arXiv subjects

Jouni Mattila

Publications and source records attributed to Jouni Mattila.

At least 19 recordsLinked to original sources

arXiv2026

Reversible Simplex Supervision with Post-Action Debt Accounting for Goal-Reaching RL

Deploying reinforcement learning (RL) on multi-tonne robots calls for supervisory mechanisms that address both operational safety and progress toward task completion. However, repeated switching need not preserve task progress when a learned action increases storage before recovery takes control. We introduce reversible Simplex supervision with post-action debt accounting for a frozen finite-state policy and robust-adaptive recovery. Under exact sampled-state information and stated model and certificate conditions, we prove that recovery repayment exceeding a uniform triggering-edge debt bound guarantees finite switching and finite-sample goal entry. We formulate reachability-based certificate constructions for establishing these sufficient conditions. In 20 matched simulations, goal-entry counts are 20 with debt gating and 18 without it; the two remaining runs terminate under the supervisor's admissibility stopping rule. On an experimental 6000 kg robot, 24 asphalt and soft-terrain trials evaluate 50 ms supervision above a 1 kHz actuator stack; all eight triggered recoveries complete debt-gated re-entry. The experiments demonstrate the supervisory mechanism in the tested trials.

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arXiv2026

IL-ACT: Imitation Learning with Adaptive Cartesian Tracking Control for a 30-ton Excavator

Autonomous excavator control is challenged by coupled kinematics, actuation lag, and uncertainty. We propose imitation learning and adaptive Cartesian tracking (IL-ACT), a novel motion control framework for a 30-ton-class excavator. An anchored, 14-input imitation policy pretrained on operator demonstrations generates nominal joint rates; adaptive Cartesian feedback and gated gain/bias estimation correct these commands before a stopping-distance governor constrains joint-reference generation. Simscape evaluation covers 100 sequential goals and spiral, figure-eight, and rounded-raster tracking, including 88 additional runs across three training seeds, two initializations, and speeds, under hydraulic response and sensing conditions. Compared with Teacher+ACT, IL-ACT completes all goals with shorter duration and lower terminal errors under both response conditions. Telemetry-initialized IL-ACT lowers RMSE in all 24 figure-eight and rounded-raster seed comparisons and lowers additional-load spiral mean RMSE by approximately 29%. Original spiral RMSE also improves over IL-only and PID. Under a shared sensor-noise realization, telemetry-initialized IL-ACT achieves 27.67% lower mean RMSE than Teacher+ACT; enabling estimation reduces mean RMSE by $22.44\%$ relative to the frozen estimator. Pretrained-weight effects remain mixed, and the original teacher comparison exhibits a spiral RMSE--maximum-error tradeoff. Analysis establishes bounded adaptive states and Cartesian feedback, with reference admissibility conditional on governor feasibility.

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arXiv2026

Modular Kinematic Reduction of Closed-Chain Mechanisms Using Path Assembly and Defect Homotopy

Closed kinematic chains complicate modular modeling by coupling active and passive coordinates through nonlinear closure constraints. This paper presents a Path-Assembled Closure Differential Mapping (PACDM) framework for modular closure resolution and kinematic reduction. Each closure element compares two ordered transformation paths with common endpoints, with their mismatch expressed through the logarithm on SE(3) and the corresponding Jacobian assembled from local transformation derivatives. Multi-path modules are constructed from a minimal set of pairwise closure elements, while rank-revealing analysis selects locally independent scalar constraints. A defect homotopy recovers closure-consistent passive coordinates from approximate estimates along a feasible and regular continuation path. At regular configurations, implicit differentiation yields the local active-to-passive differential mapping, which is subsequently used in a predictor-corrector continuation procedure for prescribed motion. The framework is evaluated on a seven-degree-of-freedom heavy-duty manipulator containing two-path and three-path closed-chain modules. Comparison with Simscape Multibody yields trajectory root-mean-square errors below 8.5 x 10^-10 rad, while predictor-corrector continuation is approximately 45.8 times faster than applying defect homotopy at every trajectory sample.

