The goal of this study was to look at touch and dance from different views to gain a better perspective on the benefits of touch, mainly when used in dance and also perhaps in broader contexts. Part of this investigation also looked at the stigmatized view of touch in the…
The goal of this study was to look at touch and dance from different views to gain a better perspective on the benefits of touch, mainly when used in dance and also perhaps in broader contexts. Part of this investigation also looked at the stigmatized view of touch in the American culture and in turn the lack of knowledge about, and comfort with touch in our society. A personal research component involved the creation of a solo reflecting about the question of why I connect with touch so intensely. The bulk of the study involved facilitating touch experiences in two introductory level dance classes for high school students. Daily journal entries were collected from each of the eighty students that focused on their personal experiences with touch in a series of six movement sessions. The study shows that bringing touch to the dance classroom has multiple benefits, including promoting a greater understanding and acceptance of the sense of touch, a positive impact on students' views about dance, and a break down of preconceived notions about the mind and the body.
Robotic systems are outmatched by the abilities of the human hand to perceive and manipulate the world. Human hands are able to physically interact with the world to perceive, learn, and act to accomplish tasks. Limitations of robotic systems to interact with and manipulate the world diminish their usefulness. In…
Robotic systems are outmatched by the abilities of the human hand to perceive and manipulate the world. Human hands are able to physically interact with the world to perceive, learn, and act to accomplish tasks. Limitations of robotic systems to interact with and manipulate the world diminish their usefulness. In order to advance robot end effectors, specifically artificial hands, rich multimodal tactile sensing is needed. In this work, a multi-articulating, anthropomorphic robot testbed was developed for investigating tactile sensory stimuli during finger-object interactions. The artificial finger is controlled by a tendon-driven remote actuation system that allows for modular control of any tendon-driven end effector and capabilities for both speed and strength. The artificial proprioception system enables direct measurement of joint angles and tendon tensions while temperature, vibration, and skin deformation are provided by a multimodal tactile sensor. Next, attention was focused on real-time artificial perception for decision-making. A robotic system needs to perceive its environment in order to make decisions. Specific actions such as “exploratory procedures” can be employed to classify and characterize object features. Prior work on offline perception was extended to develop an anytime predictive model that returns the probability of having touched a specific feature of an object based on minimally processed sensor data. Developing models for anytime classification of features facilitates real-time action-perception loops. Finally, by combining real-time action-perception with reinforcement learning, a policy was learned to complete a functional contour-following task: closing a deformable ziplock bag. The approach relies only on proprioceptive and localized tactile data. A Contextual Multi-Armed Bandit (C-MAB) reinforcement learning algorithm was implemented to maximize cumulative rewards within a finite time period by balancing exploration versus exploitation of the action space. Performance of the C-MAB learner was compared to a benchmark Q-learner that eventually returns the optimal policy. To assess robustness and generalizability, the learned policy was tested on variations of the original contour-following task. The work presented contributes to the full range of tools necessary to advance the abilities of artificial hands with respect to dexterity, perception, decision-making, and learning.
