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Zackory Erickson

43 accepted papers

2026

High Fidelity Capture, Reconstruction, and Transfer of Human Demonstrations for Robot-Assisted Bathing

Arjun Lakshmipathy, Jonathan P. King, Ethan Zuo, Rohit Satishkumar, Hongyi Chen, Jeffrey Ichnowski +3
RSS 2026poster

Despite the demand for robots in high-value clinical tasks like bathing, contemporary systems still lack the safety and reliability required for complex, sustained physical interaction with humans. A key challenge hindering the development of such systems is that collecting, understanding, and effec…

Cited by 0Source ↗Scholar ↗
2025

ArticuBot: Learning Universal Articulated Object Manipulation Policy via Large Scale Simulation

Yufei Wang, Ziyu Wang, Mino Nakura, Pratik Bhowal, Chia-Liang Kuo, Yi-Ting Chen +2
RSS 2025poster

This paper presents ArticuBot, in which a single learned policy enables a robotics system to open diverse categories of unseen articulated objects in the real world. This task has long been challenging for robotics due to the large variations in the geometry, size, and articulation types of such ob…

Cited by 0PDF ↗Scholar ↗
2025

CoRI: Communication of Robot Intent for Physical Human-Robot Interaction

Junxiang Wang, Emek Barış Küçüktabak, Rana Soltani Zarrin, Zackory Erickson
CoRL 2025poster

Clear communication of robot intent fosters transparency and interpretability in physical human-robot interaction (pHRI), particularly during assistive tasks involving direct human-robot contact. We introduce CoRI, a pipeline that automatically generates natural language communication of a robot's u…

Cited by 0Source ↗Scholar ↗
2025

DiSRT-In-Bed: Diffusion-Based Sim-to-Real Transfer Framework for In-Bed Human Mesh Recovery

Jing Gao, Ce Zheng, Laszlo A. Jeni, Zackory Erickson
CVPR 2025poster

In-bed human mesh recovery can be crucial and enabling for several healthcare applications, including sleep pattern monitoring, rehabilitation support, and pressure ulcer prevention. However, it is difficult to collect large real-world visual datasets in this domain, in part due to privacy and expen…

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2025

Force-Modulated Visual Policy for Robot-Assisted Dressing with Arm Motions

Alexis Yihong Hao, Yufei Wang, Navin Sriram Ravie, Bharath Hegde, David Held, Zackory Erickson
CoRL 2025poster

Robot-assisted dressing has the potential to significantly improve the lives of individuals with mobility impairments. To ensure an effective and comfortable dressing experience, the robot must be able to handle challenging deformable garments, apply appropriate forces, and adapt to limb movements t…

Cited by 0Source ↗Scholar ↗
2025

Geometric Red-Teaming for Robotic Manipulation

Divyam Goel, Yufei Wang, Tiancheng Wu, Guixiu Qiao, Pavel Piliptchak, David Held +1
CoRL 2025oral

Standard evaluation protocols in robotic manipulation typically assess policy performance over curated, in-distribution test sets, offering limited insight into how systems fail under plausible variation. We introduce a red-teaming framework that probes robustness through object-centric geometr…

Cited by 0Source ↗Scholar ↗
2025

Real-World Offline Reinforcement Learning from Vision Language Model Feedback

Sreyas Venkataraman, Yufei Wang, Ziyu Wang, Navin Sriram Ravie, Zackory Erickson, David Held
IROS 2025

Offline reinforcement learning can enable policy learning from pre-collected, sub-optimal datasets without online interactions. This makes it ideal for real-world robots and safety-critical scenarios, where collecting online data or expert demonstrations is slow, costly, and risky. However, most exi

Cited by 15Source ↗Scholar ↗
2025

RoboCAP: Robotic Classification and Precision Pouring of Diverse Liquids and Granular Media with Capacitive Sensing

Yexin Hu, Alexandra Gillespie, Akhil Padmanabha, Kavya Puthuveetil, Wesley Lewis, Karan Khokar +1
IROS 2025

Liquids and granular media (e.g., oats, rice, lentils) are pervasive throughout human environments, yet remain challenging for robots to sense and manipulate precisely. In this work, we present a systematic approach to integrating capacitive sensing within robotic end effectors, enabling robust sens

