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

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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Robust Model Predictive Path Integral control: Analysis and Performance Guarantees

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IEEE ROBOTICS AND AUTOMATION LETTERS 2021年第2期6卷 1423-1430页

作者： Gandhi, Manan S. Vlahov, Bogdan Gibson, Jason Williams, Grady Theodorou, Evangelos A. Georgia Inst Technol Autonomous Control & Decis Syst Lab Atlanta GA 30313 USA Embark Trucks San Francisco CA 94107 USA

In this letter we propose a novel decision making architecture for Robust Model-Predictive Path Integral control (RMPPI) and investigate its performance guarantees and applicability to off-road navigation. Key building blocks of the proposed architecture are an augmented state space representation of the system consisting of nominal and actual dynamics, a placeholder for different types of tracking controllers, a safety logic for nominal state propagation, and an importance sampling scheme that takes into account the capabilities of the underlying tracking control. Using these ingredients, we derive a hound on the free energy growth of the dynamical system which is a function of task constraint satisfaction level, the performance of the underlying tracking controller, and the sampling error of the stochastic optimization used within RMPPI. To validate the bound on free energy growth, we perform experiments in simulation using two types of tracking controllers, namely the iterative Linear Quadratic Gaussian and Contraction-Metric based control. We further demonstrate the applicability of RMPPI in real hardware using the GT AutoRally vehicle. Our experiments demonstrate that RMPPI outperforms MPPI and Tube-MPPI by alleviating issues of the aforementioned model predictive controllers related to either lack of robustness or excessive conservatism. RMPPI provides the best of the two worlds in terms of agility and robustness to disturbances.

关键词： optimization and optimal control field robots model predictive control stochastic optimization

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How Bad is Selfish Driving? Bounding the Inefficiency of Equilibria in Urban Driving Games

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IEEE ROBOTICS AND AUTOMATION LETTERS 2023年第4期8卷 2293-2300页

作者： Zanardi, Alessandro Sessa, Pier Giuseppe Kaslin, Nando Bolognani, Saverio Censi, Andrea Frazzoli, Emilio ETH Inst Dynam Syst & Control CH-8092 Zurich Switzerland ETH Inst Machine Learning CH-8092 Zurich Switzerland ETH Automat Control Lab CH-8092 Zurich Switzerland

We consider the interaction among agents engaging in a driving task and we model it as general-sum game. This class of games exhibits a plurality of different equilibria posing the issue of equilibrium selection. While selecting the most efficient equilibrium (in term of social cost) is often impractical from a computational standpoint, in this work we study the (in)efficiency of any equilibrium players might agree to play. More specifically, we bound the equilibrium inefficiency by modeling driving games as particular type of congestion games over spatio-temporal resources. We obtain novel guarantees that refine existing bounds on the Price of Anarchy (PoA) as a function of problem-dependent game parameters. For instance, the relative trade-off between proximity costs and personal objectives such as comfort and progress. Although the obtained guarantees concern open-loop trajectories, we observe efficient equilibria even when agents employ closed-loop policies trained via decentralized multi-agent reinforcement learning.

关键词： Autonomous agents game theory motion and path planning optimization and optimal control

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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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Simultaneous Contact, Gait, and Motion Planning for Robust Multilegged Locomotion via Mixed-Integer Convex optimization

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IEEE ROBOTICS AND AUTOMATION LETTERS 2018年第3期3卷 2531-2538页

作者： Aceituno-Cabezas, Bernardo Mastalli, Carlos Dai, Hongkai Focchi, Michele Radulescu, Andreea Caldwell, Darwin G. Cappelletto, Jose Grieco, Juan C. Fernandez-Lopez, Gerardo Semini, Claudio Univ Simon Bolivar Mechatron Res Grp Caracas 1080 Venezuela Ist Italiano Tecnol Dept Adv Robot I-16163 Genoa Italy Toyota Res Inst Los Altos CA 94022 USA

Traditional motion planning approaches for multi-legged locomotion divide the problem into several stages, such as contact search and trajectory generation. However, reasoning about contacts and motions simultaneously is crucial for the generation of complex whole-body behaviors. Currently, coupling theses problems has required either the assumption of a fixed gait sequence and flat terrain condition, or nonconvex optimization with intractable computation time. In this letter, we propose a mixed-integer convex formulation to plan simultaneously contact locations, gait transitions, and motion, in a computationally efficient fashion. In contrast to previous works, our approach is not limited to flat terrain nor to a prespecified gait sequence. Instead, we incorporate the friction cone stability margin, approximate the robot's torque limits, and plan the gait using mixed-integer convex constraints. We experimentally validated our approach on the HyQ robot by traversing different challenging terrains, where nonconvexity and flat terrain assumptions might lead to suboptimal or unstable plans. Our method increases the motion robustness while keeping a low computation time.

