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learning-Based model Predictive control for Autonomous Racing

IEEE ROBOTICS AND AUTOMATION LETTERS 2019年第4期4卷 3363-3370页

作者： Kabzan, Juraj Hewing, Lukas Liniger, Alexander Zeilinger, Melanie N. Swiss Fed Inst Technol Inst Dynam Syst & Control CH-8092 Zurich Switzerland Swiss Fed Inst Technol Automat Control Lab CH-8092 Zurich Switzerland

In this letter, we present a learning-based control approach for autonomous racing with an application to the AMZ Driverless race car gotthard. One major issue in autonomous racing is that accurate vehicle models that cover the entire performance envelope of a race car are highly nonlinear, complex, and complicated to identify, rendering them impractical for control. To address this issue, we employ a relatively simple nominal vehicle model, which is improved based on measurement data and tools from machine *** resulting formulation is an online learning data-driven model predictive controller, which uses Gaussian processes regression to take residual model uncertainty into account and achieve safe driving behavior. To improve the vehicle model online, we select from a constant in-flow of data points according to a criterion reflecting the information gain, and maintain a small dictionary of 300 data points. The framework is tested on the full-size AMZ Driverless race car, where it is able to improve the vehicle model and reduce lap times by 10% while maintaining safety of the vehicle.

关键词： model learning for control learning and adaptive systems model predictive control autonomous racing

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Impedance learning-Based Adaptive Force Tracking for Robot on Unknown Terrains

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IEEE TRANSACTIONS ON ROBOTICS 2025年 41卷 1404-1420页

作者： Li, Yanghong Zheng, Li Wang, Yahao Dong, Erbao Zhang, Shiwu Univ Sci & Technol China Humanoid Robot Inst Dept Precis Machinery & Precis Instrumentat State Key Lab Precis & Intelligent ChemCAS Key La Hefei 230026 Peoples R China

Aiming at the robust force tracking challenge for robots in continuous contact with uncertain environments, a novel adaptive variable impedance control policy based on deep reinforcement learning (DRL) is proposed in this article. The policy includes a neural network feedforward controller and a variable impedance feedback controller. Based on the DRL algorithm, the iterative network feedforward controller explores and prelearns the optimal policy for impedance tuning in simulation scenarios with randomly generated terrain. The converged results are then used as feedforward inputs in the variable impedance feedback controller to improve the force-tracking performance of the robot during contact. A simplified dynamic contact model between the robot and the uncertain environment called the "couch model," which satisfies the Lipschiz continuity condition, is developed to provide boundary conditions for the safe transfer of capabilities learned in simulation to real robots. Unlike the exhaustive example that relies on the completeness of the learning samples, this article gives theoretical proofs of the stability and convergence of the proposed control policy via Lyapunov's theorem and contraction mapping principle. The control method proposed in this article is more interpretable and shows higher sample utilization efficiency and generalization ability in simulations and experiments.

关键词： Impedance Robots Force Vectors Trajectory Humanoid robots Dynamics Convergence Process control Nonlinear dynamical systems Compliance and impedance control cooperating robots force tracking model learning for control

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learning to model and Plan for Wheeled Mobility on Vertically Challenging Terrain

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第2期10卷 1505-1512页

作者： Datar, Aniket Pan, Chenhui Xiao, Xuesu George Mason Univ Dept Comp Sci Fairfax VA 22030 USA

Most autonomous navigation systems assume wheeled robots are rigid bodies and their 2D planar workspaces can be divided into free spaces and obstacles. However, recent wheeled mobility research, showing that wheeled platforms have the potential of moving over vertically challenging terrain (e.g., rocky outcroppings, rugged boulders, and fallen tree trunks), invalidate both assumptions. Navigating off-road vehicle chassis with long suspension travel and low tire pressure in places where the boundary between obstacles and free spaces is blurry requires precise 3D modeling of the interaction between the chassis and the terrain, which is complicated by suspension and tire deformation, varying tire-terrain friction, vehicle weight distribution and momentum, etc. In this letter, we present a learning approach to model wheeled mobility, i.e., in terms of vehicle-terrain forward dynamics, and plan feasible, stable, and efficient motion to drive over vertically challenging terrain without rolling over or getting stuck. We present physical experiments on two wheeled robots and show that planning using our learned model can achieve up to 60% improvement in navigation success rate and 46% reduction in unstable chassis roll and pitch angles.