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arXiv2026

Robust Immersive Bilateral Teleoperation of Beyond-Human-Scale Systems with Enhanced Transparency and Sense of Embodiment

This paper presents an immersive bilateral teleoperation framework for beyond-human-scale manipulators, combining motion/force transparency with enhanced operator embodiment through virtual reality (VR) and distributed haptic feedback. The platform integrates a full-scale industrial hydraulic manipulator, a 7-DoF haptic exoskeleton, and head-tracked visual feedback to reinforce operator agency and self-location. A force-sensorless adaptive controller incorporates an augmented human-robot dynamic model to address master-surrogate asymmetries, input nonlinearities, model uncertainties, and communication delays. Rigorous analysis establishes semi-global uniform ultimate boundedness of the closed-loop system. Extensive full-scale experiments demonstrate precise motion and force tracking with scaling up to 1:13 and 1:1000, respectively, and fixed or time-varying delays up to 150 ms. Coordinated 6-DoF pick-and-place and multi-axis contact tasks further validate system performance. A ten-participant user study supports system usability and indicates approximately 50% higher normalized embodiment scores with VR than with monitor-based feedback. These results advance intuitive, force-reflected remote manipulation at industrial scale.

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arXiv2026

Vision-based Goal-Reaching Control for Mobile Robots Using a Hierarchical Learning Framework

Reinforcement learning (RL) has strong potential in robotics, but exploration-based training complicates safe deployment on large-scale robots. For such applications, this paper proposes a novel hierarchical goal-reaching framework that integrates stereo visual pose estimation, constrained RL-based motion planning, actuator-level robust adaptive control (RAC), and supervisory safe-return logic. Stereo visual localization is used as the real-time pose-estimation interface with loop closing, map fusion, and relocalization. The RL planner generates smooth, feasible goal-reaching references using a problem-specific reward structure and motion constraints that promote goal progress, reduce oscillations, preserve vision-consistent smoothness, and respect the mechanical limits of a heavy skid-steered robot. At the actuation layer, a scaled conjugate-gradient (SCG)-trained deep neural network (DNN) approximates a quasi-static actuator feedforward map from wheel-speed data to nominal control input. This feedforward map is combined with a logarithmic-barrier-based RAC to compensate for residual modeling errors, slip-induced disturbances, and bounded mismatch between the nominal map and real actuator response. For the actuator-level wheel-tracking subsystem, uniformly ultimately bounded tracking with exponential convergence to a disturbance-dependent residual set is established under bounded uncertainty. A logarithmic safety supervisor monitors execution, detects unsafe operating conditions, including faults and localization inconsistencies, and switches the robot to safe-return mode. Experiments on a 6000 kg robot over asphalt and loose-soil terrain demonstrate approximately 3--4 cm final-position root mean square error (RMSE), accurate tracking of RL-generated commands, improved actuator-level performance over two RAC baselines, and successful autonomous recovery after fault injection.

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arXiv2026

Pose-Parameterized Motion Planning and CBF-QP Self-Collision Filtering for a Long-Reach Drilling Boom

Long-reach drilling booms must reach successive poses without self-collision. Moving from operator-supervised control toward autonomy requires collision-aware motion planning and execution. For the Sandvik SB60, this study adapts established methods by integrating pose-parameterized planning with a capsule-based control barrier function quadratic program (CBF-QP) in measured-state inverse kinematics (IK). A fixed task-specific parameter set within each task generates waypoints, detours, timed references, and chained motion without target-specific retuning. The offline detour planner screens candidate waypoints using 23 selected rod-segment-to-body-region distances, whereas the online CBF-QP filters joint velocities using 14 configured capsule-pair constraints from a nine-primitive whole-body capsule model. Evaluation considers two drilling tasks in a manufacturer-developed SB60 Simscape Multibody model: a five-target restricted-orientation tour and a three-target full-pose tour. Across several hundred thousand samples, the method produced zero IK failures, generated several detour waypoints, achieved millimetre-level mean final-position error, and recorded no sampled CBF margins below the reported thresholds.

