Publications

Though it is common for robots to rely on vision for object feature estimation, there are environments where optical sensing performs poorly, due to occlusion, poor lighting or limited space for camera placement. Haptic sensing in robotics has a long history, but few approaches have combined this with within-hand-manipulation (WIHM), in order to expose more features of an object to the tactile sensing elements of the hand. As in the human hand, these sensing structures are generally non-homogenous in their coverage of a gripper's manipulation surfaces, as the sensitivity of some hand or finger regions is often different to other regions. In this work we use a modified version of the recently developed 2-finger Model VF (variable friction) robot gripper to acquire tactile information while rolling objects within the robot's grasp. This new gripper has one high-friction passive finger surface and one high-friction tactile sensing surface, equipped with 12 low-cost barometric force sensors encased in urethane. We have developed algorithms that use the data generated during these rolling actions to determine parametric aspects of the object under manipulation. Namely, two parameters are currently determined 1) the location of an object within the grasp 2) the object's shape (from three alternatives). The algorithms were first developed on a static test rig with passive object rolling and later evaluated with the robot gripper platform using active WIHM, which introduced artifacts into the data. With an object set consisting of 3 shapes and 5 sizes, an overall shape estimation accuracy was achieved of 88% and 78% for the test rig and hand respectively. Location estimation, of each object's centroid during motion, achieved a mean error of less than 2mm, along the 95mm length of the tactile sensing finger.

Background: The worldwide population of older adults will soon exceed the capacity of assisted living facilities. Accordingly, we aim to understand whether appropriately designed robots could help older adults stay active at home. Methods: Building on related literature as well as guidance from experts in game design, rehabilitation, and physical and occupational therapy, we developed eight human-robot exercise games for the Baxter Research Robot, six of which involve physical human-robot contact. After extensive iteration, these games were tested in an exploratory user study including 20 younger adult and 20 older adult users. Results: Only socially and physically interactive games fell in the highest ranges for pleasantness, enjoyment, engagement, cognitive challenge, and energy level. Our games successfully spanned three different physical, cognitive, and temporal challenge levels. User trust and confidence in Baxter increased significantly between pre- and post-study assessments. Older adults experienced higher exercise, energy, and engagement levels than younger adults, and women rated the robot more highly than men on several survey questions. Conclusions: The results indicate that social-physical exercise with a robot is more pleasant, enjoyable, engaging, cognitively challenging, and energetic than similar interactions that lack physical touch. In addition to this main finding, researchers working in similar areas can build on our design practices, our open-source resources, and the age-group and gender differences that we found.

When humans touch an object with their fingertips, they can immediately describe its tactile properties using haptic adjectives, such as hardness and roughness; however, human perception is subjective and noisy, with significant variation across individuals and interactions. Recent research has worked to provide robots with similar haptic intelligence but was focused on identifying binary haptic adjectives, ignoring both attribute intensity and perceptual variability. Combining ordinal haptic adjective labels gathered from human subjects for a set of 60 objects with features automatically extracted from raw multi-modal tactile data collected by a robot repeatedly touching the same objects, we designed a machine-learning method that incorporates partial knowledge of the distribution of object labels into training; then, from a single interaction, it predicts a probability distribution over the set of ordinal labels. In addition to analyzing the collected labels (10 basic haptic adjectives) and demonstrating the quality of our method's predictions, we hold out specific features to determine the influence of individual sensor modalities on the predictive performance for each adjective. Our results demonstrate the feasibility of modeling both the intensity and the variation of haptic perception, two crucial yet previously neglected components of human haptic perception.

Fingertip haptic feedback offers advantages in many applications, including robotic teleoperation, gaming, and training. However, fingertip size and shape vary significantly across humans, making it difficult to design fingertip interfaces and rendering techniques suitable for everyone. This article starts with an existing data-driven haptic rendering algorithm that ignores fingertip size, and it then develops two software-based approaches to personalize this algorithm for fingertips of different sizes using either additional data or geometry. We evaluate our algorithms in the rendering of pre-recorded tactile sensations onto rubber casts of six different fingertips as well as onto the real fingertips of 13 human participants. Results on the casts show that both approaches significantly improve performance, reducing force error magnitudes by an average of 78% with respect to the standard non-personalized rendering technique. Congruent results were obtained for real fingertips, with subjects rating each of the two personalized rendering techniques significantly better than the standard non-personalized method.

