Robot Trajectory Tracking

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15 papers in the last four weeks, up 400% on the four weeks before. 0.1% of all new papers.

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Latest papers 92

May 19, 2026cs.RO

Trajectory Planning and Control near the Limits: an Open Experimental Benchmark on the RoboRacer Platform

We present a modular framework to benchmark new and existing methods for trajectory planning and control in high-acceleration maneuvers that push autonomous driving to the limits. Our framework includes time-optimal raceline generation, online time-optimal velocity replanning, geometric path tracking controllers, and a new model-structured neural network (MS-NN) to learn the inverse dynamics for steering control. We deploy our framework on a 1:10-scale RoboRacer platform, using two circuits. Through several ablations with cautious and aggressive racelines, we study the performance of single modules and their combinations. We show that our MS-NN significantly improves tracking accuracy, decreases steering oscillations, and is physically interpretable. Moreover, online velocity replanning improves lap times by compensating for execution errors, and enables the vehicle to safely reach higher speeds and accelerations. To support future research, our code, datasets, videos and results are publicly available at https://roboracer-benchmark.github.io/planning_control_benchmark/.

Mattia Piccinini, Patrick Zambiasi, Aniello Mungiello +3

Professorship of Autonomous Vehicle Systems, Technical University of Munich, 85748 Garching, Germany; Munich Institute of Robotics and Machine Intelligence (MIRMI). · Avilus GmbH, Germany. · Department of Information Technology and Electrical Engineering (DIETI), University of Naples Federico II, Naples 80125, Italy. +1

Autonomous Driving Benchmarks Robot Trajectory Tracking Trajectory Optimization Trajectory Generation Autonomous Racing Vehicle Dynamics+1

May 19, 2026cs.RO

Aerial Inspection Behaviors via RL-based Quadrotor Control for Under-canopy Forest Environments

This paper addresses the problem of using a deep Reinforcement Learning (RL)-based low-level Quadrotor controller within an autonomous Quadrotor navigation stack for aerial inspection missions in under-canopy forest environments. Specifically, the article presents an end-to-end (mapping states to RPMs) Quadrotor control policy that achieves inspection view-pose tracking (simultaneous position and yaw reference tracking), which is crucial for various target inspection behaviors and point-to-point navigation in forests. To ensure safe and reliable deployment of the end-to-end RL controller in long-range missions, this article utilizes a higher navigation guidance layer comprising of a Traveling Salesman Problem planner (TSP) and a Rapidly-exploring Random Tree Star (RRT*) planner. Over a known map of a forest and a set of user-specified inspection regions, the TSP planner finds the optimal visitation sequence. Between two target regions, collision-free paths that respect the tracking limitations of the lower end-to-end RL policy are generated by an RRT* planner. Through five target inspection scenarios, this article demonstrates that an RL-based motor-level stabilizing controller, supported by a navigation guidance layer, can be used effectively as the low-level inspection execution module for under-canopy forest inspection missions.

Fausto Mauricio Lagos Suarez, Akshit Saradagi, Vidya Sumathy +2

Robotics and AI group, in the Department of Computer Science, Electrical and Space Engineering at Luleåa University of Technology, Sweden

RL Control Robot Trajectory Tracking UAV Navigation Quadrotor Control

May 18, 2026cs.RO

A Heuristic Approach for Performance Tuning in RL-based Quadrotor Control via Reward Design and Termination Conditions

Reinforcement learning (RL)-based quadrotor control policies have achieved impressive performance in tasks such as fast navigation in cluttered environments and drone racing, where the focus is on speed and agility. However, in several applications, such as infrastructure inspection, it is critical to achieve precise, controlled maneuvers with tunable performance. In this article, we present a novel heuristic approach to achieve tunable performance in RL-based Quadrotor control through reward design and termination conditions. We present a novel reward structure containing dual bandwidth exponentials that achieves a baseline critically damped response in setpoint tracking, with low steady-state errors. When trained with a Proximal Policy Optimization (PPO) algorithm, in conjunction with episode truncation conditions, the desired performance is achieved in 6 million time steps in a sample-efficient manner. In order to tune the performance about the baseline behavior, we present intuitive heuristic rules to adjust the reward weights and exponential coefficients to achieve faster (acrobatic-like) and slower (inspection-like) settling time performance, while retaining the baseline critically damped response and approximately 2% steady-state error. We evaluate the three RL policies (baseline, acrobatic, and inspection) across 100 trials and show accurate and tunable performance in position and yaw tracking from random initial conditions, thereby demonstrating the effectiveness of the proposed heuristic approach.

Fausto Mauricio Lagos Suarez, Akshit Saradagi, Vidya Sumathy +1

Robotics and AI group, in the Department of Computer Science, Electrical and Space Engineering at Luleåa University of Technology, Sweden

RL Control Robot Trajectory Tracking Reinforcement Learning Reward Shaping Quadrotor Control

May 17, 2026cs.RO

Rapid Vibration Suppression and Trajectory Tracking of a Serial Manipulator with Multi-Flexible Links

Flexible robotic manipulators (FRMs) offer advantages in lightweight design and large workspace, but their structural flexibility induces vibrations, accelerates fatigue, degrades tracking performance, and limits operational speed. These challenges are further amplified in multi-link serial manipulators, where increased overall length leads to greater structural flexibility. This article presents a backstepping output-feedback framework for fast vibration suppression and tip tracking of an n-degree-of-freedom serial flexible manipulator robot (nDSFMR), with a DeepONet-based approximation for practical deployment. Each link-joint is modeled as a Timoshenko beam coupled with an ODE and transformed into a canonical hyperbolic PDE with boundary dynamics. A backstepping-based boundary controller at the joint is developed to equivalently inject distributed damping along the beam, enabling rapid vibration suppression and trajectory tracking, only using available boundary measurements. To enable real-time implementation and scalability, a DeepONet neural operator is introduced to approximate the backstepping kernels, significantly reducing computational cost and facilitating fast controller updates under varying operating conditions. Experiments on a two-link flexible manipulator demonstrate faster vibration suppression and convergence of the end-effector to the desired trajectory, compared with a linear quadratic regulator (LQR) with feedforward control.

