The collaborative robot market is flourishing as there is a trend towards simplification, modularity, and increased flexibility on the production line. But when humans and robots are collaborating in a shared environment, the safety of humans should be a priority. We introduce a novel wearable robotic system to enhance safety during Human-Robot Interaction (HRI). The proposed wearable robot is designed to hold a fiducial marker and maintain its visibility to a motion capture system, which, in turn, localizes the user's hand with good accuracy and low latency and provides vibrotactile feedback to the user's wrist. The vibrotactile feedback guides the user's hand movement during collaborative tasks in order to increase safety and enhance collaboration efficiency. A user study was conducted to assess the recognition and discriminability of ten designed vibration patterns applied to the upper (dorsal) and the down (volar) parts of the user's wrist. The results show that the pattern recognition rate on the volar side was higher, with an average of 75.64% among all users. Four patterns with a high recognition rate were chosen to be incorporated into our system. A second experiment was carried out to evaluate users' response to the chosen patterns in real-world collaborative tasks. Results show that all participants responded to the patterns correctly, and the average response time for the patterns was between 0.24 and 2.41 seconds.

Abstract-- This research introduces the Bi-VLA (Vision-Language-Action) model, a novel system designed for bimanual robotic dexterous manipulation that seamlessly integrates vision for scene understanding, language comprehension for translating human instructions into executable code, and physical action generation. We evaluated the system's functionality through a series of household tasks, including the preparation of a desired salad upon human request. Bi-VLA demonstrates the ability to interpret complex human instructions, perceive and understand the visual context of ingredients, and execute precise bimanual actions to prepare the requested salad. We assessed the system's performance in terms of accuracy, efficiency, and adaptability to different salad recipes and human preferences through a series of experiments. Our results show a 100% success rate in generating the correct executable code by the Language Module, a 96.06% success rate in detecting specific ingredients by the Vision Module, and an overall success rate of 83.4% in However, despite their potential, the application of language models Recent advancements in language models have significantly to synthesize the bimanual skills of robots has not received impacted Human-Robot Interaction (HRI), enabling significant attention.

In cognitive robotics, the scientific community recognized the high generalization capability of these large models as a key to developing a robot that could perform new tasks based on generalized knowledge derived from familiar actions expressed in natural language. However, efforts to apply LLMs in robotics faced challenges, particularly in understanding and processing the external world. Previous attempts to convey the model's understanding of the world through text-only approaches [1], [20], [8] struggled with ambiguities and the assumption of static objects unless interacted with. The introduction of multi-modal transformer-based models such as GPT-4 [16] and Gemini [18], capable of processing images, opened up new possibilities for robotics [5], allowing robots to comprehend their environment and enhancing their'Embodied Experience' [15]. Cognitive robots have been developed on various platforms, ranging from mobile manipulators [5], [3] to bio-inspired humanoid robots [21] and quadrupedal robots [6]. In the latter, cognitive abilities were developed using an'Inner Monologue' approach [10], with improvements inspired by the'Autogen' concept [25]. The cognition of the robot is facilitated through internal communication between agent models, leveraging their strengths to provide different cognitive capabilities to the system.

The current capabilities of robotic systems make human collaboration necessary to accomplish complex tasks effectively. In this work, we are introducing a framework to ensure safety in a human-robot collaborative environment. The system is composed of a wearable 2-DOF robot, a low-cost and easy-to-install tracking system, and a collision avoidance algorithm based on the Artificial Potential Field (APF). The wearable robot is designed to hold a fiducial marker and maintain its visibility to the tracking system, which, in turn, localizes the user's hand with good accuracy and low latency and provides haptic feedback to the user. The system is designed to enhance the performance of collaborative tasks while ensuring user safety. Three experiments were carried out to evaluate the performance of the proposed system. The first one evaluated the accuracy of the tracking system. The second experiment analyzed human-robot behavior during an imminent collision. The third experiment evaluated the system in a collaborative activity in a shared working environment. The results show that the implementation of the introduced system reduces the operation time by 16% and increases the average distance between the user's hand and the robot by 5 cm.