Millions of people around the world need assistance with feeding. Robotic feeding systems offer the potential to enhance autonomy and quality of life for individuals with impairments and reduce caregiver workload. However, their widespread adoption has been limited by technical challenges such as estimating bite timing, the appropriate moment for the robot to transfer food to a user’s mouth. In this work, we introduce WAFFLE: Wearable Approach For Feeding with LEarned Bite Timing, a system that accurately predicts bite timing by leveraging wearable sensor data to be highly reactive to natural user cues such as head movements, chewing, and talking. We train a supervised regression model on bite timing data from 14 participants and incorporate a user-adjustable assertiveness threshold to convert predictions into proceed or stop commands. In a study with 15 participants without motor impairments with the Obi feeding robot, WAFFLE performs statistically on par with or better than baseline methods across measures of feeling of control, robot understanding, and workload, and is preferred by the majority of participants for both individual and social dining. We further demonstrate WAFFLE’s generalizability in a study with 2 participants with motor impairments in their home environments using a Kinova 7DOF robot. Our findings support WAFFLE’s effectiveness in enabling natural, reactive bite timing that generalizes across users, robot hardware, robot positioning, feeding trajectories, foods, and both individual and social dining contexts. Videos are located at https://sites.google.com/view/bitetiming/.

Ensuring safety via safety filters in real-world robotics presents significant challenges, particularly when the system dynamics is complex or unavailable. To handle this issue, learning-based safety filters recently gained popularity, which can be classified as model-based and model-free methods. Existing model-based approaches requires various assumptions on system model (e.g., control-affine), which limits their application in complex systems, and existing model-free approaches need substantial modifications to standard RL algorithms and lack versatility. This paper proposes a simple, plugin-and-play, and effective model-free safety filter learning framework. We introduce a novel reward formulation and use Q-learning to learn Q-value functions to safeguard arbitrary task specific nominal policies via filtering out their potentially unsafe actions. Due to its model-free nature and simplicity, our framework can be seamlessly integrated with various RL algorithms. We validate the proposed approach through simulations on double integrator and Dubin's car systems and demonstrate its effectiveness in real-world experiments with a soft robotic limb.

Wireless research has advanced in utilizing channel diversity and beamforming for more efficient communication, sensing, and harvesting ambient energy. We demonstrate our wireless robotic platform that utilizes radio-frequency beamforming for robot actuation. The platform delivers a maximum of 60 watts of power accurately towards soft robotic actuators by efficient frequency-aware beamforming. We also engineer soft actuators to absorb microwaves of specific frequencies to enable selective actuation. In this demonstration, we show a simplified version of our system that achieves frequency-selective actuation of two actuators to enable simple robot locomotion.

Teleoperation of mobile manipulators within a home environment can significantly enhance the independence of individuals with severe motor impairments, allowing them to regain the ability to perform self-care and household tasks. There is a critical need for novel teleoperation interfaces to offer effective alternatives for individuals with impairments who may encounter challenges in using existing interfaces due to physical limitations. In this work, we iterate on one such interface, HAT (Head-Worn Assistive Teleoperation), an inertial-based wearable integrated into any head-worn garment. We evaluate HAT through a 7-day in-home study with Henry Evans, a non-speaking individual with quadriplegia who has participated extensively in assistive robotics studies. We additionally evaluate HAT with a proposed shared control method for mobile manipulators termed Driver Assistance and demonstrate how the interface generalizes to other physical devices and contexts. Our results show that HAT is a strong teleoperation interface across key metrics including efficiency, errors, learning curve, and workload. Code and videos are located on our project website.

Reasoning from sequences of raw sensory data is a ubiquitous problem across fields ranging from medical devices to robotics. These problems often involve using long sequences of raw sensor data (e.g. magnetometers, piezoresistors) to predict sequences of desirable physical quantities (e.g. force, inertial measurements). While classical approaches are powerful for locally-linear prediction problems, they often fall short when using real-world sensors. These sensors are typically non-linear, are affected by extraneous variables (e.g. vibration), and exhibit data-dependent drift. For many problems, the prediction task is exacerbated by small labeled datasets since obtaining ground-truth labels requires expensive equipment. In this work, we present Hierarchical State-Space models (HiSS), a conceptually simple, new technique for continuous sequential prediction. HiSS stacks structured state-space models on top of each other to create a temporal hierarchy. Across six real-world sensor datasets, from tactile-based state prediction to accelerometer-based inertial measurement, HiSS outperforms state-of-the-art sequence models such as causal Transformers, LSTMs, S4, and Mamba by at least 23% on MSE. Our experiments further indicate that HiSS demonstrates efficient scaling to smaller datasets and is compatible with existing data-filtering techniques. Code, datasets and videos can be found on https://hiss-csp.github.io.