Chengyi Wang, Yilong Huang, Ji Wang

School of Aerospace Engineering, Xiamen University, Xiamen 361102, P. R. China

Robotic Control Robot Trajectory Tracking Feedback Control

May 14, 2026cs.RO

Reactive Planning based Control for Mobile Robots in Obstacle-Cluttered Environments

This paper addresses the motion control problem for mobile robots in obstacle-cluttered environments. The mobile robot has partial environment information only, and aims to move from an initial position to a target position without collisions. For this purpose, a reactive planning based control strategy (RPCS) is proposed. First, the initial and target positions are connected as a reference trajectory. Then, a reactive planning strategy (RPS) is developed to ensure the collision avoidance by modifying the reference trajectory locally based on the partial environment information. Next, an adaptive tracking control strategy (ATCS) is proposed to track the reference trajectory with potentially local modifications via the discretization techniques. Finally, the RPS and ATCS are combined to establish the RPCS, whose efficacy and advantages are illustrated by numerical examples.

Li Tan, Junlin Xiong, Yan Wang +1

Department of Automation, University of Science and Technology of China, Hefei, 230031, China. · School of Intelligence Science and Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 515100, China · School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China.

Robot Trajectory Tracking Mobile Robotics Roadmap-Based Motion Planning Motion Planning Robot Motion Planning Robot Collision Avoidance

May 9, 2026cs.RO

A low-cost mockup to simulate robotic laser cutting in nuclear decommissioning

This paper introduces a low-cost experimental mockup to simulate the laser cutting process of containers in nuclear decommissioning. It is composed of a three-axis table supporting a cuboid container with ultraviolet-sensitive faces, a six-degree-of-freedom serial manipulator holding an ultraviolet torch that simulates the laser, and a visual system based on cameras and fiducial markers. The system employs a constrained task-space adaptive motion controller that compensates for inaccurate parameters and eliminates the need to calibrate the system. Furthermore, as the motion controller explicitly accounts for geometric constraints, the robot reactively avoids collisions with obstacles while handling the ultraviolet torch. To improve the tracking of the laser-cutting path, we control the ultraviolet beam, which requires only four degrees of freedom, instead of the full end-effector pose. Experiments show that, despite an initially uncalibrated system, the overall system is capable of tracking different trajectories with an overall mean accuracy of 3.9 (sd 2.5) mm when the end-effector pose is controlled and 2.4 (sd 1.3) mm when the ultraviolet beam is controlled.

Frederico Fernandes Afonso Silva, Murilo Marques Marinho, Bruno Vilhena Adorno

Department of Electrical and Electronic Engineering and the Manchester Centre for Robotics and AI, The University of Manchester, United Kingdom

Robotic Control Robot Trajectory Tracking Robotics Simulation Robotic Tool Use

May 8, 2026cs.RO

Model-Reference Adaptive Flight Control of a 95-mg Insect-Scale Flapping-Wing Aerial Robot

Due to the system's scale and complex fabrication, the model describing the dynamics of a flapping-wing insect-scale aerial robot is subject to parameter uncertainty; for example, in the inertia matrix and the actuator mapping of the flier. Furthermore, due to its low inertia, this type of robot is greatly affected by stochastic and systematic disturbances during flight, including power-wire tension, gusts, and undesired aerodynamic forces produced by wing misalignment. Therefore, the high-performance execution of complex maneuvers at the subdecigram scale requires the robot to adapt its behavior to counteract disturbances and model uncertainty. Toward this objective, we introduce a model-reference adaptive control (MRAC) architecture for high-performance position control of flapping-wing robotic insects that can be modeled as rigid bodies in the three-dimensional (3D) space. In addition, we demonstrate how the implementation of a hybrid multiplicative extended Kálmán filter for estimating current and desired angular velocities during flight significantly dampens attitude vibrations, especially along the roll and pitch degrees of freedom (DOFs), and also improves flight performance. To show the suitability, functionality, and high performance of the proposed approach, we conducted real-time hovering and trajectory-tracking 6-DOF flight control experiments with a 95-mg insect-scale aerial robot.

Francisco M. F. R. Gonçalves, Conor K. Trygstad, Néstor O. Pérez-Arancibia

School of Mechanical and Materials Engineering, Washington State University (WSU), Pullman, WA 99164-2920, USA

Robot Trajectory Tracking Aerial Robotics Adaptive Control

May 7, 2026cs.RO

Intention assimilation control for accurate tracking with variable impedance in teleoperation

Robot systems for teleoperation commonly use a spring-like force pulling the follower robot towards the leader's position to track their movements. With this control strategy, the tracking accuracy deteriorates when the follower' stiffness is low, but high stiffness poses a danger to objects or people in the follower robot's environment. To address this trade-off between tracking accuracy and safety, we propose an alternative intention assimilation control (IAC) strategy where the robot's tracking accuracy can be ensured without high stiffness. Different from traditional approaches, which transmit the leader's current position to the follower, this new controller estimates the leader's target position and transmits it to the follower. With this strategy, the follower impedance can be changed on-the-fly to continuously reflect the user's desired impedance or modulated automatically to fulfill the task requirements. Our controller was validated on two 7 degree-of-freedom manipulators, yielding high tracking accuracy with varying impedance. Four experiments were conducted to compare {teleoperation} with IAC to tele-impedance control (TIC) during free tracking, interaction with a balloon, during peg insertion, and table polishing with force feedback. The results show that IAC increases tracking accuracy, improves task completion rate and reduces completion time. IAC enables the robot to accurately replicate the user's movement while giving them freedom to modulate the impedance according to their intention, providing an unprecedented level of control of the follower's position and its impedance during unilateral and bilateral teleoperation.

