Publications
Journals & Conference Proceedings
Yagüe, M. P., García, J. E. S., & Penas, M. S. (2026). Human-intelligent trajectory optimization for robotic manipulators with hybrid PSO-PS algorithm. Advanced Engineering Informatics, 69, 103941.
Abstract: Industry 5.0 is driving a new era in industrial automation, where the collaboration between artificial intelligence (AI) and human supervision enables the development of smarter, more adaptive, and more efficient systems. Robotic trajectory generation is a clear example of this new paradigm. Metaheuristic techniques help automatically generate optimized trajectories, thereby improving operational efficiency. However, optimizing trajectories using AI alone also presents limitations. Starting from random trajectories, the optimization process becomes computationally expensive, especially in complex environments. In this context, initial input from human experts plays a crucial role: expert-defined trajectories provide structured, feasible, and contextual starting points that guide AI more effectively toward high-quality solutions. Therefore, this work proposes a novel human-guided trajectory optimization algorithm. In this way, human knowledge, Particle Swarm Optimization (PSO), and Pattern Search (PS) are efficiently combined. The results demonstrate that this approach significantly improves robotic system performance, achieving cycle time reductions of up to 16.69% compared to expert-defined trajectories. This approach establishes a solid framework for intelligent automation in Industry 5.0, promoting the development of more efficient, sustainable, and adaptive robotic systems.
Peñacoba-Yagüe, M., & Sierra-García, J. E. (2026). Generalized Design Methodology for Dual-Arm Robotic Platforms: From Conceptualization to Experimental Validation Within the MANiBOT Framework. Machines, 14(1), 74.
Abstract: This work proposes a general methodology for the design and experimental validation of dual-arm robotic platforms intended for intelligent manipulation tasks in real-world environments. The proposed framework formalizes the complete engineering process, from the definition of functional requirements to the structural validation of the final prototype, ensuring reproducibility and adaptability across different applications. The methodology is organized into five main stages: (i) requirement analysis and context characterization; (ii) conceptual architecture definition; (iii) detailed mechanical design and structural analysis; (iv) prototype construction and integration; and (v) experimental validation and iterative refinement. Each stage defines its expected deliverables, evaluation metrics, and decision criteria to support systematic design progression. The approach is demonstrated through its implementation within the European project MANiBOT, where the framework guided the development of a modular bimanual robotic platform capable of integrating collaborative manipulators and conveyor subsystems for dual-arm manipulation. Structural testing, deflection measurements, and stability analyses confirmed the robustness and safety of the resulting design. Beyond this specific case, the proposed methodology provides a replicable and extensible design reference for research and industrial teams developing modular robotic structures, supporting the standardization of engineering practices in bimanual mobile robotics.
M. Kiatos, L. Koutras, I. Sarantopoulos and Z. Doulgeri, “Learning a Pre-Grasp Manipulation Policy to Effectively Retrieve a Target in Dense Clutter,” 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Abu Dhabi, United Arab Emirates, 2024, pp. 7543-7549.
Abstract: Robotic grasping of a target object in cluttered environments poses considerable challenges, often due to limited collision-free grasp affordances caused by the close proximity of other objects. To overcome this limitation, non-prehensile actions like pushing can be strategically employed to manipulate the environment and improve the chances of successful grasps. In this paper, we introduce a novel pre-grasp manipulation policy designed to efficiently retrieve a target object from dense clutter by leveraging pushing actions and considering the gripper’s kinematic capabilities to strategically position the target object within the gripper’s closing region for a secure grasp. Unlike conventional approaches, our policy incorporates sequential pushing, allowing the robot to make decisions while within the camera’s field of view without retracting to a home position, leading to significantly reduced execution time per action. Our policy, trained in simulation, seamlessly transfers to real-world scenarios. Extensive experimental evaluation demonstrates superior performance, faster completion times, and robust generalization to unseen objects compared to existing baselines.
L. Koutras, I. Ntoliou and Z. Doulgeri, “Towards Passivity Based Nonprehensile Bimanual Manipulation of Large Objects,” 2023 IEEE-RAS 22nd International Conference on Humanoid Robots (Humanoids), Austin, TX, USA, 2023, pp. 1-8.
Abstract: In this work, a passivity-based control scheme to manipulate large objects supported on planar surfaces by bimanual robots is introduced. A control input is designed to accomplish the rotation and displacement of the object to a desired pose without assuming any knowledge of the object dynamics and friction dynamics between the object and the supporting surface. Passivity arguments are used to rigorously prove its stability. The proposed method is evaluated in simulations with friction forces simulated using the elastoplastic model. Experimental results performed on a bimanual robot with two 7-dof KUKA LBR iiwa 7 arms manipulating a variety of large objects verify the theoretical results.
L. Koutras, S. Stavridis, C. Papakonstantinou and Z. Doulgeri, “Robotic Shelf Replenishment by Combining Non-Prehensile Object Manipulation with Simple Grasping,” 2025 IEEE-RAS 24th International Conference on Humanoid Robots (Humanoids), Seoul, Korea, Republic of, 2025, pp. 1-8.