Physically assistive robots present an opportunity to significantly increase the well-being and independence of individuals with motor impairments or other forms of disability who are unable to complete activities of daily living. Speech interfaces, especially ones that utilize Large Language Models (LLMs), can enable individuals to effectively and naturally communicate high-level commands and nuanced preferences to robots. Frameworks for integrating LLMs as interfaces to robots for high level task planning and code generation have been proposed, but fail to incorporate human-centric considerations which are essential while developing assistive interfaces. In this work, we present a framework for incorporating LLMs as speech interfaces for physically assistive robots, constructed iteratively with 3 stages of testing involving a feeding robot, culminating in an evaluation with 11 older adults at an independent living facility. We use both quantitative and qualitative data from the final study to validate our framework and additionally provide design guidelines for using LLMs as speech interfaces for assistive robots. Videos, code, and supporting files are located on our project website1

Mobile manipulators in the home can provide increased autonomy to individuals with severe motor impairments, who often cannot complete activities of daily living (ADLs) without the help of a caregiver. Teleoperation of an assistive mobile manipulator could enable an individual with motor impairments to independently perform self-care and household tasks, yet limited motor function can impede one's ability to interface with a robot. In this work, we present a unique inertial-based wearable assistive interface, embedded in a familiar head-worn garment, for individuals with severe motor impairments to teleoperate and perform physical tasks with a mobile manipulator. We evaluate this wearable interface with both able-bodied ($\mathrm{N}=16$) and individuals with motor impairments ($\mathrm{N}=2$) for performing ADLs and everyday household tasks. Our results show that the wearable interface enabled participants to complete physical tasks with low error rates, high perceived ease of use, and low workload measures. Overall, this inertial-based wearable serves as a new assistive interface option for control of mobile manipulators in the home.

Recent work on battery-free soft robotics has demonstrated the use of liquid crystal elastomers (LCE) to build shape-changing materials activated by applied external heat. However, sources of heat must typically be in direct field-of-view of the robot (i.e. NIR, laser, and visual light EM sources or convective heats guns), be tethered to an external power supply (i.e. thermoelectric heating or resistive joule heaters), or require a heavy on-board battery that limits mobility and range. This paper presents a novel battery-free soft-robotics platform that can crawl through confined, enclosed, and hard-to-reach spaces (e.g. packages, machinery, pipes, etc.), hidden from view of heating infrastructure. This is achieved through the co-design of a soft robotics platform and integrated soft conductive traces that enable wireless (microwave) heating through remote stimulation. We achieve fast actuation through a careful choice of materials and the overall mechanical structure of the robot to maximize heating efficiency. Further, the robot is actively tracked through enclosed spaces using a mm Wave radar to direct heat to its location. We provide a detailed evaluation on the robot's heating efficiency, location-tracking accuracy and crawling speed.

This paper proposes a new primitive that allows soft robots to be physically controlled in a completely non-line-of-sight context using wireless energy - a process we call wireless actuation. Soft robots, which are composed entirely of soft materials and exclude any rigid components, are highly flexible platforms that can change their shape. This paper considers a specific class of soft robots composed of liquid-crystal elastomers (LCE) that are entirely electronics-free and engineered to change shape when heated to 60 °C. Traditionally, such robotic systems must be in line-of-sight of a light source, such as infrared to be moved, or require an external power supply for Joule heating and often take several tens of seconds to heat. We present WASER, a novel RF-based heating platform that allows electronics-free robots to be actuated rapidly (within a few seconds) and potentially in non-line-of-sight. WASER achieves this through innovations in both wireless systems and material science. On the wireless front, WASER develops a new blind beamforming solution that directs high-power wireless energy at fine spatial granularity without electronics on the robot to provide feedback. On the material science front, WASER exhibits heat-responsive shape-morphing and energy-harvesting material functionalities that allow for rapid wireless heating. We implement and evaluate WASER and demonstrate diverse shape-morphing capabilities.