Atsushi Takagi, Yanan Li, Hiroaki Gomi +1

Robot Trajectory Tracking Robot Teleoperation Impedance Control

May 7, 2026cs.RO

AirBender: Adaptive Transportation of Bendable Objects Using Dual UAVs

The interaction of robots with bendable objects in midair presents significant challenges in control, often resulting in performance degradation and potential crashes, especially for aerial robots due to their limited actuation capabilities and constant need to remain airborne. This paper presents an adaptive controller that enables two aerial vehicles to collaboratively follow a trajectory while transporting a bendable object without relying on explicit elasticity models. Our method allows on-the-fly adaptation to the object's unknown deformable properties, ensuring stability and performance in trajectory-tracking tasks. We use Lyapunov analysis to demonstrate that our adaptive controller is asymptotically stable. Our method is evaluated through hardware experiments in various scenarios, demonstrating the capabilities of using multirotor aerial vehicles to handle bendable objects.

Jiawei Xu, Longsen Gao, Rafael Fierro +1

Autonomous and Intelligent Robotics Laboratory (AIRLab), Lehigh University, PA, 18015, USA · Electrical and Computer Engineering Department, The University of New Mexico, Albuquerque, NM, 87131, USA

Robot Trajectory Tracking Aerial Robotics Deformable Object Manipulation Multi-Robot Coordination Adaptive Control Aerial Manipulation

May 7, 2026cs.LG

Temporal Attention for Adaptive Control of Euler-Lagrange Systems with Unobservable Memory

Adaptive control of Euler-Lagrange systems is challenging when friction is governed by a finite-horizon internal state that is not directly observable from joint measurements. In this setting, the measured closed-loop state is no longer Markovian, and standard certainty-equivalence adaptive laws may lose their convergence guarantees. The paper proposes a meta-control architecture in which the gains of a computed-torque controller are generated by a self-attention block processing a short window of recent motion history. The number of attention heads is selected before policy training through a surrogate analysis of the autocovariance of the memory-state gradient along the temporal window. This surrogate is based on a temporal adaptation of an incremental rank-tracking framework previously developed by the authors. The selected head count is then fixed and used as an architectural hyperparameter in a reinforcement-learning stage, where the policy is trained under a shielded admissibility constraint. The approach is tested on a 2-DOF manipulator with nonlinear friction and variable payload. In the short and matched memory regimes, the single-layer attention-only meta-controller outperforms a deeper Transformer baseline, with tracking-error reductions of 12 and 19 percentage points, respectively. The reported effect sizes are large, with d approximately -1.1 and -2.1, and Mann-Whitney p < 0.05 in both cases. In the long memory regime, however, the advantage disappears. Four out of ten training runs show either divergence or payload-invariant policy collapse, revealing a weakness in the static Phase-1 head-count prescription. This motivates moving rank-tracking inside the reinforcement-learning loop, allowing attention heads to be pruned or grown at runtime instead of fixed before training.

Giansalvo Cirrincione, Adriano Fagiolini

Laboratoire LTI, Université de Picardie Jules Verne, Amiens, France · MIRPALab, Department of Engineering, University of Palermo, 90128 Palermo, Italy

RL Control Robot Trajectory Tracking Adaptive Control

May 7, 2026cs.RO

Accurate Trajectory Tracking with MPCC for Flapping-Wing MAVs

Flapping-wing micro aerial vehicles offer quieter and safer operation than rotary-wing drones, yet achieving precise autonomous control of bird-scale ornithopters remains challenging: lift, airspeed, and turning authority are tightly coupled and governed by only a few control inputs. Conventional cascaded controllers treat altitude, speed, and heading independently, producing persistent tracking errors during complex maneuvers, while time-parameterized trajectory tracking requires predefined speed profiles that existing methods cannot robustly produce for these coupled dynamics. We address both limitations simultaneously with a Model Predictive Contouring Control (MPCC) approach that tracks arc-length-parameterized trajectories while optimizing progress online, eliminating the need for predefined timing. However, MPCC requires a dynamical model that captures the coupled aerodynamics without exceeding the computational budget of real-time nonlinear optimization. Here, we propose a compact, continuously differentiable model that captures the dominant couplings of bird-scale ornithopters, enabling real-time predictive control. We validated the method with the XFly ornithopter flying along circular and three-dimensional racing trajectories and achieved a mean deviation from the reference trajectory between 6.5 and 9 cm at speeds up to 3 m/s, which represents an almost 10-fold improvement over prior ornithopter control methods.