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arXiv2026

Adaptive Modular Geometric Control of Robotic Manipulators

This paper develops an adaptive modular geometric control framework for robotic manipulators with uncertain inertial parameters. The manipulator is decomposed into rigid-body and joint modules, where each rigid-body module is represented by an Euler-Poincaré-type spatial dynamics on the Lie algebra se(3), and configuration errors are defined intrinsically through logarithmic maps on SE(3). The joint modules impose local screw constraints that relate adjacent body twists, accelerations, and transmitted wrenches, yielding a recursive propagation structure for the interconnected multibody system. Within this formulation, local geometric control laws are constructed at the module level, while the interconnection among modules is characterized by power-conjugate twist--wrench pairs induced by the natural duality pairing between the Lie algebra se(3) and its dual space se(3)^*. For the nominal case, exponential tracking stability of the interconnected system is established using local configuration energy functions on SE(3) and the power-preserving structure of the modular interconnection. To address inertial parametric uncertainty, a geometric adaptation law is introduced on the manifold of symmetric positive-definite matrices, ensuring physically consistent parameter estimates while retaining compatibility with the Lie-algebraic control formulation. Under the adaptive controller, semi-global uniform ultimate boundedness of the closed-loop tracking and parameter estimation errors is proven. Numerical simulations on a redundant high-inertia robotic manipulator demonstrate accurate pose tracking, smooth transient behavior, orientation regulation, and robustness under inertial perturbations. Comparative studies with state-of-the-art methods further illustrate the effectiveness of the proposed framework for complex robotic manipulation tasks.

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arXiv2026

Actuator-Aware Inverse Kinematics with Joint-Limit Admissibility for Torque-Controlled Redundant Robots

This paper proposes actuator-aware inverse kinematics for torque-controlled redundant robots under joint-limit constraints. In the considered architecture, the inverse-kinematic output is not merely a purely kinematic joint-velocity command; it is the required joint velocity supplied to a downstream torque-level controller. Therefore, a small commanded task residual may not necessarily improve realized motion. The proposed method formulates a convex quadratic programming problem whose decision variable is the joint-level required velocity. Control barrier function style bounds impose reference-level joint-limit admissibility, while the task equation is handled through a penalized slack variable. Redundancy is resolved using a controller-compatibility objective that accounts for previous-command consistency and actuator torque-capacity weighting. The method is independent of the particular torque-level controller and can serve as an intermediate IK layer between an endpoint trajectory and a redundant robot controller. Experiments on a virtual-decomposition-controlled seven-degree-of-freedom upper-limb exoskeleton compare the method with standard inverse-kinematic baselines and a constrained task-preserving quadratic programming baseline. The results indicate lower limit-pushing commands, bounded admissible required velocities, and improved realized task behavior in the tested trajectory, without modifying the downstream controller.

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arXiv2026

Modular Lie Algebraic PDE Control of Multibody Flexible Manipulators

This paper presents a subsystem-based adaptive control framework for serial flexible manipulators with an arbitrary number of links, in which the elastic deformation PDE of each link is carried through the entire control design without spatial discretization or modal truncation. All dynamic quantities -- rigid-body motion, elastic deformation, and inter-link constraint forces -- are expressed uniformly as body-fixed twists and wrenches within the se3 Lie-algebraic structure. A controllable form of the per-link dynamics is derived by substituting the strain-based deformation PDE into the dynamic equation, eliminating distributed elastic acceleration and yielding a model governed by the body-fixed twist acceleration and deformation field. Desired subsystem twist trajectories are generated via a deflection-compensating inverse kinematics procedure. A nominal per-link controller is proven to produce exponential twist error decay via a per-subsystem Lyapunov function. An adaptive modification replaces exact physical parameters with online estimates governed by a projection-based law, augmenting with a parameter estimation error term. Upon summing over all links, the interaction power terms telescope to zero by Newton's third law and the frame invariance of the natural power pairing on se3*se*(3), establishing exponential convergence of all twist errors and bounded elastic deformation under both nominal and adaptive controllers. The screw-theoretic structure renders interaction term cancellation exact, making the stability certificate modular and scalable to chains of arbitrary length. The framework is validated numerically on a two-link flexible manipulator in three-dimensional motion.

