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Contact pressure between the human body and its surroundings has important implications. For example, it plays a role in comfort, safety, posture, and health. We present a method that infers contact pressure between a human body and a mattress from a depth image. Specifically, we focus on using a depth image from a downward facing camera to infer pressure on a body at rest in bed occluded by bedding, which is directly applicable to the prevention of pressure injuries in healthcare. Our approach involves augmenting a real dataset with synthetic data generated via a soft-body physics simulation of a human body, a mattress, a pressure sensing mat, and a blanket. We introduce a novel deep network that we trained on an augmented dataset and evaluated with real data. The network contains an embedded human body mesh model and uses a white-box model of depth and pressure image generation. Our network successfully infers body pose, outperforming prior work. It also infers contact pressure across a 3D mesh model of the human body, which is a novel capability, and does so in the presence of occlusion from blankets.

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Mobile manipulators for indoor human environments can serve as versatile devices that perform a variety of tasks, yet adoption of this technology has been limited. Reducing size, weight, and cost could facilitate adoption, but risks restricting capabilities. We present a novel design that reduces size, weight, and cost, while supporting a variety of tasks. The core design consists of a two-wheeled differential-drive mobile base, a lift, and a telescoping arm configured to achieve Cartesian motion at the end of the arm. Design extensions include a 1 degree-of-freedom (DOF) wrist to stow a tool, a 2-DOF dexterous wrist to pitch and roll a tool, and a compliant gripper. We justify our design with anthropometry and mathematical models of static stability. We also provide empirical support from teleoperating and autonomously controlling a commercial robot based on our design (the Stretch RE1 from Hello Robot Inc.) to perform tasks in real homes.

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Humans and robots can recognize materials with distinct thermal effusivities by making physical contact and observing temperatures during heat transfer. This works well with room temperature materials, yet research has shown that contact with distinct materials can result in similar temperatures and confusion when one material is heated or cooled. To thoroughly investigate this form of ambiguity, we designed a psychophysical experiment in which a participant discriminates between two materials given initial conditions that result in similar temperatures (i.e., ambiguous initial conditions). In this article, we conducted a study with 32 human participants and a robot. Humans and the robot confused the materials. We also found that robots can overcome this ambiguity using two temperature sensors with different temperatures prior to contact. We support this conclusion based on a mathematical proof using a heat transfer model and empirical results in which a robot achieved 100% accuracy compared to 5% human accuracy. Our results also indicate that robots with a single temperature sensor can use subtle cues to outperform humans. Overall, our work provides insights into challenging conditions for material recognition via heat transfer, and suggests methods by which robots can overcome these challenges.

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Various situations, such as injuries or long-term disabilities, can result in people receiving physical assistance while in bed. We present a robotic system for bedside assistance that consists of a robotic bed and a mobile manipulator (i.e., a wheeled robot with arms) that work together to provide better assistance. Many assistive tasks depend on moving with respect to the person's body, and the complementary physical and perceptual capabilities of the two robots help with respect to this general goal. The system provides autonomy for common tasks, as well as an interface for direct teleoperation of the two robots. Autonomy handles coarse motions of the robots by estimating the person's pose using a pressure sensing mat and then moving the robots to configurations optimized for the task. After completing these motions, the user is given fine control of the robots to complete the task. In an evaluation using a medical mannequin, we found that the robotic bed's motion and perception each improved the assistive robotic system's performance. The system achieved 100% success over 9 trials involving 3 tasks. Using the system with the bed movement or the body pose estimation capabilities turned off resulted in success in only 33% or 78% of the trials, respectively. We also evaluated our system with Henry Evans, a person with severe quadriplegia, in his home. In a formal test, Henry successfully used the bedside-assistance system to perform 3 different tasks, 5 times each, without any failures. Henry's feedback on the system was positive regarding usefulness and ease of use, and he noted benefits of using our system over fully manual teleoperation. Overall, our results suggest that a robotic bed and a mobile manipulator can work collaboratively to provide effective personal assistance and that the combination of the two robots is beneficial.

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Robotic assistance presents an opportunity to benefit the lives of many people with physical disabilities, yet accurately sensing the human body and tracking human motion remain difficult for robots. We present a multidimensional capacitive sensing technique that estimates the local pose of a human limb in real time. A key benefit of this sensing method is that it can sense the limb through opaque materials, including fabrics and wet cloth. Our method uses a multielectrode capacitive sensor mounted to a robot's end effector. A neural network model estimates the position of the closest point on a person's limb and the orientation of the limb's central axis relative to the sensor's frame of reference. These pose estimates enable the robot to move its end effector with respect to the limb using feedback control. We demonstrate that a PR2 robot can use this approach with a custom six electrode capacitive sensor to assist with two activities of daily living- dressing and bathing. The robot pulled the sleeve of a hospital gown onto able-bodied participants' right arms, while tracking human motion. When assisting with bathing, the robot moved a soft wet washcloth to follow the contours of able-bodied participants' limbs, cleaning their surfaces. Overall, we found that multidimensional capacitive sensing presents a promising approach for robots to sense and track the human body during assistive tasks that require physical human-robot interaction.

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