Learning latent actions to control assistive robots

Personalizing driver safety interfaces via driver cognitive factors inference

Recent advances in AI and intelligent vehicle technology hold the promise of revolutionizing mobility and transportation through advanced driver assistance systems (ADAS). Certain cognitive factors, such as impulsivity and inhibitory control have been shown to relate to risky driving behavior and on-road risk-taking. However, existing systems fail to leverage such factors in assistive driving technologies adequately. Varying the levels of these cognitive factors could influence the effectiveness and acceptance of ADAS interfaces. We demonstrate an approach for personalizing driver interaction via driver safety interfaces that are are triggered based on the inference of the driver's latent cognitive states from their driving behavior. To accomplish this, we adopt a data-driven approach and train a recurrent neural network to infer impulsivity and inhibitory control from recent driving behavior. The network is trained on a population of human drivers to infer impulsivity and inhibitory control from recent driving behavior. Using data collected from a high-fidelity vehicle motion simulator experiment, we demonstrate the ability to deduce these factors from driver behavior. We then use these inferred factors to determine instantly whether or not to engage a driver safety interface. This approach was evaluated using leave-one-out cross validation using actual human data. Our evaluations reveal that our personalized driver safety interface that captures the cognitive profile of the driver is more effective in influencing driver behavior in yellow light zones by reducing their inclination to run through them.

Exploring Control Authority Preferences in Robotic Arm Assistance for Power Wheelchair Users

This paper uses mixed methods to explore the preliminary design of control authority preferences for an Assistive Robotic Manipulator (ARM). To familiarize users with an intelligent robotic arm, we perform two kitchen task iterations: one with user-initiated software autonomy (predefined autonomous actions) and one with manual control. Then, we introduce a third scenario, enabling users to choose between manual control and system delegation throughout the task. Results showed that, while manually switching modes and controlling the arm via joystick had a higher mental workload, participants still preferred full joystick control. Thematic analysis indicates manual control offered greater freedom and sense of accomplishment. Participants reacted positively to the idea of an interactive assistive system. Users did not want to ask the system to only assist, by taking over for certain actions, but also asked for situational feedback (e.g., 'How close am I (the gripper)?', 'Is the lid centered over the jug?'). This speaks to a future assistive system that ensures the user feels like they drive the system for the entirety of the task and provides action collaboration in addition to more granular situational awareness feedback.

The role of collaborative robotics in assistive and rehabilitation applications

Collaborative robotics principles and advancements may transform the field of assistive and rehabilitation robotics.

A Novel Framework for Mixed Reality-Based Control of Collaborative Robot: Development Study

BACKGROUND

Applications of robotics in daily life are becoming essential by creating new possibilities in different fields, especially in the collaborative environment. The potentials of collaborative robots are tremendous as they can work in the same workspace as humans. A framework employing a top-notch technology for collaborative robots will surely be worthwhile for further research.

OBJECTIVE

This study aims to present the development of a novel framework for the collaborative robot using mixed reality.

METHODS

The framework uses Unity and Unity Hub as a cross-platform gaming engine and project management tool to design the mixed reality interface and digital twin. It also uses the Windows Mixed Reality platform to show digital materials on holographic display and the Azure mixed reality services to capture and expose digital information. Eventually, it uses a holographic device (HoloLens 2) to execute the mixed reality-based collaborative system.

RESULTS

A thorough experiment was conducted to validate the novel framework for mixed reality-based control of a collaborative robot. This framework was successfully applied to implement a collaborative system using a 5-degree of freedom robot (xArm-5) in a mixed reality environment. The framework was stable and worked smoothly throughout the collaborative session. Due to the distributed nature of cloud applications, there is a negligible latency between giving a command and the execution of the physical collaborative robot.

CONCLUSIONS

Opportunities for collaborative robots in telerehabilitation and teleoperation are vital as in any other field. The proposed framework was successfully applied in a collaborative session, and it can also be applied in other similar potential applications for robust and more promising performance.