From creating input devices to rendering tangible information, the field of HCI is interested in using kinematic mechanisms to create human-computer interfaces. Yet, due to fabrication and design challenges, it is often difficult to create kinematic devices that are compact and have multiple reconfigurable motional degrees of freedom (DOFs) depending on the interaction scenarios. In this work, we combine compliant mechanisms (CMs) with tensioning cables to create dynamically reconfigurable kinematic mechanisms. The devices’ kinematics (DOFs) is enabled and determined by the layout of bendable rods. The additional cables function as on-demand motion constraints that can dynamically lock or unlock the mechanism's DOFs as they are tightened or loosened. We provide algorithms and a design tool prototype to help users design such kinematic devices. We also demonstrate various HCI use cases including a kinematic haptic display, a haptic proxy, and a multimodal input device.

Having accurate localization capabilities is one of the fundamental requirements of autonomous robots. For underwater vehicles, the choices for effective localization are limited due to limitations of GPS use in water and poor environ-mental visibility that makes camera-based methods ineffective. Popular inertial navigation methods for underwater localization using Doppler-velocity log sensors, sonar, high-end inertial navigation systems, or acoustic positioning systems require bulky expensive hardware which are incompatible with low-cost, bio-inspired underwater robots. In this paper, we introduce an approach for underwater robot localization inspired by GPS methods known as acoustic pseudoranging. Our method allows us to potentially localize multiple bio-inspired robots equipped with commonly available micro electro-mechanical systems microphones. This is achieved through estimating the time difference of arrival of acoustic signals sent simultaneously through four speakers with a known constellation geometry. We also leverage the same acoustic framework to perform one-way communication with the robot to execute some primitive motions. To our knowledge, this is the first application of the approach for the on-board localization of small bio-inspired robots in water. Hardware schematics and the accompanying code are released to aid further development in the field33https://github.com/rpl-cmu/underwater-acoustic-pseudoranging.

Simulating the swimming of soft underwater robot remains challenging due to the absence of an efficient numerical framework that can effectively capture the geometrically nonlinear deformation of soft materials and structures when interacting with a liquid environment. Here, we address this by introducing a discrete differential geometry-based model that incorporates an implicit treatment of the elasticity of soft limbs and a fluid model with three different components: hydrodynamic drag, jetting, and virtual added mass. The physical engine can run faster than real-time on a single thread desktop processor. We experimentally validate this numerical simulation tool by performing tests using an untethered omni-directional star-shaped swimming soft robot that is capable of moving with multiple swimming gaits. Quantitative agreement between experiment and simulation indicates the potential application of such a numerical framework for robot design and for model-based control schemes.

This paper presents a radio-frequency (RF) based assistive technology for voice impairments (i.e., dysphonia), which occurs in an estimated 1% of the global population. We specifically focus on acquired voice disorders where users continue to be able to make facial and lip gestures associated with speech. Despite the rich literature on assistive technologies in this space, there remains a gap for a solution that neither requires external infrastructure in the environment, battery-powered sensors on skin or body-worn manual input devices. We present RFTattoo, which to our knowledge is the first wireless speech recognition system for voice impairments using batteryless and flexible RFID tattoos. We design specialized wafer-thin tattoos attached around the user's face and easily hidden by makeup. We build models that process signal variations from these tattoos to a portable RFID reader to recognize various facial gestures corresponding to distinct classes of sounds. We then develop natural language processing models that infer meaningful words and sentences based on the observed series of gestures. A detailed user study with 10 users reveals 86% accuracy in reconstructing the top-100 words in the English language, even without the users making any sounds.

This paper builds a Near-field Communication (NFC) based localization system that allows ordinary surfaces to locate surrounding objects with high accuracy in the near-field. While there is rich prior work on device-free localization using far-field wireless technologies, the near-field is less explored. Prior work in this space operates at extremely small ranges (a few centimeters), leading to designs that sense close proximity rather than location.

Tactile sensors are used in robot manipulation to reduce uncertainty regarding hand-object pose estimation. However, existing sensor technologies tend to be bulky and provide signals that are difficult to interpret into actionable changes. Here, we achieve wireless tactile sensing with soft and conformable magnetic stickers that can be easily placed on objects within the robot's workspace. We embed a small magnetometer within the robot's fingertip that can localize to a magnetic sticker with sub-mm accuracy and enable the robot to pick up objects in the same place, in the same way, every time. In addition, we utilize the soft magnets' ability to exhibit magnetic field changes upon contact forces. We demonstrate the localization and force-feedback features with a 7-DOF Franka arm on deformable tool use and a key insertion task for applications in home, medical, and food robotics. By increasing the reliability of interaction with common tools, this approach to object localization and force sensing can improve robot manipulation performance for delicate, high-precision tasks.