Charbel Toumieh, Jack Zeng, Niel Mistry +1

Laboratory of Intelligent Systems, Ecole Polytechnique Federale de Lausanne (EPFL), CH1015 Lausanne, Switzerland

Robot Trajectory Tracking Aerial Robotics Nonlinear MPC Model Predictive Control

May 2, 2026eess.SY

Quasi-Static Control of Discrete Cosserat Rod

In this paper, we design feedback control laws for soft robots modelled using the Cosserat rod, which is spatially discretised using the Piecewise Constant Strain (PCS) approach. The PCS approach transforms the nonlinear PDEs describing the Cosserat rod to a system of nonlinear ODEs. This simplification results in a model describing soft robots which is similar to the serial rigid-link manipulators. We design feedback control laws for the quasi-static PCS model by using external wrenches as control inputs. The control laws are designed based on state-feedback linearisation in strain and task spaces. An extensive set of numerical results demonstrates the performance of the control laws for end-effector trajectory tracking and shape control of soft robots.

Srishti Siddharth, Domenico Campolo, Ravi Banavar

Centre for Systems and Control Indian Institute of Technology Bombay Mumbai, India · School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore

Robot Trajectory Tracking Feedback Control Continuum Robot Control Soft Robotics

Apr 28, 2026cs.RO

Reference-Augmented Learning for Precise Tracking Policy of Tendon-Driven Continuum Robots

Tendon-Driven Continuum Robots (TDCRs) pose significant control challenges due to their highly nonlinear, path-dependent dynamics and non-Markovian characteristics. Traditional Jacobian-based controllers often struggle with hysteresis-induced oscillations, while conventional learning-based approaches suffer from poor generalization to out-of-distribution trajectories. This paper proposes a reference-augmented offline learning framework for precise 6-DOF tracking control of TDCRs. By leveraging a differentiable RNN-based dynamics surrogate as a gradient bridge, we optimize a control policy through an augmented reference distribution. This multi-scale augmentation scheme incorporates stochastic bias, harmonic perturbations, and random walks, forcing the policy to internalize diverse tracking error recovery mechanisms without additional hardware interaction. Experimental results on a three-section TDCR platform demonstrate that the proposed policy achieves a 50.9% reduction in average position error compared to non-augmented baselines and significantly outperforms Jacobian-based methods in both precision and stability across various speeds. For implementation details and source code, please refer to https://github.com/ZiqingZou/ContinuumControl.

Ziqing Zou, Ke Qiu, Haojian Lu +2

Department of Control Science and Engineering, Zhejiang University, Hangzhou, China

Robot Trajectory Tracking Robot Policy Learning Data Augmentation Continuum Robot Control Tendon-Driven Actuation

Apr 28, 2026cs.RO

Learning-Based Dynamics Modeling and Robust Control for Tendon-Driven Continuum Robots

Tendon-Driven Continuum Robots (TDCRs) pose significant modeling and control challenges due to complex nonlinearities, such as frictional hysteresis and transmission compliance. This paper proposes a differentiable learning framework that integrates high-fidelity dynamics modeling with robust neural control. We develop a GRU-based dynamics model featuring bidirectional multi-channel connectivity and residual prediction to effectively suppress compounding errors during long-horizon auto-regressive prediction. By treating this model as a gradient bridge, an end-to-end neural control policy is optimized through backpropagation, allowing it to implicitly internalize compensation for intricate nonlinearities. Experimental validation on a physical three-section TDCR demonstrates that our framework achieves accurate tracking and superior robustness against unseen payloads, outperforming Jacobian-based methods by eliminating self-excited oscillations. For implementation details and source code, please refer to https://github.com/ZiqingZou/ContinuumControl.

Ziqing Zou, Ke Qiu, Fei Wang +3

Department of Control Science and Engineering, Zhejiang University, Hangzhou, China

Robot Trajectory Tracking Robot Dynamics Learning Recurrent Neural Networks Robust Control Continuum Robot Control Tendon-Driven Actuation+1

Apr 27, 2026eess.SY

Sliding Mode Control for Safe Trajectory Tracking with Moving Obstacles Avoidance: Experimental Validation on Planar Robots

This paper presents a unified control framework for robust trajectory tracking and moving obstacle avoidance applicable to a broad class of mobile robots. By formulating a generalized kinematic transformation, we convert diverse vehicle dynamics into a strict feedback form, facilitating the design of a Sliding Mode Control (SMC) strategy for precise and robust reference tracking. To ensure operational safety in dynamic environments, the tracking controller is integrated with a Collision Cone Control Barrier Function (C3BF) based safety filter. The proposed architecture guarantees asymptotic tracking in the presence of external disturbances while strictly enforcing collision avoidance constraints. The novelty of this work lies in designing a sliding mode controller for ground robots like the Ackermann drive, which has not been done before. The efficacy and versatility of the approach are validated through numerical simulations and extensive real-world experiments on three distinct platforms: an Ackermann-steered vehicle, a differential drive robot, and a quadrotor drone. Video of the experiments are available at https://youtu.be/dWcxwum96vk

Shubham Sawarkar, P Sangeerth, S Saharsh +1

Centre for Cyber-Physical Systems and Department of Aerospace Engineering, Indian Institute of Science, Bengaluru, India. · Department of Aerospace Engineering, Indian Institute of Science, Bengaluru, India.

Robot Trajectory Tracking Mobile Robotics Robust Control Dynamic Obstacle Avoidance Sliding Mode Control

Apr 23, 2026cs.RO

Dynamic Coupling and Indirect Control of Jointed Robots Rolling Atop A Moving Platform

An asymmetric two-link robot supported atop a flat platform by wheels that roll and pivot freely, but do not slip laterally, will develop forward momentum if the joint between the links is actuated internally. In particular, oscillations in the joint angle will generate undulatory locomotion suggesting fishlike swimming. If two such robots surmount a common platform that's free to translate with its own inertial dynamics, then the individual robots' dynamics will be coupled so that the locomotion of either robot is affected by that of the other. We develop a mathematical model for this system and present simulations demonstrating its behavior. We then consider a single robot with an unactuated joint rolling atop a platform that moves under control, and show that actuation of the platform is sufficient to dictate the robot's behavior. In particular, with the acceleration of the platform as an input, the robot's heading can be made to track a chosen function of time. This is sufficient to guarantee that the robot can be induced to orbit a fixed point on the platform or to locomote persistently in a desired direction.

