Author name cluster

Ioannis Kymissis

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

IROS Conference 2025 Conference Paper

Compact LED-Based Displacement Sensing for Robot Fingers

Amr El-Azizi
Sharfin Islam
Pedro Piacenza
Kai Jiang
Ioannis Kymissis
Matei Ciocarlie

In this paper, we introduce a sensor designed for robotic fingers which can provide information on the displacements induced by external forces. Our sensor uses LEDs to sense the displacement between two plates connected by a transparent elastomer; when a force is applied to the finger, the elastomer displaces and the LED signals change. We show that using LEDs as both light emitters and receivers in this context provides high sensitivity, allowing such an emitter and receiver pairs to detect very small displacements. We characterize the standalone performance of the sensor by testing the ability of a supervised learning model to predict complete force and torque data from its raw signals, and obtain a mean error between 0. 05 and 0. 07 N across the three directions of force applied to the finger. Our method allows for compact packaging (fitting at the base of a finger) with no amplification electronics, low cost manufacturing, easy integration into a complete hand, and high overload shear forces and bending torques, suggesting future applicability to complete manipulation tasks.

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IROS Conference 2025 Conference Paper

VibeCheck: Using Active Acoustic Tactile Sensing for Contact-Rich Manipulation

Kaidi Zhang
Do-Gon Kim
Eric T. Chang
Hua-Hsuan Liang
Zhanpeng He
Kathryn Lampo
Philippe Wu
Ioannis Kymissis

The acoustic response of an object can reveal a lot about its global state, for example its material properties or the extrinsic contacts it is making with the world. In this work, we build an active acoustic sensing gripper equipped with two piezoelectric fingers: one for generating signals, the other for receiving them. By sending an acoustic vibration from one finger to the other through an object, we gain insight into an object’s acoustic properties and contact state. We use this system to classify objects, estimate grasping position, estimate poses of internal structures, and classify the types of extrinsic contacts an object is making with the environment. Using our contact type classification model, we tackle a standard long-horizon manipulation problem: peg insertion. We use a simple simulated transition model based on the performance of our sensor to train an imitation learning policy that is robust to imperfect predictions from the classifier. We finally demonstrate the policy on a UR5 robot with active acoustic sensing as the only feedback. Videos can be found at https://roamlab.github.io/vibecheck.

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ICRA Conference 2024 Conference Paper

An Investigation of Multi-feature Extraction and Super-resolution with Fast Microphone Arrays

Eric T. Chang
Runsheng Wang
Peter Ballentine
Jingxi Xu 0002
Trey Smith
Brian Coltin
Ioannis Kymissis
Matei Ciocarlie

In this work, we use MEMS microphones as vibration sensors to simultaneously classify texture and estimate contact position and velocity. Vibration sensors are an important facet of both human and robotic tactile sensing, providing fast detection of contact and onset of slip. Microphones are an attractive option for implementing vibration sensing as they offer a fast response and can be sampled quickly, are affordable, and occupy a very small footprint. Our prototype sensor uses only a sparse array (8-9 mm spacing) of distributed MEMS microphones (<$1, 3. 76×2. 95×1. 10 mm) embedded under an elastomer. We use transformer-based architectures for data analysis, taking advantage of the microphones’ high sampling rate to run our models on time-series data as opposed to individual snapshots. This approach allows us to obtain 77. 3% average accuracy on 4-class texture classification (84. 2% when excluding the slowest drag velocity), 1. 8 mm mean error on contact localization, and 5. 6 mm/s mean error on contact velocity. We show that the learned texture and localization models are robust to varying velocity and generalize to unseen velocities. We also report that our sensor provides fast contact detection, an important advantage of fast transducers. This investigation illustrates the capabilities one can achieve with a MEMS microphone array alone, leaving valuable sensor real estate available for integration with complementary tactile sensing modalities.

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ICRA Conference 2017 Conference Paper

Accurate contact localization and indentation depth prediction with an optics-based tactile sensor

Pedro Piacenza
Weipeng Dang
Emily Hannigan
Jeremy Espinal
Ikram Hussain
Ioannis Kymissis
Matei Ciocarlie

Traditional methods to achieve high localization accuracy with tactile sensors usually use a matrix of miniaturized individual sensors distributed on the area of interest. This approach usually comes at a price of increased complexity in fabrication and circuitry, and can be hard to adapt for non planar geometries. We propose to use low cost optic components mounted on the edges of the sensing area to measure how light traveling through an elastomer is affected by touch. Multiple light emitters and receivers provide us with a rich signal set that contains the necessary information to pinpoint both the location and depth of an indentation with high accuracy. We demonstrate sub-millimeter accuracy on location and depth on a 20mm by 20mm active sensing area. Our sensor provides high depth sensitivity as a result of two different modalities in how light is guided through our elastomer. This method results in a low cost, easy to manufacture sensor. We believe this approach can be adapted to cover non-planar surfaces, simplifying future integration in robot skin applications.

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IROS Conference 2016 Conference Paper

Contact localization through spatially overlapping piezoresistive signals

Pedro Piacenza
Yuchen Xiao
Steve Park
Ioannis Kymissis
Matei Ciocarlie

Achieving high spatial resolution in contact sensing for robotic manipulation often comes at the price of increased complexity in fabrication and integration. One traditional approach is to fabricate a large number of taxels, each delivering an individual, isolated response to a stimulus. In contrast, we propose a method where the sensor simply consists of a continuous volume of piezoresistive elastomer with a number of electrodes embedded inside. We measure piezoresistive effects between all pairs of electrodes in the set, and count on this rich signal set containing the information needed to pinpoint contact location with high accuracy using regression algorithms. In our validation experiments, we demonstrate submillimeter median accuracy in locating contact on a 10mm by 16mm sensor using only four electrodes (creating six unique pairs). In addition to extracting more information from fewer wires, this approach lends itself to simple fabrication methods and makes no assumptions about the underlying geometry, simplifying future integration on robot fingers.

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