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Jared Grinberg

Publications and source records attributed to Jared Grinberg.

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Multimodal Sensing for Robot-Assisted Sub-Tissue Feature Detection in Physiotherapy Palpation

Robotic palpation relies on force sensing, but force signals in soft-tissue environments are variable and cannot reliably reveal subtle subsurface features. We present a compact multimodal sensor that integrates high-resolution vision-based tactile imaging with a 6-axis force-torque sensor. In experiments on silicone phantoms with diverse subsurface tendon geometries, force signals alone frequently produce ambiguous responses, while tactile images reveal clear structural differences in presence, diameter, depth, crossings, and multiplicity. Yet accurate force tracking remains essential for maintaining safe, consistent contact during physiotherapeutic interaction. Preliminary results show that combining tactile and force modalities enables robust subsurface feature detection and controlled robotic palpation.

cs.RO

Contact Sensing via Joint Torque Sensors and a Force/Torque Sensor for Legged Robots

This paper presents a method for detecting and localizing contact along robot legs using distributed joint torque sensors and a single hip-mounted force-torque (FT) sensor using a generalized momentum-based observer framework. We designed a low-cost strain-gauge-based joint torque sensor that can be installed on every joint to provide direct torque measurements, eliminating the need for complex friction models and providing more accurate torque readings than estimation based on motor current. Simulation studies on a floating-based 2-DoF robot leg verified that the proposed framework accurately recovers contact force and location along the thigh and shin links. Through a calibration procedure, our torque sensor achieved an average 96.4% accuracy relative to ground truth measurements. Building upon the torque sensor, we performed hardware experiments on a 2-DoF manipulator, which showed sub-centimeter contact localization accuracy and force errors below 0.2 N.

cs.RO

3D Steering and Localization in Pipes and Burrows using an Externally Steered Soft Growing Robot

Navigation and inspection in confined environments, such as tunnels and pipes, pose significant challenges for existing robots due to limitations in maneuverability and adaptability to varying geometries. Vine robots, which are soft growing continuum robots that extend their length through soft material eversion at their tip, offer unique advantages due to their ability to navigate tight spaces, adapt to complex paths, and minimize friction. However, existing vine robot designs struggle with navigation in manmade and natural passageways, with branches and sharp 3D turns. In this letter, we introduce a steerable vine robot specifically designed for pipe and burrow environments. The robot features a simple tubular body and an external tip mount that steers the vine robot in three degrees of freedom by changing the growth direction and, when necessary, bracing against the wall of the pipe or burrow. Our external tip steering approach enables: (1) active branch selection in 3D space with a maximum steerable angle of 51.7{\deg}, (2) navigation of pipe networks with radii as small as 2.5 cm, (3) a compliant tip enabling navigation of sharp turns, and (4) real-time 3D localization in GPS-denied environments using tip-mounted sensors and continuum body odometry. We describe the forward kinematics, characterize steerability, and demonstrate the system in a 3D pipe system as well as a natural animal burrow.

cs.RO