Future robots may need lighthearted physical interaction capabilities to connect with people in meaningful ways. To begin exploring how users perceive playful human–robot hand-to-hand interaction, we conducted a study with 20 participants. Each user played simple hand-clapping games with the Rethink Robotics Baxter Research Robot during a 1-h-long session involving 24 randomly ordered conditions that varied in facial reactivity, physical reactivity, arm stiffness, and clapping tempo. Survey data and experiment recordings demonstrate that this interaction is viable: all users successfully completed the experiment and mentioned enjoying at least one game without prompting. Hand-clapping tempo was highly salient to users, and human-like robot errors were more widely accepted than mechanical errors. Furthermore, perceptions of Baxter varied in the following statistically significant ways: facial reactivity increased the robot’s perceived pleasantness and energeticness; physical reactivity decreased pleasantness, energeticness, and dominance; higher arm stiffness increased safety and decreased dominance; and faster tempo increased energeticness and increased dominance. These findings can motivate and guide roboticists who want to design social–physical human–robot interactions.

Learning from demonstration (LfD) enables a robot to emulate natural human movement instead of merely executing preprogrammed behaviors. This article presents a hierarchical LfD structure of task-parameterized models for object movement tasks, which are ubiquitous in everyday life and could benefit from robotic support. Our approach uses the task-parameterized Gaussian mixture model (TP-GMM) algorithm to encode sets of demonstrations in separate models that each correspond to a different task situation. The robot then maximizes its expected performance in a new situation by either selecting a good existing model or requesting new demonstrations. Compared to a standard implementation that encodes all demonstrations together for all test situations, the proposed approach offers four advantages. First, a simply defined distance function can be used to estimate test performance by calculating the similarity between a test situation and the existing models. Second, the proposed approach can improve generalization, e.g., better satisfying the demonstrated task constraints and speeding up task execution. Third, because the hierarchical structure encodes each demonstrated situation individually, a wider range of task situations can be modeled in the same framework without deteriorating performance. Last, adding or removing demonstrations incurs low computational load, and thus, the robot’s skill library can be built incrementally. We first instantiate the proposed approach in a simulated task to validate these advantages. We then show that the advantages transfer to real hardware for a task where naive participants collaborated with a Willow Garage PR2 robot to move a handheld object. For most tested scenarios, our hierarchical method achieved significantly better task performance and subjective ratings than both a passive model with only gravity compensation and a single TP-GMM encoding all demonstrations.

Recently, a whole-body tactile sensing have emerged in robotics for safe human-robot interaction. A key issue in the whole-body tactile sensing is ensuring large-area manufacturability and high durability. To fulfill these requirements, a reconstruction method called electrical impedance tomography (EIT) was adopted in large-area tactile sensing. This method maps voltage measurements to conductivity distribution using only a few number of measurement electrodes. A common approach for the mapping is using a linearized model derived from the Maxwell's equation. This linearized model shows fast computation time and moderate robustness against measurement noise but reconstruction accuracy is limited. In this paper, we propose a novel nonlinear EIT algorithm through Deep Neural Network (DNN) approach to improve the reconstruction accuracy of EIT-based tactile sensors. The neural network architecture with rectified linear unit (ReLU) function ensured extremely low computational time (0.002 seconds) and nonlinear network structure which provides superior measurement accuracy. The DNN model was trained with dataset synthesized in simulation environment. To achieve the robustness against measurement noise, the training proceeded with additive Gaussian noise that estimated through actual measurement noise. For real sensor application, the trained DNN model was transferred to a conductive fabric-based soft tactile sensor. For validation, the reconstruction error and noise robustness were mainly compared using conventional linearized model and proposed approach in simulation environment. As a demonstration, the tactile sensor equipped with the trained DNN model is presented for a contact force estimation.

Implementing a whole-body tactile sensor is becoming a critical topic in robotics since physical contacts can occur at any location of the robot. Fabricating such a large-scale system typically requires complex electrical wiring to achieve high spatial resolution. Interestingly, biological skins have tactile hyperacuity, which is enabled by overlapping the receptive fields. This study introduces a fabric-based tactile sensor inspired by this biological feature. The tactile sensor injects electrical current into a pair of electrodes and measures the corresponding electrical potentials formed around the current pathway, which can be considered as a receptive field. When two or more neighboring pairs of electrodes are sampled, sensitive regions overlap in a way similar to the biological system. For the experiments, a fabric-based tactile sensor with only 24 electrodes in an area of 200 mm × 200 mm is developed. The sensor can localize point contact with an error of 8.13 mm, while the sensor’s minimum two-point discrimination distance is nearly 35 mm. This performance is comparable to that of the stomach region of human skin. This sensing approach could greatly simplify whole-body tactile skin development in the future.