关键词： Robots Mobile robots Navigation Vehicle dynamics Trajectory Solid modeling Three-dimensional displays Space vehicles Planning Collision avoidance Autonomous vehicle navigation motion and path planning model learning for control

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Garment Diffusion models for Robot-Assisted Dressing

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第2期10卷 1217-1224页

作者： Kotsovolis, Stelios Demiris, Yiannis Imperial Coll London Dept Elect & Elect Engn Personal Robot Lab London SW7 2BT England

Robots have the potential to assist people with disabilities and the elderly. One of the most common and burdensome tasks for caregivers is dressing. Two challenges of robot-assisted dressing are modeling the dynamics of garments and handling visual occlusions that obstruct the perception of the full state of the garment due to the proximity between the garment, the robot, and the human. In this letter, we propose a diffusion-based dynamics model for garments during robot-assisted dressing that can deal with partial point cloud observations. The diffusion model, conditioned on the observation and the robot's action, is used to predict a full point cloud of the garment's opening of the future state. The model is utilized in a model predictive controller, that is trained iteratively with model-based reinforcement learning. In our experiments, we examine a common problem of dressing: the insertion of a garment's sleeve on an arm. As demonstrated by the performed experiments, the proposed diffusion-based model predictive controller can be effectively used for robot-assisted dressing and handle visual occlusions. Moreover, our approach is highly sample-efficient. Specifically, the controller achieved 91.2% success rate in the examined dressing task with less than 100 sampled trajectories. Real-wold experiments demonstrate that the proposed method can adapt to the sim-to-real gap and generalize well to novel garments and configurations of the body.

关键词： Clothing Robots Point cloud compression Predictive models Diffusion models Visualization Force Dynamics Vectors Three-dimensional displays Human-centered robotics model learning for control physical human-robot interaction robot-assisted dressing

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Cutting Sequence Diffuser: Sim-to-Real Transferable Planning for Object Shaping by Grinding

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第2期10卷 1162-1169页

作者： Hachimine, Takumi Morimoto, Jun Matsubara, Takamitsu Nara Inst Sci & Technol Grad Sch Sci & Technol Div Informat Sci Ikoma Nara 6300192 Japan Kyoto Univ Grad Sch Informat Dept Syst Sci Kyoto 6068501 Japan Adv Telecommun Res Inst Int ATR Brain Informat Commun Res Lab Grp BICR Kyoto 6068501 Japan

Automating object shaping by grinding with a robot is a crucial industrial process that involves removing material with a rotating grinding belt. This process generates removal resistance depending on such process conditions as material type, removal volume, and robot grinding posture, all of which complicate the analytical modeling of shape transitions. Additionally, a data-driven approach based on real-world data is challenging due to high data collection costs and the irreversible nature of the process. This letter proposes a Cutting Sequence Diffuser (CSD) for object shaping by grinding. The CSD, which only requires simple simulation data for model learning, offers an efficient way to plan long-horizon action sequences transferable to the real world. Our method designs a smooth action space with constrained small removal volumes to suppress the complexity of the shape transitions caused by removal resistance, thus reducing the reality gap in simulations. Moreover, by using a diffusion model to generate long-horizon action sequences, our approach reduces the planning time and allows for grinding the target shape while adhering to the constraints of a small removal volume per step. Through evaluations in both simulation and real robot experiments, we confirmed that our CSD was effective for grinding to different materials and various target shapes in a short time.

