Stretch with Stretch: Physical Therapy Exercise Games Led by a Mobile Manipulator

Physical therapy (PT) is a key component of many rehabilitation regimens, such as treatments for Parkinson's disease (PD). However, there are shortages of physical therapists and adherence to self-guided PT is low. Robots have the potential to support physical therapists and increase adherence to self-guided PT, but prior robotic systems have been large and immobile, which can be a barrier to use in homes and clinics. We present Stretch with Stretch (SWS), a novel robotic system for leading stretching exercise games for older adults with PD. SWS consists of a compact and lightweight mobile manipulator (Hello Robot Stretch RE1) that visually and verbally guides users through PT exercises. The robot's soft end effector serves as a target that users repetitively reach towards and press with a hand, foot, or knee. For each exercise, target locations are customized for the individual via a visually estimated kinematic model, a haptically estimated range of motion, and the person's exercise performance. The system includes sound effects and verbal feedback from the robot to keep users engaged throughout a session and augment physical exercise with cognitive exercise. We conducted a user study for which people with PD (n=10) performed 6 exercises with the system. Participants perceived the SWS to be useful and easy to use. They also reported mild to moderate perceived exertion (RPE).

The Un-Kidnappable Robot: Acoustic Localization of Sneaking People

How easy is it to sneak up on a robot? We examine whether we can detect people using only the incidental sounds they produce as they move, even when they try to be quiet. We collect a robotic dataset of high-quality 4-channel audio paired with 360 degree RGB data of people moving in different indoor settings. We train models that predict if there is a moving person nearby and their location using only audio. We implement our method on a robot, allowing it to track a single person moving quietly with only passive audio sensing. For demonstration videos, see our project page: https://sites.google.com/view/unkidnappable-robot

ForceSight: Text-Guided Mobile Manipulation with Visual-Force Goals

We present ForceSight, a system for text-guided mobile manipulation that predicts visual-force goals using a deep neural network. Given a single RGBD image combined with a text prompt, ForceSight determines a target end-effector pose in the camera frame (kinematic goal) and the associated forces (force goal). Together, these two components form a visual-force goal. Prior work has demonstrated that deep models outputting human-interpretable kinematic goals can enable dexterous manipulation by real robots. Forces are critical to manipulation, yet have typically been relegated to lower-level execution in these systems. When deployed on a mobile manipulator equipped with an eye-in-hand RGBD camera, ForceSight performed tasks such as precision grasps, drawer opening, and object handovers with an 81% success rate in unseen environments with object instances that differed significantly from the training data. In a separate experiment, relying exclusively on visual servoing and ignoring force goals dropped the success rate from 90% to 45%, demonstrating that force goals can significantly enhance performance. The appendix, videos, code, and trained models are available at https://force-sight.github.io/.

Visual Contact Pressure Estimation for Grippers in the Wild

Sensing contact pressure applied by a gripper is useful for autonomous and teleoperated robotic manipulation, but adding tactile sensing to a gripper's surface can be difficult or impractical. If a gripper visibly deforms when forces are applied, contact pressure can be visually estimated using images from an external camera that observes the gripper. While researchers have demonstrated this capability in controlled laboratory settings, prior work has not addressed challenges associated with visual pressure estimation in the wild, where lighting, surfaces, and other factors vary widely. We present a deep learning model and associated methods that enable visual pressure estimation under widely varying conditions. Our model, Visual Pressure Estimation for Robots (ViPER), takes an image from an eye-in-hand camera as input and outputs an image representing the pressure applied by a soft gripper. Our key insight is that force/torque sensing can be used as a weak label to efficiently collect training data in settings where pressure measurements would be difficult to obtain. When trained on this weakly labeled data combined with fully labeled data containing pressure measurements, ViPER outperforms prior methods, enables precision manipulation in cluttered settings, and provides accurate estimates for unseen conditions relevant to in-home use.

Visual Estimation of Fingertip Pressure on Diverse Surfaces using Easily Captured Data

Prior research has shown that deep models can estimate the pressure applied by a hand to a surface based on a single RGB image. Training these models requires high-resolution pressure measurements that are difficult to obtain with physical sensors. Additionally, even experts cannot reliably annotate pressure from images. Thus, data collection is a critical barrier to generalization and improved performance. We present a novel approach that enables training data to be efficiently captured from unmodified surfaces with only an RGB camera and a cooperative participant. Our key insight is that people can be prompted to perform actions that correspond with categorical labels (contact labels) describing contact pressure, such as using a specific fingertip to make low-force contact. We present ContactLabelNet, which visually estimates pressure applied by fingertips. With the use of contact labels, ContactLabelNet achieves improved performance, generalizes to novel surfaces, and outperforms models from prior work.