During hugs, humans naturally provide and intuit subtle non-verbal cues that signify the desired strength and duration of an exchanged hug. Personal preferences for this close interaction may vary greatly between people; robots do not currently have the abilities to perceive or understand these preferences. This workshop paper discusses designing, building, and testing a novel inflatable chest that can simultaneously soften a robot and act as a tactile sensor to enable more natural and responsive hugging. Using PVC vinyl, two microphones, and two barometric pressure sensors, we created an inflatable two-chambered chest that forms the torso of a hugging robot. One chamber is located in the front of the robot, and the other chamber is in the back. While contacting HuggieChest in several ways common in hugs (start hug, rub, scratch, pat, squeeze, release), we recorded data from the two sensors in each chamber. The preliminary results suggest that the complementary haptic sensing channels allow the robot wearing the chest to detect coarse and fine contacts typically experienced during hugs, regardless of where the user contacts the robot. We also verified that we can detect contacts regardless of noise from the robot’s movement, as long as the HuggieChest is inflated within a certain pressure range.

The need for soft whole-body tactile sensors is emerging. Piezoresistive materials are advantageous in terms of making large tactile sensors, but the hysteresis of piezoresistive materials is a major drawback. The hysteresis of a piezoresistive material should be attenuated to make a practical piezoresistive soft tactile sensor. In this paper, we introduce a low-hysteresis and low-interference soft tactile sensor using a conductive coated porous elastomer and a structure to reduce interference (grooves). The developed sensor exhibits low hysteresis because the transduction mechanism of the sensor is dominated by the contact between the conductive coated surface. In a cyclic loading experiment with different loading frequencies, the mechanical and piezoresistive hysteresis values of the sensor are less than 21.7% and 6.8%, respectively. The initial resistance change is found to be within 4% after the first loading cycle. To reduce the interference among the sensing points, we also propose a structure where the grooves are inserted between the adjacent electrodes. This structure is implemented during the molding process, which is adopted to extend the porous tactile sensor to large-scale and facile fabrication. The effects of the structure are investigated with respect to the normalized design parameters ΘD, ΘW, and ΘT in a simulation, and the result is validated for samples with the same design parameters. An indentation experiment also shows that the structure designed for interference reduction effectively attenuates the interference of the sensor array, indicating that the spatial resolution of the sensor array is improved. As a result, the sensor can exhibit low hysteresis and low interference simultaneously. This research can be used for many applications, such as robotic skin, grippers, and wearable devices.

Masking has been used to study human perception of tactile stimuli, including those created on haptic touch screens. Earlier studies have investigated the effect of in-site masking on tactile perception of electrovibration. In this study, we investigated whether it is possible to change detection threshold of electrovibration at fingertip of index finger via remote masking, i.e. by applying a (mechanical) vibrotactile stimulus on the proximal phalanx of the same finger. The masking stimuli were generated by a voice coil (Haptuator). For eight participants, we first measured the detection thresholds for electrovibration at the fingertip and for vibrotactile stimuli at the proximal phalanx. Then, the vibrations on the skin were measured at four different locations on the index finger of subjects to investigate how the mechanical masking stimulus propagated as the masking level was varied. Finally, electrovibration thresholds measured in the presence of vibrotactile masking stimuli. Our results show that vibrotactile masking stimuli generated sub-threshold vibrations around fingertip, and hence did not mechanically interfere with the electrovibration stimulus. However, there was a clear psychophysical masking effect due to central neural processes. Electrovibration absolute threshold increased approximately 0.19 dB for each dB increase in the masking level.

Using a force-controlled robotic platform, we tested the human perception of positive and negative modulations in normal force during passive dynamic touch, which also induced a strong related change in the finger-surface lateral force. In a two-alternative forced-choice task, eleven participants had to detect brief variations in the normal force compared to a constant controlled pre-stimulation force of 1 N and report whether it had increased or decreased. The average 75% just noticeable difference (JND) was found to be around 0.25 N for detecting the peak change and 0.30 N for correctly reporting the increase or the decrease. Interestingly, the friction coefficient of a subject’s fingertip positively correlated with his or her performance at detecting the change and reporting its direction, which suggests that humans may use the lateral force as a sensory cue to perceive variations in the normal force.