Electronic skins and tactile sensors can provide the sense of touch to robotic manipulators. These sensing modalities complement existing long range optical sensors and can provide detailed information before and after contact. However, integration with existing systems can be challenging due to size constraints, the interface geometry, and restrictions of external wiring used to interface with the sensor. Here, we introduce a low-profile, wireless electronic skin for direct integration with existing robotic manipulators. The flexible electronic skin combines pressure, optical proximity sensing, and a micro-LIDAR device in a small, low profile package. Each of the sensors are characterized individually and the system is demonstrated on Robonaut 2, an anthropomorphic robot designed to work in environments designed for humans. We demonstrate the sensor can be used for contact sensing, mapping of local unknown environments, and to provide medical monitoring during an emergency in a remote area.

Soft robots are capable of inherently safer interactions with their environment than rigid robots since they can mechanically deform in response to unanticipated stimuli. However, their complex mechanics can make planning and control difficult, particularly with tasks such as locomotion. In this work, we present a mobile and untethered underwater crawling soft robot, PATRICK, paired with a testbed that demonstrates closed-loop locomotion planning. PATRICK is inspired by the brittle star, with five flexible legs actuated by a total of 20 shape-memory alloy (SMA) wires, providing a rich variety of possible motions via its large input space. We propose a motion planning infrastructure based on a simple set of PATRICK's motion primitives, and provide experiments showing that the planner can command the robot to locomote to a goal state. These experiments contribute the first examples of closed-loop, state-space goal seeking of an underwater, untethered, soft crawling robot, and make progress towards full autonomy of soft mobile robotic systems.

Wearables have emerged as an increasingly promising interactive platform, imbuing the human body with always-available computational capabilities. This unlocks a wide range of applications, including discreet information access, health monitoring, fitness, and fashion. However, unlike previous platforms, wearable electronics require structural conformity, must be comfortable for the wearer, and should be soft, elastic, and aesthetically appealing. We envision a future where electronics can be temporarily attached to the body (like bandages or party masks), but in functional and aesthetically pleasing ways. Towards this vision, we introduce ElectroDermis, a fabrication approach that simplifies the creation of highly-functional and stretchable wearable electronics that are conformal and fully untethered by discretizing rigid circuit boards into individual components. These individual components are wired together using stretchable electrical wiring and assembled on a spandex blend fabric, to provide high functionality in a robust form-factor that is reusable. We describe our system in detail- including our fabrication parameters and its operational limits-which we hope researchers and practitioners can leverage. We describe a series of example applications that illustrate the feasibility and utility of our system. Overall, we believe ElectroDermis offers a complementary approach to wearable electronics-one that places value on the notion of impermanence (i.e., unlike tattoos and implants), better conforming to the dynamic nature of the human body.

3D printing offers significant potential in creating highly customized interactive and functional objects. However, at present ability to manufacture functional objects is limited by available materials (e.g., various polymers) and their process properties. For instance, many functional objects need stronger materials which may be satisfied with metal printers. However, to create wholly interactive devices, we need both conductors and insulators to create wiring, and electronic components to complete circuits. Unfortunately, the single material nature of metal printing, and its inherent high temperatures, preclude this. Thus, in 3D printed devices, we have had a choice of strong materials, or embedded interactivity, but not both. In this paper, we introduce a set of techniques we call FiberWire, which leverages a new commercially available capability to 3D print carbon fiber composite objects. These objects are light weight and mechanically strong, and our techniques demonstrate a means to embed circuitry for interactive devices within them. With FiberWire, we describe a fabrication pipeline takes advantage of laser etching and fiber printing between layers of carbon-fiber composite to form low resistance conductors, thereby enabling the fabrication of electronics directly embedded into mechanically strong objects. Utilizing the fabrication pipeline, we show a range of sensor designs, their performance characterization on these new materials and finally three fully printed example object that are both interactive and mechanically strong -- a bicycle handle bar with interactive controls, a swing and impact sensing golf club and an interactive game controller (Figure 1).