Cited by 2Source ↗Scholar ↗
2025

SkinGrip: An Adaptive Soft Robotic Manipulator with Capacitive Sensing for Whole-Limb Bed Bathing Assistance

Fukang Liu, Kavya Puthuveetil, Akhil Padmanabha, Karan Khokar, Zeynep Temel, Zackory Erickson
IROS 2025

Robotics presents a promising opportunity for enhancing bathing assistance, potentially to alleviate labor shortages and reduce care costs, while offering consistent and gentle care for individuals with physical disabilities. However, ensuring flexible and efficient cleaning of the human body poses

Cited by 3Source ↗Scholar ↗
2024

AdaFold: Adapting Folding Trajectories of Cloths via Feedback-Loop Manipulation

Alberta Longhini, Michael C. Welle, Zackory Erickson, Danica Kragic
RA-L 2024

We present AdaFold, a model-based feedback-loop framework for optimizing folding trajectories. AdaFold extracts a particle-based representation of cloth from RGB-D images and feeds back the representation to a model predictive control to re-plan folding trajectory at every time-step. A key component

Cited by 13Source ↗Scholar ↗
2024

BodyMAP - Jointly Predicting Body Mesh and 3D Applied Pressure Map for People in Bed

Abhishek Tandon, Anujraaj Goyal, Henry M. Clever, Zackory Erickson
CVPR 2024poster

Accurately predicting the 3D human posture and the pressure exerted on the body for people resting in bed visualized as a body mesh (3D pose & shape) with a 3D pressure map holds significant promise for healthcare applications particularly in the prevention of pressure ulcers. Current methods focus…

Cited by 5Source ↗PDF ↗code ↗Scholar ↗
2024

DiffTORI: Differentiable Trajectory Optimization for Deep Reinforcement and Imitation Learning

Weikang Wan, Ziyu Wang, Yufei Wang, Zackory Erickson, David Held
NeurIPS 2024spotlight

This paper introduces DiffTORI, which utilizes $\textbf{Diff}$erentiable $\textbf{T}$rajectory $\textbf{O}$ptimization as the policy representation to generate actions for deep $\textbf{R}$einforcement and $\textbf{I}$mitation learning. Trajectory optimization is a powerful and widely used algorithm…

Cited by 5Source ↗PDF ↗code ↗Scholar ↗
2024

EMGBench: Benchmarking Out-of-Distribution Generalization and Adaptation for Electromyography

Jehan Yang, Maxwell J. Soh, Vivianna Lieu, Douglas J Weber, Zackory Erickson
NeurIPS 2024poster

This paper introduces the first generalization and adaptation benchmark using machine learning for evaluating out-of-distribution performance of electromyography (EMG) classification algorithms. The ability of an EMG classifier to handle inputs drawn from a different distribution than the training d…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2024

Force-Constrained Visual Policy: Safe Robot-Assisted Dressing via Multi-Modal Sensing

Zhanyi Sun, Yufei Wang, David Held, Zackory Erickson
RA-L 2024

Robot-assisted dressing could profoundly enhance the quality of life of adults with physical disabilities. To achieve this, a robot can benefit from both visual and force sensing. The former enables the robot to ascertain human body pose and garment deformations, while the latter helps maintain safe

Cited by 23Source ↗Scholar ↗
2024

RL-VLM-F: Reinforcement Learning from Vision Language Foundation Model Feedback

Yufei Wang, Zhanyi Sun, Jesse Zhang, Zhou Xian, Erdem Biyik, David Held +1
ICML 2024poster

Reward engineering has long been a challenge in Reinforcement Learning (RL) research, as it often requires extensive human effort and iterative processes of trial-and-error to design effective reward functions. In this paper, we propose RL-VLM-F, a method that automatically generates reward function…

Cited by 58Source ↗PDF ↗code ↗Scholar ↗
2024

RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation

Yufei Wang, Zhou Xian, Feng Chen, Tsun-Hsuan Wang, Yian Wang, Katerina Fragkiadaki +3
ICML 2024poster

We present RoboGen, a generative robotic agent that automatically learns diverse robotic skills at scale via generative simulation. RoboGen leverages the latest advancements in foundation and generative models. Instead of directly adapting these models to produce policies or low-level actions, we ad…