Hamidreza Moradi, Scott David Kelly

Department of Mechanical Engineering and Engineering Science, University of North Carolina at Charlotte, Charlotte, NC 28223 USA

Robotic Control Robot Trajectory Tracking Multi-Robot Systems Multibody Dynamics Robot Locomotion

Apr 19, 2026cs.RO

Learning Whole-Body Humanoid Locomotion via Motion Generation and Motion Tracking

Whole-body humanoid locomotion is challenging due to high-dimensional control, morphological instability, and the need for real-time adaptation to various terrains using onboard perception. Directly applying reinforcement learning (RL) with reward shaping to humanoid locomotion often leads to lower-body-dominated behaviors, whereas imitation-based RL can learn more coordinated whole-body skills but is typically limited to replaying reference motions without a mechanism to adapt them online from perception for terrain-aware locomotion. To address this gap, we propose a whole-body humanoid locomotion framework that combines skills learned from reference motions with terrain-aware adaptation. We first train a diffusion model on retargeted human motions for real-time prediction of terrain-aware reference motions. Concurrently, we train a whole-body reference tracker with RL using this motion data. To improve robustness under imperfectly generated references, we further fine-tune the tracker with a frozen motion generator in a closed-loop setting. The resulting system supports directional goal-reaching control with terrain-aware whole-body adaptation, and can be deployed on a Unitree G1 humanoid robot with onboard perception and computation. The hardware experiments demonstrate successful traversal over boxes, hurdles, stairs, and mixed terrain combinations. Quantitative results further show the benefits of incorporating online motion generation and fine-tuning the motion tracker for improved generalization and robustness.

Zewei Zhang, Kehan Wen, Michael Xu +7

Robotic Systems Lab, ETH Zurich · EPFL · Simon Fraser University +2

Robot Trajectory Tracking Humanoid Robot Locomotion Terrain-Aware Robot Locomotion Robot Locomotion RL for Legged Locomotion Robot Imitation Learning+2

Apr 18, 2026cs.RO

Neural Network-Based Adaptive Event-Triggered Control for Dual-Arm Unmanned Aerial Manipulator Systems

This paper investigates the control problem of dual-arm unmanned aerial manipulator systems (DAUAMs). Strong coupling between the dual-arm and the multirotor platform, together with unmodeled dynamics and external disturbances, poses significant challenges to stable and accurate operation. An adaptive event-triggered control scheme with neural network-based approximation is proposed to address these issues while explicitly considering communication constraints. First, a dynamic model of the DAUAM system is derived, and a command-filter-based backstepping framework with error compensation is constructed. Then, a neural network is employed to approximate external frictions, and an event-triggered mechanism is designed to reduce the transmission frequency of control updates, thereby alleviating communication and energy burdens. Lyapunov-based analysis shows that all closed-loop signals remain bounded and that the tracking error converges to a neighborhood of the desired trajectory within a fixed time. Finally, experiments on a self-built DAUAM platform demonstrate that the proposed approach achieves accurate trajectory tracking.

Yang Wang, Hai Yu, Wei He +3

Institute of Robotics and Automatic Information System, College of Artificial Intelligence, and Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300350, China. · Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Shenzhen 518083, China.

Robot Trajectory Tracking Aerial Robotics Feedback Control Quadrotor Control Event-Triggered Control Adaptive Control

Apr 18, 2026cs.RO

Refinement of Accelerated Demonstrations via Incremental Iterative Reference Learning Control for Fast Contact-Rich Imitation Learning

Fast execution of contact-rich manipulation is critical for practical deployment, yet providing fast demonstrations for imitation learning (IL) remains challenging: humans cannot demonstrate at high speed, and naively accelerating demonstrations alters contact dynamics and induces large tracking errors. We present a method to autonomously refine time-accelerated demonstrations by repurposing Iterative Reference Learning Control (IRLC) to iteratively update the reference trajectory from observed tracking errors. However, applying IRLC directly at high speed tends to produce larger early-iteration errors and less stable transients. To address this issue, we propose Incremental Iterative Reference Learning Control (I2RLC), which gradually increases the speed while updating the reference, yielding high-fidelity trajectories. We validate on real-robot whiteboard erasing and peg-in-hole tasks using a teleoperation setup with a compliance-controlled follower and a 3D-printed haptic leader. Both IRLC and I2RLC achieve up to 10x faster demonstrations with reduced tracking error; moreover, I2RLC improves spatial similarity to the original trajectories by 22.5% on average over IRLC across three tasks and multiple speeds (3x-10x). We then use the refined trajectories to train IL policies; the resulting policies execute faster than the demonstrations and achieve 100% success rates in the peg-in-hole task at both seen and unseen positions, with I2RLC-trained policies exhibiting lower contact forces than those trained on IRLC-refined demonstrations. These results indicate that gradual speed scheduling coupled with reference adaptation provides a practical path to fast, contact-rich IL.

