Hamed Rahimi Nohooji

Adaptive attention allocation in dynamic social environments remains a fundamental challenge for autonomous robots, requiring the integration of perceptual saliency, social context, and real-time decision-making.We present a bio-inspired reinforcement learning framework for robotic gaze control that incorporates a habituation mechanism to regulate the exploration–exploitation trade-off, mirroring how biological attention systems filter redundant stimuli whilst remaining responsive to novel events. Through a comprehensive ablation study comparing Deep Q-Learning (DQL), Vanilla Q-Learning (VQL), and Multi-Objective Q-Learning (MOL), we uncover a critical insight: habituation significantly enhances DQL performance, improving response efficiency and policy stability, yet causes systematic degradation in MOL due to fundamental incompatibilities between fixed-threshold resets and the extended episodes required for multi-objective optimisation. This differential effect reveals that bio-inspired mechanisms cannot be applied universally across learning architectures but must be carefully matched to algorithmic characteristics. Real-world deployment on the ARI humanoid robot validates the framework’s practical applicability, achieving robust gaze prediction accuracy across diverse interaction scenarios with well-calibrated confidence metrics that reliably distinguish correct from incorrect predictions. Our findings provide evidence-based guidelines for integrating biological principles into cognitive robotics, demonstrating both the promise and the pitfalls of bio-inspired mechanism design.

Space exploration demands innovative robotic solutions to address complex challenges. This article provides a forward‐looking perspective on the emerging field of compliant robotics for space applications, categorizing these systems into reconfigurable, hyper‐redundant, origami‐inspired, and soft robots, each offering unique advantages and facing distinct challenges. The review explores in‐depth the critical roles these compliant robots can assume, ranging from on‐orbit servicing to planetary exploration and beyond. It also addresses material selection, accounting for the harsh conditions of space, and examines the complexities in design, actuation, sensing, and control. The article concludes with a future‐focused discussion of emerging trends, challenges, and research directions. This review aims to offer a comprehensive understanding of the current state of the art, positioning compliant robotics as a transformative force in the next frontier of space exploration.

This paper presents a reinforcement learning-based neuroadaptive control framework for robotic manipulators operating under deferred constraints. The proposed approach improves traditional barrier Lyapunov functions by introducing a smooth constraint enforcement mechanism that offers two key advantages: (i) it minimizes control effort in unconstrained regions and progressively increases it near constraints, improving energy efficiency, and (ii) it enables gradual constraint activation through a prescribed-time shifting function, allowing safe operation even when initial conditions violate constraints. To address system uncertainties and improve adaptability, an actor-critic reinforcement learning framework is employed. The critic network estimates the value function, while the actor network learns an optimal control policy in real time, enabling adaptive constraint handling without requiring explicit system modeling. Lyapunov-based stability analysis guarantees the boundedness of all closed-loop signals. The effectiveness of the proposed method is validated through numerical simulations.

This study presents a novel framework that integrates the universal jamming gripper (UG) with unmanned aerial vehicles (UAVs) to enable automated grasping with no human operator in the loop. Grounded in the principles of granular jamming, the UG exhibits remarkable adaptability and proficiency, navigating the complexities of soft aerial grasping with enhanced robustness and versatility. Central to this integration is a uniquely formulated constrained trajectory optimization using model predictive control, coupled with a robust force control strategy, increasing the level of automation and operational reliability in aerial grasping. This control structure, while simple, is a powerful tool for various applications, ranging from material handling to disaster response, and marks an advancement toward genuine autonomy in aerial manipulation tasks. The key contribution of this research is the combination of a UG with a suitable control strategy, that can be kept relatively straightforward thanks to the mechanical intelligence built into the UG. The algorithm is validated through numerical simulations and virtual experiments.

This paper introduces a novel Nussbaum function-based PID control for robotic manipulators. The integration of the Nussbaum function into the PID framework provides a solution with a simple structure that effectively tackles the challenge of unknown control directions. Stability is achieved through a combination of neural network-based estimation and Lyapunov analysis, facilitating automatic gain adjustment without the need for system dynamics. Our approach offers a gain determination with minimum parameter requirements, significantly reducing the complexity and enhancing the efficiency of robotic manipulator control. The paper guarantees that all signals within the closed-loop system remain bounded. Lastly, numerical simulations validate the theoretical framework, confirming the effectiveness of the proposed control strategy in enhancing robotic manipulator control.