Cited by 88Source ↗PDF ↗Scholar ↗
2023

Causal Confusion and Reward Misidentification in Preference-Based Reward Learning

Jeremy Tien, Jerry Zhi-Yang He, Zackory Erickson, Anca Dragan, Daniel S. Brown
ICLR 2023poster

Learning policies via preference-based reward learning is an increasingly popular method for customizing agent behavior, but has been shown anecdotally to be prone to spurious correlations and reward hacking behaviors. While much prior work focuses on causal confusion in reinforcement learning and b…

Cited by 59Source ↗PDF ↗Scholar ↗
2023

EDO-Net: Learning Elastic Properties of Deformable Objects from Graph Dynamics

Alberta Longhini, Marco Moletta, Alfredo Reichlin, Michael C. Welle, David Held, Zackory Erickson +1
ICRA 2023poster

We study the problem of learning graph dynamics of deformable objects that generalizes to unknown physical properties. Our key insight is to leverage a latent representation of elastic physical properties of cloth-like deformable objects that can be extracted, for example, from a pulling interaction…

Cited by 27Source ↗Scholar ↗
2023

Elastic Context: Encoding Elasticity for Data-driven Models of Textiles Elastic Context: Encoding Elasticity for Data-driven Models of Textiles

Alberta Longhini, Marco Moletta, Alfredo Reichlin, Michael C. Welle, Alexander Kravberg, Yufei Wang +3
ICRA 2023poster

Physical interaction with textiles, such as assistive dressing or household tasks, requires advanced dexterous skills. The complexity of textile behavior during stretching and pulling is influenced by the material properties of the yarn and by the textile's construction technique, which are often un…

Cited by 10Source ↗Scholar ↗
2023

HAT: Head-Worn Assistive Teleoperation of Mobile Manipulators

Akhil Padmanabha, Qin Wang, Daphne Han, Jashkumar Diyora, Kriti Kacker, Hamza Khalid +3
ICRA 2023poster

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…

Cited by 12Source ↗Scholar ↗
2023

One Policy to Dress Them All: Learning to Dress People with Diverse Poses and Garments

Yufei Wang, Zhanyi Sun, Zackory Erickson, David Held
RSS 2023poster

Robot-assisted dressing could benefit the lives of many people such as older adults and individuals with disabilities. Despite such potential, robot-assisted dressing remains a challenging task for robotics as it involves complex manipulation of deformable cloth in 3D space. Many prior works aim to…

Cited by 21Source ↗PDF ↗Scholar ↗
2023

Quantifying Assistive Robustness Via the Natural-Adversarial Frontier

Jerry Zhi-Yang He, Daniel S. Brown, Zackory Erickson, Anca Dragan
CoRL 2023poster

Our ultimate goal is to build robust policies for robots that assist people. What makes this hard is that people can behave unexpectedly at test time, potentially interacting with the robot outside its training distribution and leading to failures. Even just measuring robustness is a challenge. Adve…

Cited by 0Source ↗Scholar ↗
2023

Robust Body Exposure (RoBE): A Graph-Based Dynamics Modeling Approach to Manipulating Blankets Over People

Kavya Puthuveetil, Sasha Wald, Atharva Pusalkar, Pratyusha Karnati, Zackory Erickson
RA-L 2023

Robotic caregivers could potentially improve the quality of life of many who require physical assistance. However, in order to assist individuals who are lying in bed, robots must be capable of dealing with a significant obstacle: the blanket or sheet that will almost always cover the person's body.

Cited by 6Source ↗Scholar ↗
2023

SLURP! Spectroscopy of Liquids Using Robot Pre-Touch Sensing

Nathaniel Hanson, Wesley Lewis, Kavya Puthuveetil, Donelle Furline, Akhil Padmanabha, Taşlan Padir +1
ICRA 2023poster

Liquids and granular media are pervasive throughout human environments. Their free-flowing nature causes people to constrain them into containers. We do so with thousands of different types of containers made out of different materials with varying sizes, shapes, and colors. In this work, we present…

Cited by 10Source ↗code ↗Scholar ↗
2022

Bodies Uncovered: Learning to Manipulate Real Blankets Around People via Physics Simulations

