Humanoid robots legged locomotion multicontact locomotion optimization and optimal control

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A semi-Lagrangian approach for time and energy path planning optimization in static flow fields

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JOURNAL OF THE FRANKLIN INSTITUTE 2025年第7期362卷

作者： Campos, Victor C. da S. Neto, Armando A. Macharet, Douglas G. Univ Fed Minas Gerais Dept Elect Engn Av Antonio Carlos 6627 BR-31270901 Belo Horizonte MG Brazil Univ Fed Minas Gerais Comp Vis & Robot Lab VeRLab Dept Comp Sci Av Antonio Carlos 6627 BR-31270901 Belo Horizonte MG Brazil

Efficient path planning for autonomous mobile robots is a critical problem across numerous domains, where optimizing both time and energy consumption is paramount. This paper introduces a novel methodology that considers the dynamic influence of an environmental flow field and geometric constraints, including obstacles and forbidden zones, enriching the complexity of the planning problem. Here, we formulate it as a multi-objective optimal control problem, and propose a novel transformation called Harmonic Transformation, applying a semiLagrangian scheme to solve it. The set of Pareto efficient solutions is obtained considering two distinct approaches: (i) a deterministic method referred to as Concurrent Policy Iteration (CPI);and (ii) an evolutionary-based one, called Multi-objective Evolutionary Policy Iteration (MEPI). Both methods were designed to make use of the proposed Harmonic Transformation. Through an extensive analysis of these approaches, comparing them with the state-of-the-art literature, we demonstrate their efficacy in finding optimized paths. Generally speaking, the Pareto Set of solutions found in our experiments indicates that the CPI demonstrated better performance in finding solutions close to the time-optimal one, whereas the MEPI was most successful in finding solutions close to the energy-optimal solution.

关键词： Motion and path planning Collision avoidance optimization and optimal control Multi-objective optimal control

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