Both vision and touch contribute to the perception of real surfaces. Although there have been many studies on the individual contributions of each sense, it is still unclear how each modality’s information is processed and integrated. To fill this gap, we investigated the similarity of visual and haptic perceptual spaces, as well as how well they each correlate with fingertip interaction metrics. Twenty participants interacted with ten different surfaces from the Penn Haptic Texture Toolkit by either looking at or touching them and judged their similarity in pairs. By analyzing the resulting similarity ratings using multi-dimensional scaling (MDS), we found that surfaces are similarly organized within the three-dimensional perceptual spaces of both modalities. Also, between-participant correlations were significantly higher in the haptic condition. In a separate experiment, we obtained the contact forces and accelerations acting on one finger interacting with each surface in a controlled way. We analyzed the collected fingertip interaction data in both the time and frequency domains. Our results suggest that the three perceptual dimensions for each modality can be represented by roughness/smoothness, hardness/softness, and friction, and that these dimensions can be estimated by surface vibration power, tap spectral centroid, and kinetic friction coefficient, respectively.

In this study, we develop a high-fidelity finite element (FE) analysis framework that enables multiphysics simulation of the human finger in contact with a surface that is providing tactile feedback. We aim to elucidate a variety of physical interactions that can occur at finger-surface interfaces, including contact, friction, vibration, and electrovibration. We also develop novel FE-based methods that will allow prediction of nonconventional features such as real finger-surface contact area and finger stickiness. We envision using the developed computational tools for efficient design and optimization of haptic devices by replacing expensive and lengthy experimental procedures with high-fidelity simulation.

During hugs, humans naturally provide and intuit subtle non-verbal cues that signify the strength and duration of an exchanged hug. Personal preferences for this close interaction may vary greatly between people; robots do not currently have the abilities to perceive or understand these preferences. This work-in-progress paper discusses designing, building, and testing a novel inflatable torso that can simultaneously soften a robot and act as a tactile sensor to enable more natural and responsive hugging. Using PVC vinyl, a microphone, and a barometric pressure sensor, we created a small test chamber to demonstrate a proof of concept for the full torso. While contacting the chamber in several ways common in hugs (pat, squeeze, scratch, and rub), we recorded data from the two sensors. The preliminary results suggest that the complementary haptic sensing channels allow us to detect coarse and fine contacts typically experienced during hugs, regardless of user hand placement.

A typical approach to creating realistic vibrotactile feedback is reducing 3D vibrations recorded by an accelerometer to 1D signals that can be played back on a haptic actuator, but some of the information is often lost in this dimensional reduction process. This paper describes seven representative algorithms and proposes four metrics based on the spectral match, the temporal match, and the average value and the variability of them across 3D rotations. These four performance metrics were applied to four texture recordings, and the method utilizing the discrete fourier transform (DFT) was found to be the best regardless of the sensing axis. We also recruited 16 participants to assess the perceptual similarity achieved by each algorithm in real time. We found the four metrics correlated well with the subjectively rated similarities for the six dimensional reduction algorithms, with the exception of taking the 3D vector magnitude, which was perceived to be good despite its low spectral and temporal match metrics.

Dysgraphia is a neurological disorder characterized by writing disabilities that affects between 7% and 15% of children. It presents itself in the form of unfinished letters, letter distortion, inconsistent letter size, letter collision, etc. Traditional therapeutic exercises require continuous assistance from teachers or occupational therapists. Autonomous partial or full haptic guidance can produce positive results, but children often become bored with the repetitive nature of such activities. Conversely, virtual rehabilitation with video games represents a new frontier for occupational therapy due to its highly motivational nature. Virtual reality (VR) adds an element of novelty and entertainment to therapy, thus motivating players to perform exercises more regularly. We propose leveraging the HTC VIVE Pro and the EXOS Wrist DK2 to create an immersive spellcasting “exergame” (exercise game) that helps motivate children with dysgraphia to improve writing fluency.

To understand the adhesive force that occurs when a finger pulls off of a smooth surface, we built an apparatus to measure the fingerpad’s moisture, normal force, and real contact area over time during interactions with a glass plate. We recorded a total of 450 trials (45 interactions by each of ten human subjects), capturing a wide range of values across the aforementioned variables. The experimental results showed that the pull-off force increases with larger finger contact area and faster detachment rate. Additionally, moisture generally increases the contact area of the finger, but too much moisture can restrict the increase in the pull-off force.

