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Motion planning and control are crucial components of robotics applications like automated driving. Here, spatio-temporal hard constraints like system dynamics and safety boundaries (e.g., obstacles) restrict the robot's motions. Direct…

Accurate robot localization is essential for effective operation. Monte Carlo Localization (MCL) is commonly used with known maps but is computationally expensive due to landmark matching for each particle. Humanoid robots face additional…

Iterative learning control (ILC) is capable of improving the tracking performance of repetitive control systems by utilizing data from past iterations. The aim of this paper is to achieve both task flexibility, which is often achieved by…

The intrinsic compliance and high degree of freedom (DoF) of redundant soft manipulators facilitate safe interaction and flexible task execution. However, effective kinematic control remains highly challenging, as it must handle…

This paper explores the energy efficiency (EE) of integrated sensing and communication (ISAC) systems employing massive multiple-input multiple-output (mMIMO) techniques to leverage spatial beamforming gains for both communication and…

This paper proposes a novel framework for humanoid robots to execute inspection tasks with high efficiency and millimeter-level precision. The approach combines hierarchical planning, time-optimal standing position generation, and…

The ability to reflect on and correct failures is crucial for robotic systems to interact stably with real-life objects.Observing the generalization and reasoning capabilities of Multimodal Large Language Models (MLLMs), previous approaches…

Model Predictive Control (MPC) approaches are widely used in robotics, since they guarantee feasibility and allow the computation of updated trajectories while the robot is moving. They generally require heuristic references for the…

[Accepted to IROS 2025] In this paper, we address the problem of tracking high-speed agile trajectories for Unmanned Aerial Vehicles(UAVs), where model inaccuracies can lead to large tracking errors. Existing Nonlinear Model Predictive…

Underactuated systems like sea vessels have degrees of motion that are insufficiently matched by a set of independent actuation forces. In addition, the underlying trajectory-tracking control problems grow in complexity in order to decide…

When inverse kinematics (IK) is adopted to control robotic arms in manipulation tasks, there is often a discrepancy between the end effector (EE) position of the robot model in the simulator and the physical EE in reality. In most robotic…

This paper presents MEMROC (Multi-Eye to Mobile RObot Calibration), a novel motion-based calibration method that simplifies the process of accurately calibrating multiple cameras relative to a mobile robot's reference frame. MEMROC utilizes…

A Learning Model Predictive Controller (LMPC) for linear system in presented. The proposed controller is an extension of the LMPC [1] and it aims to decrease the computational burden. The control scheme is reference-free and is able to…

In this paper, we investigate continual learning performance metrics used in class incremental learning strategies for continual learning (CL) using some high performing methods. We investigate especially mean task accuracy. First, we show…

Integrated sensing and communications (ISAC) enabled by unmanned aerial vehicles (UAVs) is a promising technology to facilitate target tracking applications. In contrast to conventional UAV-based ISAC system designs that mainly focus on…

With the widespread application of industrial robots, the problem of absolute positioning accuracy becomes increasingly prominent. To ensure the working state of the robots, researchers commonly adopt calibration techniques to improve its…

In-Context Learning (ICL) is suffering from unsatisfactory performance and under-calibration due to high prior bias and unfaithful confidence. Some previous works fine-tuned language models for better ICL performance with enormous datasets…

Optimal MIMO detection has been one of the most challenging and computationally inefficient tasks in wireless systems. We show that the new analog computing techniques like Coherent Ising Machines (CIM) are promising candidates for…

Thanks to recent advancements in accelerating non-linear model predictive control (NMPC), it is now feasible to deploy whole-body NMPC at real-time rates for humanoid robots. However, enforcing inequality constraints in real time for such…

The ability to flexibly leverage limbs for loco-manipulation is essential for enabling autonomous robots to operate in unstructured environments. Yet, prior work on loco-manipulation is often constrained to specific tasks or predetermined…