8 citations · 33 across the 17 of their papers we have counts for
14 papers · 1 filter
From LLM Plans to Authorized Motion: Execution-Bound Authorization for Excavators
Mehdi Heydari Shahna, Seihun Kim, Soyi Jung +3
An LLM-directed excavator can execute mechanically feasible motion whose destination or authority no longer matches the operator's approval. We propose execution-bound authorizatio…
Composable Dynamics for Cooperative Humanoids: A RoboCompiler Extension
Mehdi Heydari Shahna, Jouni Mattila
Physically connected humanoids need consistent dynamics for internal actuator loops, inter-robot forces, and changing ground supports. We extend RoboCompiler with member-wise dynam…
RoboCompiler: Graph-Native Compilation of Closed-Chain Robots for Consistent Modeling, Control, and Simulation
Mehdi Heydari Shahna, Joongheon Kim, Jouni Mattila
Robots with kinematic loops, coupled actuators, and changing contacts require consistent models of configuration, motion, force, and dynamics. Yet these interfaces are often recons…
IL-ACT: Imitation Learning with Adaptive Cartesian Tracking Control for a 30-ton Excavator
Mehdi Heydari Shahna, Seihun Kim, Soyi Jung +3
Autonomous excavator control is challenged by coupled kinematics, actuation lag, and uncertainty. We propose imitation learning and adaptive Cartesian tracking (IL-ACT), a novel mo…
Pose-Parameterized Motion Planning and CBF-QP Self-Collision Filtering for a Long-Reach Drilling Boom
Mehdi Heydari Shahna, Tuomo Kivelä, Jouni Mattila
Long-reach drilling booms must reach successive poses without self-collision. Moving from operator-supervised control toward autonomy requires collision-aware motion planning and e…
NMPC-Augmented Visual Navigation and Safe Learning Control for Large-Scale Mobile Robots
Mehdi Heydari Shahna, Pauli Mustalahti, Jouni Mattila
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…
Reversible Simplex Supervision with Post-Action Debt Accounting for Goal-Reaching RL
Mehdi Heydari Shahna, Joongheon Kim, Jouni Mattila
Deploying reinforcement learning (RL) on multi-tonne robots calls for supervisory mechanisms that address both operational safety and progress toward task completion. However, repe…
Vision-based Goal-Reaching Control for Mobile Robots Using a Hierarchical Learning Framework
Mehdi Heydari Shahna, Pauli Mustalahti, Jouni Mattila
Reinforcement learning (RL) has strong potential in robotics, but exploration-based training complicates safe deployment on large-scale robots. For such applications, this paper pr…
Robust Deep Learning Control with Guaranteed Performance for Safe and Reliable Robotization in Heavy-Duty Machinery
Mehdi Heydari Shahna
Today's heavy-duty mobile machines (HDMMs) face two transitions: from diesel-hydraulic actuation to clean electric systems driven by climate goals, and from human supervision towar…
Synthesis of Deep Neural Networks with Safe Robust Adaptive Control for Reliable Operation of Wheeled Mobile Robots
Mehdi Heydari Shahna, Jouni Mattila
Deep neural networks (DNNs) can enable precise control while maintaining low computational costs by circumventing the need for dynamic modeling. However, the deployment of such bla…
LiDAR-Inertial SLAM-Based Navigation and Safety-Oriented AI-Driven Control System for Skid-Steer Robots
Mehdi Heydari Shahna, Eemil Haaparanta, Pauli Mustalahti +1
Integrating artificial intelligence (AI) and stochastic technologies into the mobile robot navigation and control (MRNC) framework while adhering to rigorous safety standards prese…
Anti-Slip AI-Driven Model-Free Control with Global Exponential Stability in Skid-Steering Robots
Mehdi Heydari Shahna, Pauli Mustalahti, Jouni Mattila
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 accur…
Integrating DeepRL with Robust Low-Level Control in Robotic Manipulators for Non-Repetitive Reaching Tasks
Mehdi Heydari Shahna, Seyed Adel Alizadeh Kolagar, Jouni Mattila
In robotics, contemporary strategies are learning-based, characterized by a complex black-box nature and a lack of interpretability, which may pose challenges in ensuring stability…
Exponential Auto-Tuning Fault-Tolerant Control of N Degrees-of-Freedom Manipulators Subject to Torque Constraints
Mehdi Heydari Shahna, Jouni Mattila
This paper presents a novel auto-tuning subsystem-based fault-tolerant control (SBFC) system designed for robotic manipulator systems with n degrees of freedom (DoF). It initially…