Author name cluster

Tess Lee Hellebrekers

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.

10 papers

1 author row

ICRA Conference 2025 Conference Paper

AnySkin: Plug-and-Play Skin Sensing for Robotic Touch

Raunaq M. Bhirangi
Venkatesh Pattabiraman
Enes Erciyes
Yifeng Cao
Tess Lee Hellebrekers
Lerrel Pinto

While tactile sensing is widely accepted as an important and useful sensing modality, its use pales in comparison to other sensory modalities like vision and proprioception. AnySkin addresses the critical challenges that impede the use of tactile sensing - versatility, replaceability, and data reusability. Building on the simplistic design of ReSkin, and decoupling the sensing electronics from the sensing interface, AnySkin simplifies integration making it as straightforward as putting on a phone case and connecting a charger. Furthermore, AnySkin is the first uncalibrated tactile-sensor to report crossinstance generalizability of learned manipulation policies. To summarize, this work makes three key contributions: first, we introduce a streamlined fabrication process and a design tool for creating an adhesive-free, durable and easily replaceable magnetic tactile sensor; second, we characterize slip detection and policy learning with the AnySkin sensor; third, we demonstrate zero-shot generalization of models trained on one instance of AnySkin to new instances, and compare it with popular existing tactile solutions like DIGIT and ReSkin. Code, design files, and videos of policy experiments can be found on https://any-skin.github.io

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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 2024 Conference Paper

Hearing Touch: Audio-Visual Pretraining for Contact-Rich Manipulation

Jared Mejia
Victoria Dean
Tess Lee Hellebrekers
Abhinav Gupta 0001

Although pre-training on a large amount of data is beneficial for robot learning, current paradigms only perform large-scale pretraining for visual representations, whereas representations for other modalities are trained from scratch. In contrast to the abundance of visual data, it is unclear what relevant internet-scale data may be used for pretraining other modalities such as tactile sensing. Such pretraining becomes increasingly crucial in the low-data regimes common in robotics applications. In this paper, we address this gap by using contact microphones as an alternative tactile sensor. Our key insight is that contact microphones capture inherently audio-based information, allowing us to leverage large-scale audio-visual pretraining to obtain representations that boost the performance of robotic manipulation. To the best of our knowledge, our method is the first approach leveraging large-scale multisensory pre-training for robotic manipulation. For supplementary information including videos of real robot experiments, please see https://sites.google.com/view/hearing-touch.

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ICML Conference 2024 Conference Paper

Hierarchical State Space Models for Continuous Sequence-to-Sequence Modeling

Raunaq M. Bhirangi
Chenyu Wang
Venkatesh Pattabiraman
Carmel Majidi
Abhinav Gupta 0001
Tess Lee Hellebrekers
Lerrel Pinto

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.

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ICRA Conference 2023 Conference Paper

Linear Delta Arrays for Compliant Dexterous Distributed Manipulation

Sarvesh Patil
Tony Tao
Tess Lee Hellebrekers
Oliver Kroemer
F. Zeynep Temel

This paper presents a new type of distributed dexterous manipulator: delta arrays. Our delta array setup consists of 64 linearly-actuated delta robots with 3D-printed compliant linkages. Through the design of the individual delta robots, the modular array structure, and distributed communication and control, we study a wide range of in-plane and out-of-plane manipulations, as well as prehensile manipulations among subsets of neighboring delta robots. We also demonstrate dexterous manipulation capabilities of the delta array using reinforcement learning while leveraging compliance. Our evaluations show that the resulting 192 DoF compliant robot is capable of performing various coordinated distributed manipulations of a variety of objects, including translation, alignment, prehensile squeezing, lifting, and grasping.

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IROS Conference 2020 Conference Paper

An Untethered Brittle Star-Inspired Soft Robot for Closed-Loop Underwater Locomotion

Zach J. Patterson
Andrew P. Sabelhaus
Keene Chin
Tess Lee Hellebrekers
Carmel Majidi

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.

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IROS Conference 2020 Conference Paper

Localization and Force-Feedback with Soft Magnetic Stickers for Precise Robot Manipulation

Tess Lee Hellebrekers
Kevin Zhang 0002
Manuela Veloso
Oliver Kroemer
Carmel Majidi

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.

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IROS Conference 2019 Conference Paper

Predicting Grasp Success with a Soft Sensing Skin and Shape-Memory Actuated Gripper

Julian Zimmer
Tess Lee Hellebrekers
Tamim Asfour
Carmel Majidi
Oliver Kroemer

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.

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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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IROS Conference 2015 Conference Paper

Highly stretchable optical sensors for pressure, strain, and curvature measurement

Celeste To
Tess Lee Hellebrekers
Yong-Lae Park

Recent advances in soft sensors using microfluidic liquid conductors enabled sensing of large deformation of soft structures. However, the use of liquids as conductive media carries a risk of leakage in many cases. Furthermore, it could be harmful when exposed to the human body in certain applications. To address these issues, a different sensing mechanism was proposed: highly stretchable optical sensors that could detect multiple modes of deformation. The method of operation involves a simple waveguide and its housing which are both made of silicone elastomer. The soft waveguide is coated with a thin gold reflective layer to encapsulate light propagating internally, with an light-emitting diode (LED) and a photodiode embedded at each end. When the sensor is stretched, compressed, or bent, micro-cracks within the reflective layer form and allow part of the light to escape, resulting in optical power losses in the light transmission. In this paper, we describe the design and fabrication of the proposed soft sensors. A prototype was created and characterized for pressure, strain, and curvature up to 350 kPa, 90%, and 0. 12 mm −1, respectively, showing promising results of reasonable repeatability and linearity in certain ranges.

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