Kavya Puthuveetil, Charles C. Kemp, Zackory Erickson
RA-L 2022

While robots present an opportunity to provide physical assistance to older adults and people with mobility impairments in bed, people frequently rest in bed with blankets that cover the majority of their body. To provide assistance for many daily self-care tasks, such as bathing, dressing, or ambul

Cited by 22Source ↗code ↗Scholar ↗
2022

CapSense: A Real-Time Capacitive Sensor Simulation Framework for Physical Human-Robot Interaction

Christian Schöffmann, Zackory Erickson, Hubert Zangl
RA-L 2022

This letter presents CapSense, a real-time open-source capacitive sensor simulation framework for robotic applications. CapSense provides raw data of capacitive proximity sensors based on a fast and efficient 3D finite-element method (FEM) implementation. The proposed framework is interfaced to off-

Cited by 2Source ↗Scholar ↗
2022

Learning Representations that Enable Generalization in Assistive Tasks

Jerry Zhi-Yang He, Zackory Erickson, Daniel S. Brown, Aditi Raghunathan, Anca Dragan
CoRL 2022poster

Recent work in sim2real has successfully enabled robots to act in physical environments by training in simulation with a diverse ``population'' of environments (i.e. domain randomization). In this work, we focus on enabling generalization in \emph{assistive tasks}: tasks in which the robot is acting…

Cited by 35Source ↗Scholar ↗
2022

ToolFlowNet: Robotic Manipulation with Tools via Predicting Tool Flow from Point Clouds

Daniel Seita, Yufei Wang, Sarthak J Shetty, Edward Yao Li, Zackory Erickson, David Held
CoRL 2022poster

Point clouds are a widely available and canonical data modality which convey the 3D geometry of a scene. Despite significant progress in classification and segmentation from point clouds, policy learning from such a modality remains challenging, and most prior works in imitation learning focus on le…

Cited by 59Source ↗Scholar ↗
2022

Visual Haptic Reasoning: Estimating Contact Forces by Observing Deformable Object Interactions

Yufei Wang, David Held, Zackory Erickson
RA-L 2022

Robotic manipulation of highly deformable cloth presents a promising opportunity to assist people with several daily tasks, such as washing dishes; folding laundry; or dressing, bathing, and hygiene assistance for individuals with severe motor impairments. In this letter, we introduce a formulation

Cited by 23Source ↗Scholar ↗
2020

Assistive Gym: A Physics Simulation Framework for Assistive Robotics

Zackory Erickson, Vamsee Gangaram, Ariel Kapusta, C. Karen Liu, Charles C. Kemp
ICRA 2020poster

Autonomous robots have the potential to serve as versatile caregivers that improve quality of life for millions of people worldwide. Yet, conducting research in this area presents numerous challenges, including the risks of physical interaction between people and robots. Physics simulations have bee…

Cited by 136Source ↗code ↗Scholar ↗
2020

Bodies at Rest: 3D Human Pose and Shape Estimation From a Pressure Image Using Synthetic Data

Henry M. Clever, Zackory Erickson, Ariel Kapusta, Greg Turk, Karen Liu, Charles C. Kemp
CVPR 2020oral

People spend a substantial part of their lives at rest in bed. 3D human pose and shape estimation for this activity would have numerous beneficial applications, yet line-of-sight perception is complicated by occlusion from bedding. Pressure sensing mats are a promising alternative, but training data…

Cited by 79PDF ↗code ↗Scholar ↗
2020

Learning to Collaborate From Simulation for Robot-Assisted Dressing

Alexander Clegg, Zackory Erickson, Patrick Grady, Greg Turk, Charles C. Kemp, C. Karen Liu
RA-L 2020

We investigated the application of haptic feedback control and deep reinforcement learning (DRL) to robot-assisted dressing. Our method uses DRL to simultaneously train human and robot control policies as separate neural networks using physics simulations. In addition, we modeled variations in human

Cited by 58Source ↗Scholar ↗
2020

Multimodal Material Classification for Robots using Spectroscopy and High Resolution Texture Imaging

Zackory Erickson, Eliot Xing, Bharat Srirangam, Sonia Chernova, Charles C. Kemp
IROS 2020poster