Koki Yamane, Cristian C. Beltran-Hernandez, Steven Oh +2

OMRON SINIC X Corporation, Bunkyo-ku, Tokyo 113-0033, Japan · Waseda University, Shinjuku-ku, Tokyo, 169-8050, Japan · University of Tsukuba, Tsukuba, Ibaraki 305-8573, Japan

Robot Trajectory Tracking Contact-Rich Robotic Manipulation Learning from Demonstration Robot Imitation Learning Adaptive Control

Mar 13, 2026cs.RO

PhyGile: Physics-Prefix Guided Motion Generation for Agile General Humanoid Motion Tracking

Humanoid robots are expected to execute agile and expressive whole-body motions in real-world settings. Existing text-to-motion generation models are predominantly trained on captured human motion datasets, whose priors assume human biomechanics, actuation, mass distribution, and contact strategies. When such motions are directly retargeted to humanoid robots, the resulting trajectories may satisfy geometric constraints (e.g., joint limits and pose continuity) and appear kinematically reasonable. However, they frequently violate the physical feasibility required for real-world execution. To address these issues, we present PhyGile, a unified framework that closes the loop between robot-native motion generation and General Motion Tracking (GMT). PhyGile performs physics-prefix-guided robot-native motion generation at inference time, directly generating robot-native motions in a 262-dimensional skeletal space with physics-guided prefixes, thereby eliminating inference-time retargeting artifacts and reducing generation-execution discrepancies. Before physics-prefix adaptation, we train the GMT controller with a curriculum-based mixture-of-experts scheme, followed by post-training on unlabeled motion data to improve robustness over large-scale robot motions. During physics-prefix adaptation, the GMT controller is further fine-tuned with generated objectives under physics-derived prefixes, enabling agile and stable execution of complex motions on real robots. Extensive offline and real-robot experiments demonstrate that PhyGile expands the frontier of text-driven humanoid control, enabling stable tracking of agile, highly difficult whole-body motions that go well beyond walking and low-dynamic motions typically achieved by prior methods.

Jiacheng Bao, Haoran Yang, Yucheng Xin +7

Shanghai AI Laboratory · University of Science and Technology of China · Tsinghua University +3

Robot Trajectory Tracking Humanoid Robot Control Robot Motion Generation Humanoid Motion Tracking

Mar 5, 2026cs.RO

Residual RL-MPC for Robust Microrobotic Cell Pushing Under Time-Varying Flow

Contact-rich micromanipulation in microfluidic flow is challenging because small disturbances can break pushing contact and induce large lateral drift. We study planar cell pushing with a magnetic rolling microrobot that tracks a waypoint-sampled reference curve under time-varying Poiseuille flow in simulation. We propose a hybrid controller that augments a nominal MPC with a learned residual policy trained by SAC. The policy outputs a bounded 2D velocity correction that is contact-gated, so residual actions are applied only during robot-cell contact, preserving reliable approach behavior and stabilizing learning. All methods share the same actuation interface and speed envelope for fair comparisons. Simulation results show improved robustness and tracking accuracy over pure MPC and PID under nonstationary flow, with generalization from a clover training curve to unseen circle and square trajectories. A residual-bound sweep identifies an intermediate correction limit as the best trade-off, which we use in all benchmarks.

Yanda Yang, Sambeeta Das

Department of Mechanical Engineering, University of Delaware, Newark, DE 19716, USA

Robot Trajectory Tracking Contact-Rich Robotic Manipulation Robust Control Model Predictive Control Robotic Manipulation Residual Policy Learning

Mar 5, 2026cs.RO

Accelerating Sampling-Based Control via Learned Linear Koopman Dynamics

This paper presents an efficient model predictive path integral (MPPI) control framework for systems with complex nonlinear dynamics. To improve the computational efficiency of classic MPPI while preserving control performance, we replace the nonlinear dynamics used for trajectory propagation with a learned linear deep Koopman operator (DKO) model, enabling faster rollout and more efficient trajectory sampling. The DKO dynamics are learned directly from interaction data, eliminating the need for analytical system models. The resulting controller, termed MPPI-DK, is evaluated in simulation on pendulum balancing and surface vehicle navigation tasks, and validated on hardware through reference-tracking experiments on a quadruped robot. Experimental results demonstrate that MPPI-DK achieves control performance close to MPPI with true dynamics while substantially reducing computational cost, enabling efficient real-time control on robotic platforms.

Wenjian Hao, Yuxuan Fang, Zehui Lu +1

independent researcher

Robotic Control Robot Trajectory Tracking MPPI Control Optimal Control

Feb 21, 2026cs.RO

Learning to Localize Reference Trajectories in Image-Space for Visual Navigation

We present LoTIS, a model for visual navigation that provides robot-agnostic image-space guidance by localizing a reference RGB trajectory in the robot's current view, without requiring camera calibration, poses, or robot-specific training. Instead of predicting actions tied to specific robots, we predict the image-space coordinates of the reference trajectory as they would appear in the robot's current view. This creates robot-agnostic visual guidance that easily integrates with local planning. Consequently, our model's predictions provide guidance zero-shot across diverse embodiments. By decoupling perception from action and learning to localize trajectory points rather than imitate behavioral priors, we enable a cross-trajectory training strategy for robustness to viewpoint and camera changes. We outperform state-of-the-art methods by 20-50 percentage points in success rate on conventional forward navigation, achieving 94-98% success rate across diverse sim and real environments. Furthermore, we achieve over 5x improvements on challenging tasks where baselines fail, such as backward traversal. The system is straightforward to use: we show how even a video from a phone camera directly enables different robots to navigate to any point on the trajectory. Videos, demo, and code are available at https://finnbusch.com/lotis.