Existing fingertip haptic devices can deliver different subsets of tactile cues in a compact package, but we have not yet seen a wearable six-degree-of-freedom (6-DOF) display. This paper presents the Fuppeteer (short for Fingertip Puppeteer), a device that is capable of controlling the position and orientation of a flat platform, such that any combination of normal and shear force can be delivered at any location on any human fingertip. We build on our previous work of designing a parallel continuum manipulator for fingertip haptics by presenting a motorized version in which six flexible Nitinol wires are actuated via independent roller mechanisms and proportional-derivative controllers. We evaluate the settling time and end-effector vibrations observed during system responses to step inputs. After creating a six-dimensional lookup table and adjusting simulated inputs using measured Jacobians, we show that the device can make contact with all parts of the fingertip with a mean error of 1.42 mm. Finally, we present results from a human-subject study. A total of 24 users discerned 9 evenly distributed contact locations with an average accuracy of 80.5%. Translational and rotational shear cues were identified reasonably well near the center of the fingertip and more poorly around the edges.

In this paper we present a novel method of categorizing naturalistic human arm motions during activities of daily living using clustering techniques. While many current approaches attempt to define all arm motions using heuristic interpretation, or a combination of several abstract motion primitives, our unsupervised approach generates a hierarchical description of natural human motion with well recognized groups. Reliable recommendation of a subset of motions for task achievement is beneficial to various fields, such as robotic and semi-autonomous prosthetic device applications. The proposed method makes use of well-known techniques such as dynamic time warping (DTW) to obtain a divergence measure between motion segments, DTW barycenter averaging (DBA) to get a motion average, and Ward's distance criterion to build the hierarchical tree. The clusters that emerge summarize the variety of recorded motions into the following general tasks: reach-to-front, transfer-box, drinking from vessel, on-table motion, turning a key or door knob, and reach-to-back pocket. The clustering methodology is justified by comparing against an alternative measure of divergence using Bezier coefficients and K-medoids clustering.

Since the 1960s, technologists have worked to develop systems that facilitate independent navigation by vision-impaired (VI) pedestrians. These devices vary in terms of conveyed information and feedback modality. Unfortunately, many such prototypes never progress beyond laboratory testing. Conversely, smartphone-based navigation systems for sighted pedestrians have grown in robustness and capabilities, to the point of now being ubiquitous. How can we leverage the success of sighted navigation technology, which is driven by a larger global market, as a way to progress VI navigation systems? We believe one possibility is to make common devices that benefit both VI and sighted individuals, by providing information in a way that does not distract either user from their tasks or environment. To this end we have developed physical interfaces that eschew visual, audio or vibratory feedback, instead relying on the natural human ability to perceive the shape of a handheld object.

Creating haptic experiences often entails inventing, modifying, or selecting specialized hardware. However, experience designers are rarely engineers, and 30 years of haptic inventions are buried in a fragmented literature that describes devices mechanically rather than by potential purpose. We conceived of Haptipedia to unlock this trove of examples: Haptipedia presents a device corpus for exploration through metadata that matter to both device and experience designers. It is a taxonomy of device attributes that go beyond physical description to capture potential utility, applied to a growing database of 105 grounded force-feedback devices, and accessed through a public visualization that links utility to morphology. Haptipedia's design was driven by both systematic review of the haptic device literature and rich input from diverse haptic designers. We describe Haptipedia's reception (including hopes it will redefine device reporting standards) and our plans for its sustainability through community participation.

Humans can form an impression of how a new object feels simply by touching its surfaces with the densely innervated skin of the fingertips. Many haptics researchers have recently been working to endow robots with similar levels of haptic intelligence, but these efforts almost always employ hand-crafted features, which are brittle, and concrete tasks, such as object recognition. We applied unsupervised feature learning methods, specifically K-SVD and Spatio-Temporal Hierarchical Matching Pursuit (ST-HMP), to rich multi-modal haptic data from a diverse dataset. We then tested the learned features on 19 more abstract binary classification tasks that center on haptic adjectives such as smooth and squishy. The learned features proved superior to traditional hand-crafted features by a large margin, almost doubling the average F1 score across all adjectives. Additionally, particular exploratory procedures (EPs) and sensor channels were found to support perception of certain haptic adjectives, underlining the need for diverse interactions and multi-modal haptic data.

Haptipedia is an online taxonomy, database, and visualization that aims to accelerate ideation of new haptic devices and interactions in human-computer interaction, virtual reality, haptics, and robotics. The current version of Haptipedia (105 devices) was created through iterative design, data entry, and evaluation by our team of experts. Next, we aim to greatly increase the number of devices and keep Haptipedia updated by soliciting data entry and verification from haptics experts worldwide.