Tactile sensors have been increasingly used to support rigid robot grippers in object grasping and manipulation. However, rigid grippers are often limited in their ability to handle compliant, delicate, or irregularly shaped objects. In recent years, grippers made from soft and flexible materials have become increasingly popular for certain manipulation tasks, e.g., grasping, due to their ability to conform to the object shape without the need for precise control. Although promising, such soft robot grippers currently suffer from the lack of available sensing modalities. In this work, we introduce a soft and stretchable sensing skin and incorporate it into the two fingers of a shape-memory actuated soft gripper. The onboard sensing skin includes a 9-axis inertial measurement unit (IMU) and five discrete pressure sensors per finger. We use this sensorized soft gripper to study grasp success and stability of over 2585 grasps with various objects using several machine learning methods. Our experiments show that LSTMs were the most accurate predictors of grasp success and stability, compared to SVMs, FFNNs, and ST-HMP. We also evaluated the effects on performance of each sensor's data, and the success rates for individual objects. The results show that the accelerometer data of the IMUs has the largest contribution to the overall grasp prediction, which we attribute to its ability to detect precise movements of the gripper during grasping.

Progress in soft robotics depends on the integration of electronics for sensing, power regulation, and signal processing. Commercially available microelectronics satisfy these functions and are small enough to preserve the natural mechanics of the host system. Here, we present a method for incorporating microelectronic sensors and integrated circuits (ICs) into the elastomeric skin of a soft robot. The thin stretchable skin contains various solid-state electronics for orientation, pressure, proximity, and temperature sensing, and a microprocessor. The components are connected by thin-film copper traces wetted with eutectic gallium indium (EGaIn), a room temperature liquid metal alloy that allows the circuit to maintain conductivity as it deforms under mechanical loading. In this paper, we characterize the function of the individual sensors in air and water, discuss the integration of the microelectronic skin with a shape-memory actuated soft gripper, and demonstrate the sensorized soft gripper in conjunction with a 4 degree-of-freedom (DOF) robot arm.

Efforts in soft-matter sensing with liquid metal (LM) have largely focused on pressure, strain, and bend sensing by measuring changes in capacitance or resistance of LM circuits embedded in a soft elastomer. Here, we show that LM electronics can be used to combine pressure sensing with proximity detection and remote finger tracking - sensing modalities not previously demonstrated in a soft electronic skin (E-Skin). This is accomplished with a novel architecture composed of 9 capacitive sensors arranged in two layers and placed below a spiral-shaped pressure transducer. The E-skin is composed of eutectic gallium-indium (EGaIn) LM alloy and Pt-cured EcoFlex 0030 elastomer. The multi-modal sensor is able to detect the proximity and motion of a finger as it moves above the sensor and is able to detect touch and pressure once the finger comes into contact. Proximity sensing and contact pressure are distinguish by comparing changes in capacitance and resistance of the embedded LM circuitry. Measurements on a cm-scale prototype suggest proximity sensing with a range of 8.7 cm and maximum contact pressure of 110 kPa.

Clutches can be used to enhance the functionality of springs or actuators in robotic devices. Here we describe a lightweight, low-power clutch used to control spring engagement in an ankle exoskeleton. The clutch is based on electrostatic adhesion between thin electrode sheets coated with a dielectric material. Each electrode pair weighs 1.5 g, bears up to 100 N, and changes states in less than 30 ms. We placed clutches in series with elastomer springs to allow control of spring engagement, and placed several clutched springs in parallel to discretely adjust stiffness. By engaging different numbers of springs, the system produced six different levels of stiffness. Force at peak displacement ranged from 14 to 501 N, and the device returned 95% of stored mechanical energy. Each clutched spring element weighed 26 g. We attached one clutched spring to an ankle exoskeleton and used it to engage the spring only while the foot was on the ground during 150 consecutive walking steps. Peak torque was 7.3 N·m on an average step, and the device consumed 0.6 mW of electricity. Compared to other electrically-controllable clutches, this approach results in three times higher torque density and two orders of magnitude lower power consumption per unit torque. We anticipate this technology will be incorporated into exoskeletons that tune stiffness online and into new actuator designs that utilize many lightweight, low-power clutches acting in concert.

We propose iSkin, a novel class of skin-worn sensors for touch input on the body. iSkin is a very thin sensor overlay, made of biocompatible materials, and is flexible and stretchable. It can be produced in different shapes and sizes to suit various locations of the body such as the finger, forearm, or ear. Integrating capacitive and resistive touch sensing, the sensor is capable of detecting touch input with two levels of pressure, even when stretched by 30% or when bent with a radius of 0.5cm. Furthermore, iSkin supports single or multiple touch areas of custom shape and arrangement, as well as more complex widgets, such as sliders and click wheels. Recognizing the social importance of skin, we show visual design patterns to customize functional touch sensors and allow for a visually aesthetic appearance. Taken together, these contributions enable new types of on-body devices. This includes finger-worn devices, extensions to conventional wearable devices, and touch input stickers, all fostering direct, quick, and discreet input for mobile computing.