Force/Torque Sensing for Soft Grippers using an External Camera

Robotic manipulation can benefit from wrist-mounted force/torque (F/T) sensors, but conventional F/T sensors can be expensive, difficult to install, and damaged by high loads. We present Visual Force/Torque Sensing (VFTS), a method that visually estimates the 6-axis F/T measurement that would be reported by a conventional F/T sensor. In contrast to approaches that sense loads using internal cameras placed behind soft exterior surfaces, our approach uses an external camera with a fisheye lens that observes a soft gripper. VFTS includes a deep learning model that takes a single RGB image as input and outputs a 6-axis F/T estimate. We trained the model with sensor data collected while teleoperating a robot (Stretch RE1 from Hello Robot Inc.) to perform manipulation tasks. VFTS outperformed F/T estimates based on motor currents, generalized to a novel home environment, and supported three autonomous tasks relevant to healthcare: grasping a blanket, pulling a blanket over a manikin, and cleaning a manikin's limbs. VFTS also performed well with a manually operated pneumatic gripper. Overall, our results suggest that an external camera observing a soft gripper can perform useful visual force/torque sensing for a variety of manipulation tasks.

Visual Pressure Estimation and Control for Soft Robotic Grippers

Soft robotic grippers facilitate contact-rich manipulation, including robust grasping of varied objects. Yet the beneficial compliance of a soft gripper also results in significant deformation that can make precision manipulation challenging. We present visual pressure estimation & control (VPEC), a method that uses a single RGB image of an unmodified soft gripper from an external camera to directly infer pressure applied to the world by the gripper. We present inference results for a pneumatic gripper and a tendon-actuated gripper making contact with a flat surface. We also show that VPEC enables precision manipulation via closed-loop control of inferred pressure. We present results for a mobile manipulator (Stretch RE1 from Hello Robot) using visual servoing to do the following: achieve target pressures when making contact; follow a spatial pressure trajectory; and grasp small objects, including a microSD card, a washer, a penny, and a pill. Overall, our results show that VPEC enables grippers with high compliance to perform precision manipulation.

PressureVision: Estimating Hand Pressure from a Single RGB Image

People often interact with their surroundings by applying pressure with their hands. Machine perception of hand pressure has been limited by the challenges of placing sensors between the hand and the contact surface. We explore the possibility of using a conventional RGB camera to infer hand pressure. The central insight is that the application of pressure by a hand results in informative appearance changes. Hands share biomechanical properties that result in similar observable phenomena, such as soft-tissue deformation, blood distribution, hand pose, and cast shadows. We collected videos of 36 participants with diverse skin tone applying pressure to an instrumented planar surface. We then trained a deep model (PressureVisionNet) to infer a pressure image from a single RGB image. Our model infers pressure for participants outside of the training data and outperforms baselines. We also show that the output of our model depends on the appearance of the hand and cast shadows near contact regions. Overall, our results suggest the appearance of a previously unobserved human hand can be used to accurately infer applied pressure.

The Design of Stretch: A Compact, Lightweight Mobile Manipulator for Indoor Human Environments

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 still performing 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 mathematical models of static stability that relate the robot's size and weight to its workspace, payload, and applied forces. We also provide empirical support by teleoperating and autonomously controlling a commercial robot based on our design (the Stretch RE1 from Hello Robot Inc.) to perform tasks in real homes.

Bodies Uncovered: Learning to Manipulate Real Blankets Around People via Physics Simulations

While robots present an opportunity to provide physical assistance to older adults and people with mobility impairments in bed, people frequently rest in bed with blankets that cover the majority of their body. To provide assistance for many daily self-care tasks, such as bathing, dressing, or ambulating, a caregiver must first uncover blankets from part of a person's body. In this work, we introduce a formulation for robotic bedding manipulation around people in which a robot uncovers a blanket from a target body part while ensuring the rest of the human body remains covered. We compare both reinforcement and supervised learning approaches for optimizing policies which provide a robot with grasp and release points that uncover a target part of the body. We trained and conducted evaluations of these policies in physics simulation environments that consist of a deformable cloth mesh covering a simulated human lying supine on a bed. In addition, we transfer simulation-trained policies to a real mobile manipulator and demonstrate that it can uncover a blanket from target body parts of a manikin lying in bed. Source code is available online.