Our goals in this research project are to determine the significance of performance of inanimate tasks as a marker for robotic proficiency and assess the utility of inanimate task training on robotic skill performance.  We aim to establish standardized tasks for training, define accurate metrics for performance, and assess motor skill acquisition in virtual and real environments.

Robot-assisted surgery offers distinct advantages and is rapidly being applied to a diverse range of surgical procedures.  However, teleoperation inherently decouples the surgeon from the patient.  While robotic-assistance permits a more natural, intuitive interface in comparison to standard laparoscopy, there is still a significant learning curve in mastering the technique.  In addition, the advantages of the robotic system are further limited by the lack of tactile and kinesthetic information transmitted to the surgeon.  Given this lack of sensory feedback, more emphasis is placed on interpreting visual cues and understanding robotic movement during performance.

Vibrating muscle tendons at a range of frequencies is known to produce movement illusions in human subjects. Although there are examples in the literature on the use of vibrators to transmit simple cues such as direction information, movement illusions due to vibration have not been utilized as a method of providing illusory kinesthetic feedback. One possible main application is artificial proprioception for prosthetic devices.

Although it is relatively easy to induce the illusion, it is difficult to generate controlled sensations due to the inconsistency and instability of the illusion, differences observed among subjects, muscle configuration, and load conditions, among other reasons.

The implementation of training virtual environments (TVEs) is intended to reduce risk, improve and accelerate learning over traditional training methods, thereby transferring what is learned in the simulation to the targeted real world task. One type of TVE employs a type of robotic force feedback, also called haptic guidance, to assist the human trainee in performing the critical components of the task. Prior work suggests that these haptic guidance schemes perform best when the level of guidance is based on the trainee's changing level of performance during training. Our objective is to demonstrate that expert based progressive haptic guidance can accelerate and improve training outcomes over visual or practice-only methods. To that end, we design a guidance scheme based on a detailed analysis of performance differences between expert and novice trainees. The guidance design is then tested with two trainees in a dynamic task experiment thereby verifying its functionality.

The task of preparing samples for electron cryomicroscopy is a tedious and repetitive one, and well-suited to robotic automation.  In coordination with Baylor College of Medicine, we are using an Adept Cobra SCARA-type industrial robot to automate the process, from the retrieval of sample grids through to the storage of the vitrified samples.