Author name cluster

Jessica Yin

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers

1 author row

ICRA Conference 2025 Conference Paper

Learning In-Hand Translation Using Tactile Skin with Shear and Normal Force Sensing

Jessica Yin
Haozhi Qi
Jitendra Malik
James H. Pikul
Mark Yim
Tess Lee Hellebrekers

Recent progress in reinforcement learning (RL) and tactile sensing has significantly advanced dexterous manipulation. However, these methods often utilize simplified tactile signals due to the gap between tactile simulation and the real world. We introduce a sensor model for tactile skin that enables zero-shot sim-to-real transfer of ternary shear and binary normal forces. Using this model, we develop an RL policy that leverages sliding contact for dexterous inhand translation. We conduct extensive real-world experiments to assess how tactile sensing facilitates policy adaptation to various unseen object properties and robot hand orientations. We demonstrate that our 3-axis tactile policies consistently outperform baselines that use only shear forces, only normal forces, or only proprioception. Videos and details available on the project website.

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ICRA Conference 2025 Conference Paper

Proximity and Visuotactile Point Cloud Fusion for Contact Patches in Extreme Deformation

Jessica Yin
Paarth Shah
Naveen Kuppuswamy
Andrew Beaulieu
Avinash Uttamchandani
Alejandro M. Castro
James H. Pikul
Russ Tedrake

Visuotactile sensors are a popular tactile sensing strategy due to high-fidelity estimates of local object geometry. However, existing algorithms for processing raw sensor inputs to useful intermediate signals such as contact patches struggle in high-deformation regimes. This is due to physical constraints imposed by sensor hardware and small-deformation assumptions used by mechanics-based models. In this work, we propose a fusion algorithm for proximity and visuotactile point clouds for contact patch segmentation, entirely independent from membrane mechanics. This algorithm exploits the synchronous, high spatial resolution proximity and visuotactile modalities enabled by an extremely deformable, selectively transmissive soft membrane, which uses visible light for visuotactile sensing and infrared light for proximity depth. We evaluate our contact patch algorithm in low ( $\mathbf{1 0 \%}$ ), medium ( $\mathbf{6 0 \%}$ ), and high $(100 \%+)$ strain states. We compare our method against three baselines: proximity-only, tactile-only, and a first principles mechanics model. Our approach outperforms all baselines with an average RMSE under 2. 8 mm of the contact patch geometry across all strain ranges. We demonstrate our contact patch algorithm in four applications: varied stiffness membranes, torque and shear-induced wrinkling, closed loop control, and pose estimation.

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ICRA Conference 2025 Conference Paper

Steerable Tape-Spring Needle for Autonomous Sharp Turns Through Tissue

Omar Abdoun
Davin Tjandra
Katie Yin
Pablo Kurzan
Jessica Yin
Mark Yim

Steerable needles offer a minimally invasive method to deliver treatment to hard-to-reach tissue regions. We introduce a new class of tape-spring steerable needles capable of sharp turns ranging from 15 to 150 degrees with a turn radius as low as 3 mm, which minimizes surrounding tissue damage. In this work, we derive and experimentally validate a geometric model for our steerable needle design. We evaluate both manual and robotic steering of the needle along a Dubins path in 7 kPa and 13 kPa tissue phantoms, simulating our target clinical application in healthy and unhealthy liver tissue. We conduct experiments to measure needle robustness to stiffness transitions between non-homogeneous tissues. We demonstrate progress towards clinical use with needle tip tracking via ultrasound imaging, navigation around anatomical obstacles, and integration with a robotic autonomous steering system.

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IROS Conference 2022 Conference Paper

Electroadhesive Clutches for Programmable Shape Morphing of Soft Actuators

Gregory M. Campbell
Jessica Yin
Yuyang Song
Umesh Gandhi
Mark Yim
James H. Pikul

Soft robotic actuators are safe and adaptable devices with inherent compliance, which makes them attractive for manipulating delicate and complex objects. Researchers have integrated stiff materials into soft actuators to increase their force capacity and direct their deformation. However, these embedded materials have largely been pre-prescribed and static, which constrains the actuators to a predetermined range of motion. In this work, electroadhesive (EA) clutches integrated on a single-chamber soft pneumatic actuator (SPA) provide local programmable stiffness modulation to control the actuator deformation. We show that activating different clutch patterns inflates a silicone membrane into pyramidal, round, and plateau shapes. Curvatures from these shapes are combined during actuation to apply forces on both a 3. 7 g and 820 g object along five different degrees of freedom (DoF). The actuator workspace is up to 12 mm for light objects. Clutch deactivation, which results in local elastomeric expansion, rapidly applies forces up to 3. 2 N to an object resting on the surface and launches a 3. 7 g object in controlled directions. The actuator also rotates a heavier, 820 g, object by 5 degrees and rapidly restores it to horizontal alignment after clutch deactivation. This actuator is fully powered by a 5 V battery, AA battery, DC-DC transformer, and 4. 5 V (63 g) DC air pump. These results demonstrate a first step towards realizing a soft actuator with high DoF shape change that preserves the inherent benefits of pneumatic actuation while gaining the electrical controllability and strength of EA clutches. We envision such a system supplying human contact forces in the form of a low-profile sit-to-stand assistance device, bed-ridden patient manipulator, or other ergonomic mechanism.

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IROS Conference 2018 Conference Paper

Liquid Metal-Microelectronics Integration for a Sensorized Soft Robot Skin

Tess Lee Hellebrekers
Kadri Bugra Ozutemiz
Jessica Yin
Carmel Majidi

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.

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