Closed-Loop Verbal Reinforcement Learning for Task-Level Robotic Planning

We propose a new Verbal Reinforcement Learning (VRL) framework for interpretable task-level planning in mobile robotic systems operating under execution uncertainty. The framework follows a closed-loop architecture that enables iterative policy improvement through interaction with the physical environment. In our framework, executable Behavior Trees are repeatedly refined by a Large Language Model actor using structured natural-language feedback produced by a Vision-Language Model critic that observes the physical robot and execution traces. Unlike conventional reinforcement learning, policy updates in VRL occur directly at the symbolic planning level, without gradient-based optimization. This enables transparent reasoning, explicit causal feedback, and human-interpretable policy evolution. We validate the proposed framework on a real mobile robot performing a multi-stage manipulation and navigation task under execution uncertainty. Experimental results show that the framework supports explainable policy improvements, closed-loop adaptation to execution failures, and reliable deployment on physical robotic systems.

GuideTouch: An Obstacle Avoidance Device with Tactile Feedback for Visually Impaired

Safe navigation for the visually impaired individuals remains a critical challenge, especially concerning head-level obstacles, which traditional mobility aids often fail to detect. We introduce GuideTouch, a compact, affordable, standalone wearable device designed for autonomous obstacle avoidance. The system integrates two vertically aligned Time-of-Flight (ToF) sensors, enabling three-dimensional environmental perception, and four vibrotactile actuators that provide directional haptic feedback. Proximity and direction information is communicated via an intuitive 4-point vibrotactile feedback system located across the user's shoulders and upper chest. For real-world robustness, the device includes a unique centrifugal self-cleaning optical cover mechanism and a sound alarm system for location if the device is dropped. We evaluated the haptic perception accuracy across 22 participants (17 male and 5 female, aged 21-48, mean 25.7, sd 6.1). Statistical analysis confirmed a significant difference between the perception accuracy of different patterns. The system demonstrated high recognition accuracy, achieving an average of 92.9% for single and double motor (primary directional) patterns. Furthermore, preliminary experiments with 14 visually impaired users validated this interface, showing a recognition accuracy of 93.75% for primary directional cues. The results demonstrate that GuideTouch enables intuitive spatial perception and could significantly improve the safety, confidence, and autonomy of users with visual impairments during independent navigation.