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arXiv2026

Inertia Partitioning Modular Robust Control Framework for Reconfigurable Multibody Systems

A novel modular modeling and control framework based on Lagrangian mechanics is proposed for multibody systems, motivated by the challenges of modular control of systems with closed kinematic chains and by the need for a modeling framework that remains locally updatable under reconfiguration of body-level geometric and inertial properties. In the framework, modularity is defined with respect to the degrees of freedom of the multibody system, represented in the model by the minimal generalized coordinates, and the inertial properties of each body are partitioned with respect to how they are reflected in the kinetic energy of the system through the motion induced by each degree of freedom. By expressing body contributions through body-fixed-frame Jacobians and spatial inertia matrices, the dynamic model remains locally updatable under changes in geometric and inertial parameters, which is advantageous for reconfigurable multibody systems. For multibody systems in which a mapping between the auxiliary and minimal generalized coordinates is available, the approach accommodates closed kinematic chains in a minimal-coordinate ordinary-differential-equation form without explicit constraint-force calculation or differential-algebraic-equation formulation. Based on the resulting modular equations of motion, a robust model-based controller is designed for trajectory tracking, and practical boundedness of the tracking error is analyzed under bounded uncertainty and external disturbance. The proposed framework is implemented in simulation on a three-degree-of-freedom series-parallel manipulator, where uncertainties and disturbances are introduced to assess robustness. The results are consistent with the expected stability and tracking performance, indicating the potential of the framework for trajectory-tracking control of reconfigurable multibody systems with closed kinematic chains.

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arXiv2026

NMPC-Augmented Visual Navigation and Safe Learning Control for Large-Scale Mobile Robots

A large-scale mobile robot (LSMR) is a high-order multibody system that often operates on loose, unconsolidated terrain, which reduces traction. This paper presents a comprehensive navigation and control framework for an LSMR that ensures stability and safety-defined performance, delivering robust operation on slip-prone terrain by jointly leveraging high-performance techniques. The proposed architecture comprises four main modules: (1) a visual pose-estimation module that fuses onboard sensors and stereo cameras to provide an accurate, low-latency robot pose, (2) a high-level nonlinear model predictive control that updates the wheel motion commands to correct robot drift from the robot reference pose on slip-prone terrain, (3) a low-level deep neural network control policy that approximates the complex behavior of the wheel-driven actuation mechanism in LSMRs, augmented with robust adaptive control to handle out-of-distribution disturbances, ensuring that the wheels accurately track the updated commands issued by high-level control module, and (4) a logarithmic safety module to monitor the entire robot stack and guarantees safe operation. The proposed low-level control framework guarantees uniform exponential stability of the actuation subsystem, while the safety module ensures the whole system-level safety during operation. Comparative experiments on a 6,000 kg LSMR actuated by two complex electro-hydrostatic drives, while synchronizing modules operating at different frequencies.