This paper proposes GeckoGripper, a novel soft, inflatable gripper based on the controllable adhesion mechanism of gecko-inspired micro-fiber adhesives, to pick-and-place complex and fragile non-planar or planar parts serially or in parallel. Unlike previous fibrillar structures that use peel angle to control the manipulation of parts, we developed an elastomer micro-fiber adhesive that is fabricated on a soft, flexible membrane, increasing the adaptability to non-planar three-dimensional (3D) geometries and controllability in adhesion. The adhesive switching ratio (the ratio between the maximum and minimum adhesive forces) of the developed gripper was measured to be around 204, which is superior to previous works based on peel angle-based release control methods. Adhesion control mechanism based on the stretch of the membrane and superior adaptability to non-planar 3D geometries enable the micro-fibers to pick-and-place various 3D parts as shown in demonstrations.

We introduce a soft-matter sensor that measures elastic pressure and shear deformation. The sensor is composed of a sheet of elastomer that is embedded with fluidic parallel-plate capacitors. When the elastomer is pressed or sheared, the electrodes of the embedded capacitors come closer together or slide past each other, respectively, leading to a change in capacitance. The magnitude and direction of the shear deformation is established by comparing the change in capacitance of multiple embedded capacitors. We characterize the soft sensor theoretically and experimentally. Experiments indicate that 2D shear and pressure deformation can be discriminated with approximately 500 μm and 5 kPa sensitivity, respectively. The theoretical predictions and experimental results are in reasonable agreement. We also propose improvements to the fabrication method in order to facilitate integration of soft-matter sensing with wearable electronics.

A proposed adaptive soft orthotic device performs motion sensing and production of assistive forces with a modular, pneumatically-driven, hyper-elastic composite. Wrapping the material around a joint will allow simultaneous motion sensing and active force response through shape and rigidity control. This monolithic elastomer sheet contains a series of miniaturized pneumatically-powered McKibben-type actuators that exert tension and enable adaptive rigidity control. The elastomer is embedded with conductive liquid channels that detect strain and bending deformations induced by the pneumatic actuators. In addition, the proposed system is modular and can be configured for a diverse range of motor tasks, joints, and human subjects. This modular functionality is accomplished with a decentralized network of self-configuring nodes that manage the collection of sensory data and the delivery of actuator feedback commands. This paper mainly describes the design of the soft orthotic device as well as actuator and sensor components. The characterization of the individual sensors, actuators, and the integrated device is also presented.

A hyperelastic, thin, transparent pressure sensitive keypad is fabricated by embedding a silicone rubber film with conductive liquid-filled microchannels. Applying pressure to the surface of the elastomer deforms the cross-section of underlying microchannels and changes the electrical resistance across the affected channels. Perpendicular conductive channels form a quasi-planar network within an elastomeric matrix that registers the location, intensity and duration of applied pressure. Pressing channel intersections of the keypad triggers one of twelve keys, allowing the user to write any combination of alphabetic letters. A 5% change in channel output voltage must be achieved to trigger a key. It is found that approximately 100 kPa of pressure is necessary to produce a 5% change in voltage across a conductive microchannel that is 20 microns in height and 200 microns in width. Sensitivity of the keypad is tunable via channel geometry and choice of elastomeric material.

We introduce a curvature sensor composed of a thin, transparent elastomer film (polydimethylsiloxane, PDMS) embedded with a microchannel of conductive liquid (eutectic Gallium Indium, eGaIn) and a sensing element. Bending the sensor exerts pressure on the embedded microchannel via the sensing element. Deformation of the cross-section of the microchannel leads to a change in electrical resistance. We demonstrate the functionality of the sensor through testing on a finger joint. The film is wrapped around a finger with the sensing element positioned on top of the knuckle. Finger bending both stretches the elastomer and exerts pressure on the sensing element, leading to an enhanced change in the electrical resistance. Because the sensor is soft (elastic modulus E ~ 1 MPa) and stretchable (>350%), it conforms to the host bending without interfering with the natural mechanics of motion. This sensor represents the first use of liquid-embedded elastomer electronics to monitor human or robotic motion.