关键词： Legged robots motion and path planning optimization and optimal control

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Modeling Perceptual Aliasing in SLAM via Discrete-Continuous Graphical Models

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IEEE ROBOTICS AND AUTOMATION LETTERS 2019年第2期4卷 1232-1239页

作者： Lajoie, Pierre-Yves Hu, Siyi Beltrame, Giovanni Carlone, Luca Ecole Polytech Montreal Dept Comp & Software Engn Montreal PQ H3S 1P9 Canada MIT LIDS 77 Massachusetts Ave Cambridge MA 02139 USA

Perceptual aliasing is one of the main causes of the failure for simultaneous localization and mapping (SLAM) systems operating in the wild. Perceptual aliasing is a phenomenon where different places generate a similar visual (or, in general, perceptual) footprint. This causes spurious measurements to be fed to the SLAM estimator, which typically results in incorrect localization and mapping results. This problem is exacerbated by the fact that those outliers are highly correlated, in the sense that perceptual aliasing creates a large number of mutually consistent outliers. Another issue stems from the fact that most state-of-the-art techniques rely on a given trajectory guess (e.g., from odometry) to discern between inliers and outliers, and this makes the resulting pipeline brittle, since the accumulation of error may result in incorrect choices and recovery from failures is far from trivial. This paper provides a unified framework to model perceptual aliasing in SLAM and provides practical algorithms that can cope with outliers without relying on any initial guess. We present two main contributions. The first is a discrete-continuous graphical model (DC-GM) for SLAM: The continuous portion of the DC-GM captures the standard SLAM problem, while the discrete portion describes the selection of the outliers and models their correlation. The second contribution is a semidefinite relaxation to perform inference in the DC-GM that returns estimates with provable sub-optimality guarantees. Experimental results on standard benchmarking datasets show that the proposed technique compares favorably with state-of-the-art methods while not relying on an initial guess for optimization.

关键词： SLAM sensor fusion localization mapping optimization and optimal control

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optimal Perimeter Guarding With Heterogeneous Robot Teams: Complexity Analysis and Effective Algorithms

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IEEE ROBOTICS AND AUTOMATION LETTERS 2020年第2期5卷 430-437页

作者： Feng, Si Wei Yu, Jingjin Rutgers State Univ Dept Comp Sci Piscataway NJ 08901 USA

We perform structural and algorithmic studies of significantly generalized versions of the optimal perimeter guarding (OPG) problem. As compared with the original OPG where robots are uniform, in this paper, many mobile robots with heterogeneous sensing capabilities are to be deployed to optimally guard a set of one-dimensional segments. Two complimentary formulations are investigated where one limits the number of available robots (OPG(LR)) and the other seeks to minimize the total deployment cost (OPG(MC)). In contrast to the original OPG which admits low-polynomial time solutions, both OPG(LR) and OPG(MC) are computationally intractable with OPG(LR) being strongly NP-hard. Nevertheless, we develop fairly scalable pseudo-polynomial time algorithms for practical, fixed-parameter subcase of OPG(LR);we also develop pseudo-polynomial time algorithm for general OPG(MC) and polynomial time algorithm for the fixed-parameter OPG(MC) case. The applicability and effectiveness of selected algorithms are demonstrated through extensive numerical experiments.

关键词： Multi-robot systems optimization and optimal control surveillance systems

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Bayesian optimization for Whole-Body control of High-Degree-of-Freedom Robots Through Reduction of Dimensionality

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IEEE ROBOTICS AND AUTOMATION LETTERS 2019年第3期4卷 2268-2275页

作者： Yuan, Kai Chatzinikolaidis, Iordanis Li, Zhibin Univ Edinburgh Sch Informat Edinburgh EH8 9LE Midlothian Scotland

This letter aims to achieve automatic tuning of optimal parameters for whole-body control algorithms to achieve the best performance of high-DoF robots. Typically, the control parameters at a scale up to hundreds are often hand-tuned yielding sub-optimal performance. Bayesian optimization (BO) can be an option to automatically find optimal parameters. However, for high-dimensional problems, BO is often infeasible in realistic settings as we studied in this letter. Moreover, the data is too little to perform dimensionality reduction techniques, such as principal component analysis or partial least square. We hereby propose an alternating BO algorithm that iteratively learns the parameters of sub-spaces from the whole high-dimensional parametric space through interactive trials, resulting in sample efficiency and fast convergence. Furthermore, for the balancing and locomotion control of humanoids, we developed techniques of dimensionality reduction combined with the proposed ABO approach that demonstrated optimal parameters for robust whole-body control.

关键词： optimization and optimal control legged robots humanoid and bipedal locomotion humanoid robots

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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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