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arXiv2025

Anti-Slip AI-Driven Model-Free Control with Global Exponential Stability in Skid-Steering Robots

Undesired lateral and longitudinal wheel slippage can disrupt a mobile robot's heading angle, traction, and, eventually, desired motion. This issue makes the robotization and accurate modeling of heavy-duty machinery very challenging because the application primarily involves off-road terrains, which are susceptible to uneven motion and severe slippage. As a step toward robotization in skid-steering heavy-duty robot (SSHDR), this paper aims to design an innovative robust model-free control system developed by neural networks to strongly stabilize the robot dynamics in the presence of a broad range of potential wheel slippages. Before the control design, the dynamics of the SSHDR are first investigated by mathematically incorporating slippage effects, assuming that all functional modeling terms of the system are unknown to the control system. Then, a novel tracking control framework to guarantee global exponential stability of the SSHDR is designed as follows: 1) the unknown modeling of wheel dynamics is approximated using radial basis function neural networks (RBFNNs); and 2) a new adaptive law is proposed to compensate for slippage effects and tune the weights of the RBFNNs online during execution. Simulation and experimental results verify the proposed tracking control performance of a 4,836 kg SSHDR operating on slippery terrain.

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arXiv2025

Full-Dynamics Real-Time Nonlinear Model Predictive Control of Heavy-Duty Hydraulic Manipulator for Trajectory Tracking Tasks

Heavy-duty hydraulic manipulators (HHMs) operate under strict physical and safety-critical constraints due to their large size, high power, and complex nonlinear dynamics. Ensuring that both joint-level and end-effector trajectories remain compliant with actuator capabilities, such as force, velocity, and position limits, is essential for safe and reliable operation, yet remains largely underexplored in real-time control frameworks. This paper presents a nonlinear model predictive control (NMPC) framework designed to guarantee constraint satisfaction throughout the full nonlinear dynamics of HHMs, while running at a real-time control frequency of 1 kHz. The proposed method combines a multiple-shooting strategy with real-time sensor feedback, and is supported by a robust low-level controller based on virtual decomposition control (VDC) for precise joint tracking. Experimental validation on a full-scale hydraulic manipulator shows that the NMPC framework not only enforces actuator constraints at the joint level, but also ensures constraint-compliant motion in Cartesian space for the end-effector. These results demonstrate the method's capability to deliver high-accuracy trajectory tracking while strictly respecting safety-critical limits, setting a new benchmark for real-time control in large-scale hydraulic systems.

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arXiv2025

AI-Enhanced Kinematic Modeling of Flexible Manipulators Using Multi-IMU Sensor Fusion

This paper presents a novel framework for estimating the position and orientation of flexible manipulators undergoing vertical motion using multiple inertial measurement units (IMUs), optimized and calibrated with ground truth data. The flexible links are modeled as a series of rigid segments, with joint angles estimated from accelerometer and gyroscope measurements acquired by cost-effective IMUs. A complementary filter is employed to fuse the measurements, with its parameters optimized through particle swarm optimization (PSO) to mitigate noise and delay. To further improve estimation accuracy, residual errors in position and orientation are compensated using radial basis function neural networks (RBFNN). Experimental results validate the effectiveness of the proposed intelligent multi-IMU kinematic estimation method, achieving root mean square errors (RMSE) of 0.00021~m, 0.00041~m, and 0.00024~rad for $y$, $z$, and $θ$, respectively.

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arXiv2025

Real-Time Nonlinear Model Predictive Control of Heavy-Duty Skid-Steered Mobile Platform for Trajectory Tracking Tasks

This paper presents a framework for real-time optimal controlling of a heavy-duty skid-steered mobile platform for trajectory tracking. The importance of accurate real-time performance of the controller lies in safety considerations of situations where the dynamic system under control is affected by uncertainties and disturbances, and the controller should compensate for such phenomena in order to provide stable performance. A multiple-shooting nonlinear model-predictive control framework is proposed in this paper. This framework benefits from suitable algorithm along with readings from various sensors for genuine real-time performance with extremely high accuracy. The controller is then tested for tracking different trajectories where it demonstrates highly desirable performance in terms of both speed and accuracy. This controller shows remarkable improvement when compared to existing nonlinear model-predictive controllers in the literature that were implemented on skid-steered mobile platforms.