Finn Lukas Busch, Matti Vahs, Quantao Yang +4

Division of Robotics, Perception, and Learning, KTH Royal Institute of Technology, Sweden, and also affiliated with Digital Futures

Robot Trajectory Tracking Robot Navigation Vision-Based Robot Control Cross-Embodiment Transfer

Feb 2, 2026cs.RO

Tilt-Ropter: A Fully Actuated Hybrid Aerial-Terrestrial Vehicle with Tilt Rotors and Passive Wheels

In this work, we present Tilt-Ropter, a fully actuated hybrid aerial-terrestrial vehicle (HATV) that integrates tilt rotors with passive wheels to enable efficient multi-modal locomotion. Unlike conventional underactuated HATVs, the fully actuated design of Tilt-Ropter allows decoupled force and torque control, improving maneuverability and ground locomotion efficiency. A unified nonlinear model predictive controller (NMPC) is developed to track reference trajectories, enforce non-holonomic constraints, and accommodate contact effects across locomotion modes, while ensuring actuator feasibility through dedicated control allocation. To address complex wheel-ground dynamics, an external wrench estimator is incorporated to provide real-time interaction wrench estimates. The system is validated through simulation and real-world experiments, including seamless air-ground transitions and trajectory tracking tasks. Experimental results demonstrate low tracking errors in both modes and reveal a 92.8% reduction in power consumption during ground locomotion compared with flight, highlighting the platform's potential for long-duration missions where energy efficiency is critical.

Ruoyu Wang, Xuchen Liu, Zongzhou Wu +3

Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong · Faculty of Engineering, The University of Hong Kong, Pokfulam, Hong Kong · Peng Cheng Laboratory, Shenzhen, Guangdong, China

Robotic Control Robot Trajectory Tracking Aerial Robotics Robotics

Dec 13, 2025cs.RO

Temporal Cascading of Planning and Control for Quadrotor MPC

Many aerial tasks involving quadrotors demand both instant reactivity and long-horizon planning for obstacle avoidance, energy efficiency, or trajectory tracking. High-fidelity models enable accurate control but are too slow for long horizons. Low-fidelity planners scale but cannot directly control the system, necessitating cascaded architectures. Prevailing hierarchical approaches plan with a simplified model and use a high-fidelity controller for tracking, yet this decomposition is inherently suboptimal. The controller is limited by the coarse plan, and conventional MPC alternatives shorten the horizon to stay real-time feasible. We present UNIQUE, an MPC architecture that replaces this hierarchical stacking with temporal cascading. The planning problem is formulated as the second-tail horizon of a single multi-phase MPC, rather than being solved separately. We align costs across horizons, derive feasibility constraints for the point-mass planning model, and introduce transition constraints that convert high-fidelity states into meaningful low-fidelity states. Parallel point-mass and mixed-integer solvers address nonconvexities while incorporating progressive 3D obstacle smoothing over the planning horizon. In simulations and real flights, under equal computational budgets, UNIQUE improves closed-loop tracking by up to 75% compared with standard MPC and hierarchical baselines. Ablations and Pareto analyses confirm performance gains across variations in horizon, constraint approximations, and smoothing schedules.

Rudolf Reiter, Chao Qin, Leonard Bauersfeld +1

Robotics and Perception Group, Department of Informatics, University of Zurich, Andreasstrasse 15, 8050 Zurich, Switzerland · Institute for Aerospace Studies, University of Toronto, 4925 Dufferin Street, Toronto, ON M3H 5T6, Canada

Robot Trajectory Tracking Aerial Robotics Model Predictive Control UAV Trajectory Optimization Quadrotor Control Safe Motion Planning

Jun 10, 2025eess.SY

Differentiable Physics-Informed Adaptive Koopman Control for Stable Flight under Unknown Disturbances

Uncertainties and disturbances in robotic systems, such as aerodynamic forces, are fundamentally outcomes of physical interactions with the environment, manifesting as learnable spatiotemporal sequences rather than random noise. However, achieving high-precision control for robotic systems operating in unstructured environments is often hindered by complex unmodeled dynamics and external disturbances. While learning-based methods offer powerful approximation capabilities, they typically suffer from heavy reliance on offline training and lack theoretical guarantees. Conversely, traditional robust control strategies are predominantly reactive, limited to instantaneous estimation without the foresight to anticipate future disturbance trends. To bridge this gap, this paper proposes a differentiable data-enabled Koopman control framework termed DEKC. Unlike black-box approaches, DEKC adopts a hybrid modeling strategy that retains the nominal physics model while employing a deep neural network to parameterize the lifting function of Koopman operator for unknown residual dynamics. Crucially, the framework formulates disturbances as a dynamical system, learning their temporal evolution in a global linear space. This enables the prediction of future disturbance trajectories, which are explicitly integrated into controller for preemptive compensation. Furthermore, an online backward gradient update mechanism is introduced to ensure real-time adaptation to time-varying uncertainties. Numerical simulations on a tethered space robot demonstrate the efficacy of the proposed DEKC in mitigating highly coupled uncertainties. Complementing these results, real-world experiments on a quadrotor substantiate its superiority in tracking agile trajectories under uncertainties induced by aerodynamics and suspended payload.

