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Haptics devices have been developed in a wide range of form factors, actuation methods, and degrees of freedom, often with the goal of communicating information. While work has investigated the maximum rate and quantity of information that can be transferred through haptics, these measures often do not inform how humans will use the devices. In this work, we measure the differences between perception and use as it relates to signal complexity. Using an inflatable soft haptic display with four independently actuated pouches, we provide navigation directions to participants. The haptic device operates in three modalities, in increasing order of signal complexity: Cardinal, Ordinal, and Continuous. We first measure participants’ accuracy in perceiving continuous signals generated by the device, showing average errors below 5 deg. Participants then used the haptic device in each operating mode to guide an object towards a target in a 2-dimensional plane. Our results indicate that human’s use of haptic signals often lags significantly behind the displayed signal and is less accurate than their static perception. Additionally signal complexity was correlated with path efficiency but inversely correlated with movement speed, showing that even simple design changes create complex tradeoffs. 

Physical interaction between humans and robots can help robots learn to perform complex tasks. The robot arm gains information by observing how the human kinesthetically guides it throughout the task. While prior works focus on how the robot learns, it is equally important that this learning is transparent to the human teacher. Visual displays that show the robot’s uncertainty can potentially communicate this information; however, we hypothesize that visual feedback mechanisms miss out on the physical connection between the human and robot. In this work we present a soft haptic display that wraps around and conforms to the surface of a robot arm, adding a haptic signal at an existing point of contact without significantly affecting the interaction. We demonstrate how soft actuation creates a salient haptic signal while still allowing flexibility in device mounting. Using a psychophysics experiment, we show that users can accurately distinguish inflation levels of the wrapped display with an average Weber fraction of 11.4%. When we place the wrapped display around the arm of a robotic manipulator, users are able to interpret and leverage the haptic signal in sample robot learning tasks, improving identification of areas where the robot needs more training and enabling the user to provide better demonstrations. See videos of our device and user studies here: https://youtu.be/tX-2Tqeb9Nw 

Navigation and motion control of a robot to a destination are tasks that have historically been performed with the assumption that contact with the environment is harmful. This makes sense for rigid-bodied robots, where obstacle collisions are fundamentally dangerous. However, because many soft robots have bodies that are low-inertia and compliant, obstacle contact is inherently safe. As a result, constraining paths of the robot to not interact with the environment is not necessary and may be limiting. In this article, we mathematically formalize interactions of a soft growing robot with a planar environment in an empirical kinematic model. Using this interaction model, we develop a method to plan paths for the robot to a destination. Rather than avoiding contact with the environment, the planner exploits obstacle contact when beneficial for navigation. We find that a planner that takes into account and capitalizes on environmental contact produces paths that are more robust to uncertainty than a planner that avoids all obstacle contact. 

Pneumatically operated soft growing robots that extend via tip eversion are well-suited for navigation in confined spaces. Adding the ability to interact with the environment using sensors and tools attached to the robot tip would greatly enhance the usefulness of these robots for exploration in the field. However, because the material at the tip of the robot body continually changes as the robot grows and retracts, it is challenging to keep sensors and tools attached to the robot tip during actuation and environment interaction. In this paper, we analyze previous designs for mounting to the tip of soft growing robots, and we present a novel device that successfully remains attached to the robot tip while providing a mounting point for sensors and tools. Our tip mount incorporates and builds on our previous work on a device to retract the robot without undesired buckling of its body. Using our tip mount, we demonstrate two new soft growing robot capabilities: (1) pulling on the environment while retracting, and (2) retrieving and delivering objects. Finally, we discuss the limitations of our design and opportunities for improvement in future soft growing robot tip mounts. 

Soft continuum manipulators provide a safe alternative to traditional rigid manipulators, because their bodies can absorb and distribute contact forces. Soft manipulators have near infinite potential degrees of freedom, but a limited number of control inputs. This underactuation means soft continuum manipulators often lack either the controllability or the dexterity to achieve desired tasks. In this work, we present an extension of McKibben actuators, which have well-known models, that increases the controllable degrees of freedom using active reconfiguration of the constraining fibers. These Active Fiber Reinforced Elastomeric Enclosures (AFREEs) preform some combination of length change and twisting, depending on the fiber configuration. Experimental results shows that by changing the fiber angles within a range of -30 to 30 degrees and actuating the resulting configuration between 10.3 kPa and 24.1 kPa, we can achieve twists between ± 60 degrees and displacements between -2 and 4 mm. By additionally controlling the fiber lengths and pressure, we can modify the AFREE kinematics further, creating dynamic behaviors and trajectories of actuation. The presented actuator creates the possibility to reconFigure actuator kinematics to meet desired soft robot motions.