Material recognition can help inform robots about how to properly interact with and manipulate real-world objects. In this paper, we present a multimodal sensing technique, leveraging near-infrared spectroscopy and close-range high resolution texture imaging, that enables robots to estimate the mate…

Cited by 51Source ↗code ↗Scholar ↗
2018

3D Human Pose Estimation on a Configurable Bed from a Pressure Image

Henry M. Clever, Ariel Kapusta, Daehyung Park, Zackory Erickson, Yash Chitalia, Charles C. Kemp
IROS 2018poster

Robots have the potential to assist people in bed, such as in healthcare settings, yet bedding materials like sheets and blankets can make observation of the human body difficult for robots. A pressure-sensing mat on a bed can provide pressure images that are relatively insensitive to bedding materi…

Cited by 58Source ↗Scholar ↗
2018

Deep Haptic Model Predictive Control for Robot-Assisted Dressing

Zackory Erickson, Henry M. Clever, Greg Turk, C. Karen Liu, Charles C. Kemp
ICRA 2018poster

Robot-assisted dressing offers an opportunity to benefit the lives of many people with disabilities, such as some older adults. However, robots currently lack common sense about the physical implications of their actions on people. The physical implications of dressing are complicated by non-rigid g…

Cited by 121Source ↗Scholar ↗
2018

Tracking Human Pose During Robot-Assisted Dressing Using Single-Axis Capacitive Proximity Sensing

Zackory Erickson, Maggie Collier, Ariel Kapusta, Charles C. Kemp
RA-L 2018

Dressing is a fundamental task of everyday living and robots offer an opportunity to assist people with motor impairments. While several robotic systems have explored robot-assisted dressing, few have considered how a robot can manage errors in human pose estimation, or adapt to human motion in real

Cited by 44Source ↗Scholar ↗
2017

A multimodal execution monitor with anomaly classification for robot-assisted feeding

Daehyung Park, Hokeun Kim, Yuuna Hoshi, Zackory Erickson, Ariel Kapusta, Charles C. Kemp
IROS 2017poster

Activities of daily living (ADLs) are important for quality of life. Robotic assistance offers the opportunity for people with disabilities to perform ADLs on their own. However, when a complex semi-autonomous system provides real-world assistance, occasional anomalies are likely to occur. Robots th…

Cited by 84Source ↗Scholar ↗
2017

Learning to navigate cloth using haptics

Alexander Clegg, Wenhao Yu, Zackory Erickson, Jie Tan, C. Karen Liu, Greg Turk
IROS 2017poster

We present a controller that allows an armlike manipulator to navigate deformable cloth garments in simulation through the use of haptic information. The main challenge of such a controller is to avoid getting tangled in, tearing or punching through the deforming cloth. Our controller aggregates for…

Cited by 34Source ↗Scholar ↗
2017

Semi-Supervised Haptic Material Recognition for Robots using Generative Adversarial Networks

Zackory Erickson, Sonia Chernova, Charles C. Kemp
CoRL 2017

Material recognition enables robots to incorporate knowledge of material properties into their interactions with everyday objects. For example, material recognition opens up opportunities for clearer communication with a robot, such as "bring me the metal coffee mug", and recognizing plastic versus

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2017

What does the person feel? Learning to infer applied forces during robot-assisted dressing

Zackory Erickson, Alexander Clegg, Wenhao Yu, Greg Turk, C. Karen Liu, Charles C. Kemp
ICRA 2017poster

During robot-assisted dressing, a robot manipulates a garment in contact with a person's body. Inferring the forces applied to the person's body by the garment might enable a robot to provide more effective assistance and give the robot insight into what the person feels. However, complex mechanics…

Cited by 46Source ↗Scholar ↗
2016

Multimodal execution monitoring for anomaly detection during robot manipulation

Daehyung Park, Zackory Erickson, Tapomayukh Bhattacharjee, Charles C. Kemp
ICRA 2016

Online detection of anomalous execution can be valuable for robot manipulation, enabling robots to operate more safely, determine when a behavior is inappropriate, and otherwise exhibit more common sense. By using multiple complementary sensory modalities, robots could potentially detect a wider var

Cited by 97Source ↗Scholar ↗