We live in an aging society; social-physical human-robot interaction has the potential to keep our elderly adults healthy by motivating them to exercise. After summarizing prior work, this paper proposes a tool that can be used to design exercise and therapy interactions to be performed by an upper-body humanoid robot. The interaction design tool comprises a teleoperation system that transmits the operator’s arm motions, head motions and facial expression along with an interface to monitor and assess the motion of the user interacting with the robot. We plan to use this platform to create dynamic and intuitive exercise interactions.

Generating realistic texture feelings on tactile displays using data-driven methods has attracted a lot of interest in the last decade. However, the need for large data storages and transmission rates complicates the use of these methods for the future commercial displays. In this paper, we propose a new texture rendering approach which can compress the texture data signicantly for electrostatic displays. Using three sample surfaces, we first explain how to record, analyze and compress the texture data, and render them on a touchscreen. Then, through psychophysical experiments conducted with nineteen participants, we show that the textures can be reproduced by a signicantly less number of frequency components than the ones in the original signal without inducing perceptual degradation. Moreover, our results indicate that the possible degree of compression is affected by the surface properties.

This report presents research done at the Max Planck Institute for Intelligent Systems from January 2016 to December 2018. It is our third report since the founding of the institute in 2011. This status report is organized as follows: we begin with an overview of the institute, including its organizational structure (Chapter 1). The central part of the scientific report consists of chapters on the research conducted by the institute’s departments (Chapters 2 to 5) and its independent research groups (Chapters 6 to 18), as well as the work of the institute’s central scientific facilities (Chapter 19). For entities founded after January 2016, the respective report sections cover work done from the date of the establishment of the department, group, or facility.

Large-scale tactile sensing is important for household robots and human-robot interaction because contacts can occur all over a robot’s body surface. This paper presents a new fabric-based tactile sensor that is straightforward to manufacture and can cover a large area. The tactile sensor is made of conductive and non-conductive fabric layers, and the electrodes are stitched with conductive thread, so the resulting device is flexible and stretchable. The sensor utilizes internal array electrodes and a reconstruction method called electrical resistance tomography (ERT) to achieve a high spatial resolution with a small number of electrodes. The developed sensor shows that only 16 electrodes can accurately estimate single and multiple contacts over a square that measures 20 cm by 20 cm.

Hugs are one of the first forms of contact and affection humans experience. Due to their prevalence and health benefits, roboticists are naturally interested in having robots one day hug humans as seamlessly as humans hug other humans. This project's purpose is to evaluate human responses to different robot physical characteristics and hugging behaviors. Specifically, we aim to test the hypothesis that a soft, warm, touch-sensitive PR2 humanoid robot can provide humans with satisfying hugs by matching both their hugging pressure and their hugging duration. Thirty relatively young and rather technical participants experienced and evaluated twelve hugs with the robot, divided into three randomly ordered trials that focused on physical robot characteristics (single factor, three levels) and nine randomly ordered trials with low, medium, and high hug pressure and duration (two factors, three levels each). Analysis of the results showed that people significantly prefer soft, warm hugs over hard, cold hugs. Furthermore, users prefer hugs that physically squeeze them and release immediately when they are ready for the hug to end. Taking part in the experiment also significantly increased positive user opinions of robots and robot use.

Autonomous robots need to efficiently walk over varied surfaces and grasp diverse objects. We hypothesize that the association between how such surfaces look and how they physically feel during contact can be learned from a database of matched haptic and visual data recorded from various end-effectors' interactions with hundreds of real-world surfaces. Testing this hypothesis required the creation of a new multimodal sensing apparatus, the collection of a large multimodal dataset, and development of a machine-learning pipeline. This thesis begins by describing the design and construction of the Portable Robotic Optical/Tactile ObservatioN PACKage (PROTONPACK, or Proton for short), an untethered handheld sensing device that emulates the capabilities of the human senses of vision and touch. Its sensory modalities include RGBD vision, egomotion, contact force, and contact vibration. Three interchangeable end-effectors (a steel tooling ball, an OptoForce three-axis force sensor, and a SynTouch BioTac artificial fingertip) allow for different material properties at the contact point and provide additional tactile data. We then detail the calibration process for the motion and force sensing systems, as well as several proof-of-concept surface discrimination experiments that demonstrate the reliability of the device and the utility of the data it collects. This thesis then presents a large-scale dataset of multimodal surface interaction recordings, including 357 unique surfaces such as furniture, fabrics, outdoor fixtures, and items from several private and public material sample collections. Each surface was touched with one, two, or three end-effectors, comprising approximately one minute per end-effector of tapping and dragging at various forces and speeds. We hope that the larger community of robotics researchers will find broad applications for the published dataset. Lastly, we demonstrate an algorithm that learns to estimate haptic surface properties given visual input. Surfaces were rated on hardness, roughness, stickiness, and temperature by the human experimenter and by a pool of purely visual observers. Then we trained an algorithm to perform the same task as well as infer quantitative properties calculated from the haptic data. Overall, the task of predicting haptic properties from vision alone proved difficult for both humans and computers, but a hybrid algorithm using a deep neural network and a support vector machine achieved a correlation between expected and actual regression output between approximately ρ = 0.3 and ρ = 0.5 on previously unseen surfaces.