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arXiv2025

All-Electric Heavy-Duty Robotic Manipulator: Actuator Configuration Optimization and Sensorless Control

This paper presents a unified framework that integrates modeling, optimization, and sensorless control of an all-electric heavy-duty robotic manipulator (HDRM) driven by electromechanical linear actuators (EMLAs). An EMLA model is formulated to capture motor electromechanics and direction-dependent transmission efficiencies, while a mathematical model of the HDRM, incorporating both kinematics and dynamics, is established to generate joint-space motion profiles for prescribed TCP trajectories. A safety-ensured trajectory generator, tailored to this model, maps Cartesian goals to joint space while enforcing joint-limit and velocity margins. Based on the resulting force and velocity demands, a multi-objective Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed to select the optimal EMLA configuration. To accelerate this optimization, a deep neural network, trained with EMLA parameters, is embedded in the optimization process to predict steady-state actuator efficiency from trajectory profiles. For the chosen EMLA design, a physics-informed Kriging surrogate, anchored to the analytic model and refined with experimental data, learns residuals of EMLA outputs to support force and velocity sensorless control. The actuator model is further embedded in a hierarchical virtual decomposition control (VDC) framework that outputs voltage commands. Experimental validation on a one-degree-of-freedom EMLA testbed confirms accurate trajectory tracking and effective sensorless control under varying loads.

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arXiv2025

Robust Sensor-Limited Control with Safe Input-Output Constraints for Hydraulic In-Wheel Motor Drive Mobility Systems

In-wheel drive (IWD) systems enhance the responsiveness, traction, and maintenance efficiency of vehicles by enabling each wheel to operate independently. This paper proposes a novel robust torque-observed valve-based control (RTOVC) framework to address velocity tracking in hydraulic IWDs that actuate heavy-duty wheeled mobile robots (HWMRs), considering such challenges as wheel slippages, sensor limitations, rough terrains, and modeling uncertainties. To overcome the sensor-dependent control systems associated with the closed-loop torque/pressure in hydraulic IWD-actuated HWMRs, a robust observer network based on an adaptive barrier Lyapunov function (BLF) is proposed to estimate the required in-wheel motor torque to track the velocity references. Then, another adaptive BLF for valve control signals is employed to modulate the hydraulic fluid to generate the estimated torque for each IWD. The RTOVC strategy ensures user-defined safety within the logarithmic BLF framework by constraining the valve control signal, actual velocity, velocity tracking error, and torque of each hydraulic IWD in an HWMR to avoid exceeding specified limits. Despite its safety constraints, external disturbances, and modeling uncertainties, robustness and uniformly exponential stability of the RTOVC-applied hydraulic IWD mechanism are ensured in HWMRs. Experimental investigations using a 6,500-kg HWMR, actuated by four independent IWDs under intense disturbances and safety-defined constraints, validate the performance of the RTOVC.

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arXiv2025

Model-Free Generic Robust Control for Servo-Driven Actuation Mechanisms with Layered Insight into Energy Conversions

To advance theoretical solutions and address limitations in modeling complex servo-driven actuation systems experiencing high non-linearity and load disturbances, this paper aims to design a practical model-free generic robust control (GRC) framework for these mechanisms. This framework is intended to be applicable across all actuator systems encompassing electrical, hydraulic, or pneumatic servomechanisms, while also functioning within complex interactions among dynamic components and adhering to control input constraints. In this respect, the state-space model of actuator systems is decomposed into smaller subsystems that incorporate the first principle equation of actuator motion dynamics and interactive energy conversion equations. This decomposition operates under the assumption that the comprehensive model of the servo-driven actuator system and energy conversion, uncertainties, load disturbances, and their bounds are unknown. Then, the GRC employs subsystem-based adaptive control strategies for each state-variant subsystem separately. Despite control input constraints and the unknown interactive system model, the GRC-applied actuator mechanism ensures uniform exponential stability and robustness in tracking desired motions. It features straightforward implementation, experimentally evaluated by applying it to two industrial applications.

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