关键词： Shape Robots Diffusion models Planning Trajectory Surface resistance Belts Solid modeling Noise reduction Data collection model learning for control manipulation planning machine learning for eobot control

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Adaptive control Based Friction Estimation for Tracking control of Robot Manipulators

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第3期10卷 2454-2461页

作者： Huang, Junning Tateo, Davide Liu, Puze Peters, Jan Tech Univ Darmstadt Dept Comp Sci D-64289 Darmstadt Germany German Res Ctr DFKI Res Dept Syst AI Robot Learning D-67663 Kaiserslautern Germany

Adaptive control is often used for friction compensation in trajectory tracking tasks because it does not require torque sensors. However, it has some drawbacks: first, the most common certainty-equivalence adaptive control design is based on linearized parameterization of the friction model, therefore nonlinear effects, including the stiction and Stribeck effect, are usually omitted. Second, the adaptive control-based estimation can be biased due to non-zero steady-state error. Third, neglecting unknown model mismatch could result in non-robust estimation. This paper proposes a novel linear parameterized friction model capturing the nonlinear static friction phenomenon. Subsequently, an adaptive control-based friction estimator is proposed to reduce the bias during estimation based on backstepping. Finally, we propose an algorithm to generate excitation for robust estimation. Using a KUKA iiwa 14, we conducted trajectory tracking experiments to evaluate the estimated friction model, including random Fourier and drawing trajectories, showing the effectiveness of our methodology in different control schemes.

关键词： Friction Robots Estimation Adaptation models Trajectory Mathematical models Adaptive control Manipulator dynamics Computational modeling Numerical models model learning for control robust/adaptive control calibration and identification

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QT-TDM: Planning With Transformer Dynamics model and Autoregressive Q-learning

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第1期10卷 112-119页

作者： Kotb, Mostafa Weber, Cornelius Hafez, Muhammad Burhan Wermter, Stefan Univ Hamburg Dept Informat Knowledge Technol Grp D-22527 Hamburg Germany Aswan Univ Fac Sci Math Dept Aswan 81528 Egypt Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ England

Inspired by the success of the Transformer architecture in natural language processing and computer vision, we investigate the use of Transformers in Reinforcement learning (RL), specifically in modeling the environment's dynamics using Transformer Dynamics models (TDMs). We evaluate the capabilities of TDMs for continuous control in real-time planning scenarios with model Predictive control (MPC). While Transformers excel in long-horizon prediction, their tokenization mechanism and autoregressive nature lead to costly planning over long horizons, especially as the environment's dimensionality increases. To alleviate this issue, we use a TDM for short-term planning, and learn an autoregressive discrete Q-function using a separate Q-Transformer (QT) model to estimate a long-term return beyond the short-horizon planning. Our proposed method, QT-TDM, integrates the robust predictive capabilities of Transformers as dynamics models with the efficacy of a model-free Q-Transformer to mitigate the computational burden associated with real-time planning. Experiments in diverse state-based continuous control tasks show that QT-TDM is superior in performance and sample efficiency compared to existing Transformer-based RL models while achieving fast and computationally efficient inference.

关键词： model learning for control machine learning for robot control deep learning methods model learning for control machine learning for robot control deep learning methods

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Deep reinforcement learning-based variable impedance control for grinding workpieces with complex geometry

ROBOTIC INTELLIGENCE AND AUTOMATION

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ROBOTIC INTELLIGENCE AND AUTOMATION 2025年第1期45卷 159-172页

作者： Li, Yanghong Wang, Yahao Li, Zhen Lv, Yingxiang Chai, Jin Dong, Erbao Univ Sci & Technol China Humanoid Robot Inst Dept Precis Machinery & Precis Instrumentat State Key Lab Precis & Intelligent ChemCAS Key La Hefei Peoples R China