Abstract: In this work, the problem of robotic shelf replenishment is being studied. Such tasks involve a variety of object types and geometries, which should be picked from boxes where they are tightly packed and placed on shelves in tight formations with the appropriate orientation. We consider a bimanual robotic set up with a parallel finger gripper and a 3Dprinted end-effector and propose to combine simple grasping with a set of non-prehensile manipulations to achieve such a replenishment task. This work reports on the details of the implementation of the proposed strategy and on the initial investigation of the feasibility and effectiveness of the proposed solution for a representative set of super-market products, demonstrated in a number of experiments in the lab.
S. Jauhri, I. Lunawat, and G. Chalvatzaki, “Learning Any-View 6DoF Robotic Grasping in Cluttered Scenes via Neural Surface Rendering,” Robotics: Science and Systems 2024, Delft, Netherlands, July 15-July 19, 2024.
Abstract: A significant challenge for real-world robotic manipulation is the effective 6DoF grasping of objects in cluttered scenes from any single viewpoint without needing additional scene exploration. This work re-interprets grasping as rendering and introduces NeuGraspNet, a novel method for 6DoF grasp detection that leverages advances in neural volumetric representations and surface rendering. We encode the interaction between a robot’s end-effector and an object’s surface by jointly learning to render the local object surface and learning grasping functions in a shared feature space. Our approach uses global (scene-level) features for grasp generation and local (grasp-level) neural surface features for grasp evaluation. This enables effective, fully implicit 6DoF grasp quality prediction, even in partially observed scenes. NeuGraspNet operates on random viewpoints, common in mobile manipulation scenarios, and outperforms existing implicit and semi-implicit grasping methods. We demonstrate the real-world applicability of the method with a mobile manipulator robot, grasping in open cluttered spaces.
L. N. Bikas and G. A. Rovithakis, “Discrete-time Prescribed Performance Control and Maximum Allowable Transmission Interval,” 2024 European Control Conference (ECC), Stockholm, Sweden, 2024, pp. 1159-1164.
Abstract: In this paper, we consider a discrete-time implementation of prescribed performance control (PPC), focusing on its robustness and operability. Specifically, given a prescribed performance controller that guarantees prescribed performance attributes, in terms of maximum overshoot, minimum convergence rate and maximum steady-state error, when operating in continuous-time, the task is to derive sufficient conditions on the maximum allowable transmission interval to enable PPC to preserve its performance characteristics. Interestingly, the maximum allowable transmission interval is directly related with the performance achieved at steady-state. Simulations clarify and verify the theoretical findings.
T. A. Aforozi and G. A. Rovithakis, “Event-Triggered Prescribed Performance Control for SISO Uncertain Nonlinear Systems in Brunovsky Canonical Form,” 2024 European Control Conference (ECC), Stockholm, Sweden, 2024, pp. 1177-1182.
Abstract: In this work, we consider the problem of designing tracking controllers for SISO uncertain high relative degree systems in Brunovsky canonical form in the presence of non-periodic communication. The proposed control scheme is static, and requires no hard calculations, analytic or numerical, to produce the control signal. Event-triggered mechanisms are considered in both sensor-to-controller and controller-to-actuator channels, yet the enforcement of prescribed performance bounds in terms of steady-state accuracy and convergence rate is ensured. No prior knowledge or estimation structure regarding system nonlinearities are required and no high-order derivatives of the desired output trajectories are incorporated in the controller design. Simulation results clarify and verify the theoretical findings.
V. Prasad, A. Kshirsagar, D. Koert, R. Stock-Homburg, J. Peters and G. Chalvatzaki, “MoVEInt: Mixture of Variational Experts for Learning Human–Robot Interactions From Demonstrations,” in IEEE Robotics and Automation Letters, vol. 9, no. 7, pp. 6043-6050, July 2024.
Abstract: Shared dynamics models are important for capturing the complexity and variability inherent in Human-Robot Interaction (HRI). Therefore, learning such shared dynamics models can enhance coordination and adaptability to enable successful reactive interactions with a human partner. In this work, we propose a novel approach for learning a shared latent space representation for HRIs from demonstrations in a Mixture of Experts fashion for reactively generating robot actions from human observations. We train a Variational Autoencoder (VAE) to learn robot motions regularized using an informative latent space prior that captures the multimodality of the human observations via a Mixture Density Network (MDN). We show how our formulation derives from a Gaussian Mixture Regression formulation that is typically used approaches for learning HRI from demonstrations such as using an HMM/GMM for learning a joint distribution over the actions of the human and the robot. We further incorporate an additional regularization to prevent “mode collapse”, a common phenomenon when using latent space mixture models with VAEs. We find that our approach of using an informative MDN prior from human observations for a VAE generates more accurate robot motions compared to previous HMM-based or recurrent approaches of learning shared latent representations, which we validate on various HRI datasets involving interactions such as handshakes, fistbumps, waving, and handovers. Further experiments in a real-world human-to-robot handover scenario show the efficacy of our approach for generating successful interactions with four different human interaction partners.
Magazines, Press, & Media Coverage
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