Little is known about the shape and properties of the human finger during haptic interaction, even though these are essential parameters for controlling wearable finger devices and deliver realistic tactile feedback. This study explores a framework for four-dimensional scanning (3D over time) and modelling of finger-surface interactions, aiming to capture the motion and deformations of the entire finger with high resolution while simultaneously recording the interfacial forces at the contact. Preliminary results show that when the fingertip is actively pressing a rigid surface, it undergoes lateral expansion and proximal/distal bending, deformations that cannot be captured by imaging of the contact area alone. Therefore, we are currently capturing a dataset that will enable us to create a statistical model of the finger’s deformations and predict the contact forces induced by tactile interaction with objects. This technique could improve current methods for tactile rendering in wearable haptic devices, which rely on general physical modelling of the skin’s compliance, by developing an accurate model of the variations in finger properties across the human population. The availability of such a model will also enable a more realistic simulation of virtual finger behaviour in virtual reality (VR) environments, as well as the ability to accurately model a specific user’s finger from lower resolution data. It may also be relevant for inferring the physical properties of the underlying tissue from observing the surface mesh deformations, as previously shown for body tissues.

The broader research objective of this line of research is to test the hypothesis that real-time stereo video analysis and augmented reality can increase safety and task efficiency in robot-assisted surgery. This master’s thesis aims to solve the first step needed to achieve this goal: the creation of a robust system that delivers the envisioned feedback to a surgeon while he or she controls a surgical robot that is identical to those used on human patients. Several approaches for applying augmented reality to da Vinci Surgical Systems have been proposed, but none of them entirely rely on a clinical robot; specifically, they require additional sensors, depend on access to the da Vinci API, are designed for a very specific task, or were tested on systems that are starkly different from those in clinical use. There has also been prior work that presents the real-world camera view and the computer graphics on separate screens, or not in real time. In other scenarios, the digital information is overlaid manually by the surgeons themselves or by computer scientists, rather than being generated automatically in response to the surgeon’s actions. We attempted to overcome the aforementioned constraints by acquiring input signals from the da Vinci stereo endoscope and providing augmented reality to the console in real time (less than 150 ms delay, including the 62 ms of inherent latency of the da Vinci). The potential benefits of the resulting system are broad because it was built to be general, rather than customized for any specific task. The entire platform is compatible with any generation of the da Vinci System and does not require a dVRK (da Vinci Research Kit) or access to the API. Thus, it can be applied to existing da Vinci Systems in operating rooms around the world.

Small size and low weight are critical requirements for wearable and portable haptic interfaces, making it essential to work toward the optimization of their sensing and actuation systems. This paper presents a new approach for task-driven design optimization of fingertip cutaneous haptic devices. Given one (or more) target tactile interactions to render and a cutaneous device to optimize, we evaluate the minimum number and best configuration of the device’s actuators to minimize the estimated haptic rendering error. First, we calculate the motion needed for the original cutaneous device to render the considered target interaction. Then, we run a principal component analysis (PCA) to search for possible couplings between the original motor inputs, looking also for the best way to reconfigure them. If some couplings exist, we can re-design our cutaneous device with fewer motors, optimally configured to render the target tactile sensation. The proposed approach is quite general and can be applied to different tactile sensors and cutaneous devices. We validated it using a BioTac tactile sensor and custom plate-based 3-DoF and 6-DoF fingertip cutaneous devices, considering six representative target tactile interactions. The algorithm was able to find couplings between each device’s motor inputs, proving it to be a viable approach to optimize the design of wearable and portable cutaneous devices. Finally, we present two examples of optimized designs for our 3-DoF fingertip cutaneous device.