Ao Jin, Qiujin Liang, Tao Zhang +2

Research Center for Intelligent Robotics, School of Astronautics, Northwestern Polytechnical University, Xi’an 710072, China · Department of Automation, Tsinghua University, Beijing 100080, China

Robot Trajectory Tracking Robust Control Feedback Control Quadrotor Control Adaptive Control

Jun 5, 2025cs.RO

A Three-Stage Offline SDRE-Based Control Framework for Human Motion Reproduction on a Suspended Bipedal Robot

This paper presents a three-stage offline command generation framework for reproducing human lower-limb motion on a suspended bipedal robot while matching torque trajectories computed from the robot dynamic model. First, State-Dependent Riccati Equation (SDRE) control derives the reference torque trajectory for the measured motion. Second, parameterized optimization converts this trajectory into trapezoidal joint velocity commands under motor speed and acceleration limits. Third, a proportional-integral-derivative linear quadratic regulator (PID-LQR) compensation scheme refines these commands using experimental tracking data. The platform executes the resulting profiles to reproduce human walking and squatting motions recorded by a Vicon system, allowing evaluation of tracking accuracy and repeatability. Results show that the average root mean square error (RMSE) and standard deviation (STD) of joint angles across repeated trials remain below 7° and 0.33°, respectively. Joint angle and torque trajectory comparisons show lower maximum RMSE and STD values than those for MPC and IPSO-PID in every reported case. The framework enables accurate and repeatable motion reproduction within actuator limits, providing controlled and measurable conditions that can reduce reliance on human participation and associated risks during preliminary evaluation of devices for assistive walking, gait training, and rehabilitation.

Ping-Kong Huang, Chien-Wu Lan, Chin-Tien Wu +1

Department of Applied Mathematics, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan · Department of Electrical Engineering, National Central University, Taoyuan City 320317, Taiwan

Robotic Control Robot Trajectory Tracking Robot Motion Generation Feedback Control

Mar 18, 2025cs.RO

Learning-Based Progressive Barrier Control for Robot Manipulators with Initial Errors Outside Prescribed Tracking Bounds

Robot manipulators may start a new task with a tracking error larger than the prescribed tolerance. Conventional barrier controllers generally require the initial error to lie within this tolerance, which prevents their direct use under such conditions. This paper develops a progressive barrier controller that gradually contracts an initial error bound to the required value within a prescribed time. The robot can therefore start outside the final bound, while the direct joint-position error satisfies it after the transition. The closed-form control law combines progressive barrier feedback with an online adaptive torque term based on an actor--critic structure. A Lyapunov analysis establishes bounded closed-loop signals and gives sufficient conditions for satisfaction of the final tracking bound. Two-link simulations consider large initial errors, actuator saturation, dynamic variations, disturbances, and measurement errors. The adaptive term reduces the median root-mean-square tracking error by 45.1% compared with the zero-weight progressive barrier controller. The simulations also map the initial errors that can be handled at different transition times under fixed torque limits. Finally, an experiment on a Niryo Ned3 Pro illustrates tracking performance using measured position and motor-current data.

Hamed Rahimi Nohooji, Danial Zafaranchizadeh Moghaddam, Abolfazl Zaraki +1

Automation and Robotics Research Group, Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, 1855 Luxembourg, Luxembourg · School of Physics, Engineering and Computer Science (SPECS), and Robotics Research Group of the University of Hertfordshire, Hatfield, AL10 9AB, United Kingdom

Robotic Control Robot Trajectory Tracking Feedback Control Adaptive Control

Date pendingcs.RO

Simplifying ROS2 controllers with a modular architecture for robot-agnostic reference generation

This paper introduces a novel modular architecture for ROS2 that decouples the logic required to acquire, validate, and interpolate references from the control laws that track them. The design includes a dedicated component, named Reference Generator, that receives references, in the form of either single points or trajectories, from external nodes (e.g., planners), and writes single-point references at the controller's sampling period via the existing ros2_control chaining mechanism to downstream controllers. This separation removes duplicated reference-handling code from controllers and improves reusability across robot platforms. We implement two reference generators: one for handling joint-space references and one for Cartesian references, along with a set of new controllers (PD with gravity compensation, Cartesian pose, and admittance controllers) and validate the approach on simulated and real Universal Robots and Franka Emika manipulators. Results show that (i) references are tracked reliably in all tested scenarios, (ii) reference generators reduce duplicated reference-handling code across chained controllers to favor the construction and reuse of complex controller pipelines, and (iii) controller implementations remain focused only on control laws.

Davide Risi, Vincenzo Petrone, Antonio Langella +3

Department of Information Engineering, Electrical Engineering and Applied Mathematics (DIEM), University of Salerno, 84084 Fisciano, Italy

Robotic Control Robot Trajectory Tracking

Date pendingeess.SY

Trajectory Tracking Control Design for Autonomous Helicopters with Guaranteed Error Bounds

This paper presents a systematic framework for computing formally guaranteed trajectory tracking error bounds for autonomous helicopters based on Robust Positive Invariant (RPI) sets. The approach establishes a closed-loop translational error dynamics which is cast into polytopic linear parameter-varying form with bounded additive and state-dependent disturbances. Ellipsoidal RPI sets are computed, yielding explicit position error bounds suitable as certified buffer zones in upper-level trajectory planning. Three controller architectures are compared with respect to the conservatism of their error bounds and tracking performance. Simulation results on a nonlinear helicopter model demonstrate that all architectures satisfy the derived bounds, while highlighting trade-offs between performance and the conservatism of the computed invariant set.

Philipp Schitz, Johann C. Dauer, Paolo Mercorelli

Institute of Flight Systems, German Aerospace Center (DLR), 38108 Braunschweig, Germany · Institute for Production Technology and Systems, Leuphana University of Lueneburg, 21335 Lueneburg, Germany

Robot Trajectory Tracking Aerial Robotics Robust Control