PurposeThis paper aims to design a deep reinforcement learning (DRL)-based variable impedance control policy that supports stability analysis for robot force tracking in complex geometric ***/methodology/approachThe DRL-based variable impedance controller explores and pre-learns the optimal policy for impedance parameter tuning in simulation scenarios with randomly generated workpieces. The trained results are then used as feedforward inputs to improve the force-tracking performance of the robot during contact. Based on Lyapunov's theory, the stability of the proposed control policy is analysed to illustrate the interpretability of the *** and experiments are performed on different types of complex environments. The results show that the proposed method is not only theoretically feasible but also has better force-tracking effects in ***/valueCompared with most other DRL-based control policies, the proposed method possesses stability and interpretability, effectively avoids the overfitting phenomenon and thus has better simulation-to-real deployment results.

关键词： Robotic machining model learning for control Compliance and impedance control Force tracking in workpieces

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Efficient Camera Exposure control for Visual Odometry via Deep Reinforcement learning

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第2期10卷 1609-1616页

作者： Zhang, Shuyang He, Jinhao Zhu, Yilong Wu, Jin Yuan, Jie Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China Hong Kong Univ Sci & Technol GZ Thrust Robot & Autonomous Syst Guangzhou 511453 Peoples R China

The stability of visual odometry (VO) systems is undermined by degraded image quality, especially in environments with significant illumination changes. This study employs a deep reinforcement learning (DRL) framework to train agents for exposure control, aiming to enhance imaging performance in challenging conditions. A lightweight image simulator is developed to facilitate the training process, enabling the diversification of image exposure and sequence trajectory. This setup enables completely offline training, eliminating the need for direct interaction with camera hardware and the real environments. Different levels of reward functions are crafted to enhance the VO systems, equipping the DRL agents with varying intelligence. Extensive experiments have shown that our exposure control agents achieve superior efficiency-with an average inference duration of 1.58 ms per frame on a CPU-and respond more quickly than traditional feedback control schemes. By choosing an appropriate reward function, agents acquire an intelligent understanding of motion trends and can anticipate future changes in illumination. This predictive capability allows VO systems to deliver more stable and precise odometry results.

关键词： Training Cameras Lighting Measurement Imaging Hardware Robot vision systems Optimization Visual odometry Deep reinforcement learning SLAM reinforcement learning model learning for control

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Kinematics-Informed Neural Networks: Enhancing Generalization Performance of Soft Robot model Identification

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第4期9卷 3068-3075页

作者： Yoon, Taerim Chai, Yoonbyung Jang, Yeonwoo Lee, Hajun Kim, Junghyo Kwon, Jaewoon Kim, Jiyun Choi, Sungjoon Korea Univ Dept Artificial Intelligence Seoul 02577 South Korea Ulsan Natl Inst Sci & Technol Dept Mat Sci & Engn Ulsan 44951 South Korea NAVER LABS Seongnam 13561 Gyeonggi South Korea

A hybrid system combining rigid and soft robots (e.g., soft fingers attached to a rigid arm) ensures safe and dexterous interaction with humans. Nevertheless, modeling complex movements involving both soft and rigid robots presents a challenge. Additionally, the difficulty of obtaining large datasets for soft robots, due to the risk of damage by repetitive and extreme actuations, hiders the utilization of data-driven approaches. In this study, we present a Kinematics-Informed Neural Network (KINN), which incorporates rigid body kinematics as an inductive bias to enhance sample efficiency and provide holistic control for the hybrid system. The model identification performance of the proposed method is extensively evaluated in simulated and real-world environments using pneumatic and tendon-driven soft robots. The evaluation result shows employing a kinematic prior leads to an 80.84% decrease in positional error measured in the L1-norm for extrapolation tasks in real-world tendon-driven soft robots. We also demonstrate the dexterous and holistic control of the rigid arm with soft fingers by opening bottles and painting letters.

关键词： modeling control and learning for soft robots model learning for control soft robot application

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