Colleagues often shake hands in greeting, friends connect through high fives, and children around the world rejoice in hand-clapping games. As robots become more common in everyday human life, they will have the opportunity to join in these social-physical interactions, but few current robots are intended to touch people in friendly ways. This article describes how we enabled a Baxter Research Robot to both teach and learn bimanual hand-clapping games with a human partner. Our system monitors the user's motions via a pair of inertial measurement units (IMUs) worn on the wrists. We recorded a labeled library of 10 common hand-clapping movements from 10 participants; this dataset was used to train an SVM classifier to automatically identify hand-clapping motions from previously unseen participants with a test-set classification accuracy of 97.0%. Baxter uses these sensors and this classifier to quickly identify the motions of its human gameplay partner, so that it can join in hand-clapping games. This system was evaluated by N = 24 naïve users in an experiment that involved learning sequences of eight motions from Baxter, teaching Baxter eight-motion game patterns, and completing a free interaction period. The motion classification accuracy in this less structured setting was 85.9%, primarily due to unexpected variations in motion timing. The quantitative task performance results and qualitative participant survey responses showed that learning games from Baxter was significantly easier than teaching games to Baxter, and that the teaching role caused users to consider more teamwork aspects of the gameplay. Over the course of the experiment, people felt more understood by Baxter and became more willing to follow the example of the robot. Users felt uniformly safe interacting with Baxter, and they expressed positive opinions of Baxter and reported fun interacting with the robot. Taken together, the results indicate that this robot achieved credible social-physical interaction with humans and that its ability to both lead and follow systematically changed the human partner's experience.

Children with autism often endure sensory overload, may be nonverbal, and have difficulty understanding and relaying emotions. These experiences result in heightened stress during social interaction. Animal-assisted intervention has been found to improve the behavior of children with autism during social interaction, but live animal companions are not always feasible. We are thus in the process of designing a robotic animal to mimic some successful characteristics of animal-assisted intervention while trying to improve on others. The over-arching hypothesis of this research is that an appropriately designed robot animal can reduce stress in children with autism and empower them to engage in social interaction.

How many haptic devices have been proposed in the last 30 years? How can we leverage this rich source of design knowledge to inspire future innovations? Our goal is to make historical haptic invention accessible through interactive visualization of a comprehensive library – a Haptipedia – of devices that have been annotated with designer-relevant metadata. In this demonstration, participants can explore Haptipedia’s growing library of grounded force feedback devices through several prototype visualizations, interact with 3D simulations of the device mechanisms and movements, and tell us about the attributes and devices that could make Haptipedia a useful resource for the haptic design community.

In this demonstration, you will hold two pen-shaped modules: an in-pen and an out-pen. The in-pen is instrumented with a high-bandwidth three-axis accelerometer, and the out-pen contains a one-axis voice coil actuator. Use the in-pen to interact with different surfaces; the measured 3D accelerations are continually converted into 1D vibrations and rendered with the out-pen for you to feel. You can test conversion methods that range from simply selecting a single axis to applying a discrete Fourier transform or principal component analysis for realistic and brisk real-time conversion.

Haptics is an interdisciplinary field that seeks to both understand and engineer touch-based interaction. Although a wide range of systems and applications are being investigated, haptics researchers often concentrate on perception and manipulation through the human hand. A haptic interface is a mechatronic system that modulates the physical interaction between a human and his or her tangible surroundings. Haptic interfaces typically involve mechanical, electrical, and computational layers that work together to sense user motions or forces, quickly process these inputs with other information, and physically respond by actuating elements of the user’s surroundings, thereby enabling him or her to act on and feel a remote and/or virtual environment.

For simple and realistic vibrotactile feedback, 3D accelerations from real contact interactions are usually rendered using a single-axis vibration actuator; this dimensional reduction can be performed in many ways. This demonstration implements a real-time conversion system that simultaneously measures 3D accelerations and renders corresponding 1D vibrations using a two-pen interface. In the demonstration, a user freely interacts with various objects using an In-Pen that contains a 3-axis accelerometer. The captured accelerations are converted to a single-axis signal, and an Out-Pen renders the reduced signal for the user to feel. We prepared seven conversion methods from the simple use of a single-axis signal to applying principal component analysis (PCA) so that users can compare the performance of each conversion method in this demonstration.