GelSight sensor has been used to study microgeometry of objects since 2009 in tactile sensing applications. Elastomer, reflective coating, lighting, and camera were the main challenges of making a GelSight sensor within a short period. The recent addition of permanent markers to the GelSight was a new era in shear/slip studies. In our previous studies, we introduced Ultraviolet (UV) ink and UV LEDs as a new form of marker and lighting respectively. UV ink markers are invisible using ordinary LED but can be made visible using UV LED. Currently, recognition of objects or surface textures using GelSight sensor is done using fusion of camera-only images and GelSight captured images with permanent markings. Those images are fed to Convolutional Neural Networks (CNN) to classify objects. However, our novel approach in using low-cost GelSight sensor with UV markings, the 3D height map to 2D image conversion, and the additional non-Gelsight captured images for training the CNN can be eliminated. AlexNet and optical flow algorithm have been used for feature recognition of five coins without UV markings and shear/slip of the coin in GelSight with UV markings respectively. Our results on confusion matrix show that, on average coin recognition can reach 93.4% without UV markings using AlexNet. Therefore, our novel method of using GelSight with UV markings would be useful to recognize full/partial object, shear/slip, and force applied to the objects without any 3D image reconstruction.

This paper presents salient features of human-human interaction where one person with limited auditory and visual perception of the environment (a follower) is guided by an agent with full perceptual capabilities (a guider) via a hard rein along a given path. We investigate several salient features of the interaction between the guider and follower such as: 1) the order of an autoregressive (AR) control policy that maps states of the follower to actions of the guider; 2) how the guider may modulate the pulling force in response to the trust level of the follower; and 3) how learning may successively apportion the responsibility of control across different muscles of the guider. Based on experimental systems identification on human demonstrations from ten pairs of naive subjects, we show that guiders tend to adopt a third-order AR predictive control policy and followers tend to adopt second-order reactive control policy. Moreover, the extracted guider's control policy was implemented and validated by human-robot interaction experiments. By modeling the follower's dynamics with a time varying virtual damped inertial system, we found that it is the coefficient of virtual damping which is most sensitive to the trust level of the follower. We used these experimental insights to derive a novel controller that integrates an optimal order control policy with a push/pull force modulator in response to the trust level of the follower monitored using a time varying virtual damped inertial model.

In the case of human intervention in disaster response operations like indoor firefighting, where the environment perception is limited due to thick smoke, noise in the oxygen masks and clutter, not only limit the environmental perception of the human responders, but also causes distress. An intelligent agent (man/machine) with full environment perceptual capabilities is an alternative to enhance navigation in such unfavorable environments. Since haptic communication is the least affected mode of communication in such cases, we consider human demonstrations to use a hard rein to guide blindfolded followers with auditory distraction to be a good paradigm to extract salient features of guiding using hard reins. Based on numerical simulations and experimental systems identification based on demonstrations from eight pairs of human subjects, we show that, the relationship between the orientation difference between the follower and the guider, and the lateral swing patterns of the hard rein by the guider can be explained by a novel 3rdorder auto regressive predictive controller. Moreover, by modeling the two party voluntary movement dynamics using a virtual damped inertial model, we were able to model the mutual trust between two parties. In the future, the novel controller extracted based on human demonstrations can be tested on a human-robot interaction scenario to guide a visually impaired person in various applications like fire fighting, search and rescue, medical surgery, etc.

The aim of this paper is to improve the optimality and accuracy of techniques to guide a human in limited visibility and auditory conditions such as in fire-fighting in warehouses or similar environments. At present, breathing apparatus (BA) wearing fire-fighters move in teams following walls. Due to limited visibility and high noise in the oxygen masks, they predominantly depend on haptic communication through reins. An intelligent agent (man/machine) with full environment perceptual capabilities is an alternative to enhance navigation in such unfavorable environments, just like a dog guiding a blind person. This paper proposes an optimal state-dependent control policy to guide a follower with limited environmental perception, by an intelligent and environmentally perceptive agent. Based on experimental systems identification and numerical simulations on human demonstrations from eight pairs of participants, we show that the guiding agent and the follower experience learning for a optimal stable state-dependent novel 3rd and 2nd order auto regressive predictive and reactive control policies respectively. Our findings provide a novel theoretical basis to design advanced human-robot interaction algorithms in a variety of cases that require the assistance of a robot to perceive the environment by a human counterpart.