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<!DOCTYPE html>
<html lang="en">
<head>
<title>Kelin Yu</title>
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
<meta name="description" content="Kelin Yu is a Master student at Georgia Tech constructing learning tools for Robots.">
<meta name="keywords" content="Kelin Yu,Robotics,Computer Vision,Reinforcement Learning,Imitation Learning">
<link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.1.1/css/bootstrap.min.css" integrity="sha384-WskhaSGFgHYWDcbwN70/dfYBj47jz9qbsMId/iRN3ewGhXQFZCSftd1LZCfmhktB" crossorigin="anonymous">
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<body>
<div id="main">
<div id="main-content">
<div id="main-content-container" class="container">
<div class="row">
<div class="col-sm-12 col-md-3 col-avatar">
<img src="images/kelin.png" class="avatar" />
</div>
<div class="col-sm-12 col-md-9 col-info">
<div id="main-info-container" class="container">
<div class="row">
<div class="col-md-12 col-xl-6 col-name">
<div class="textbox-info">
<h1 class="name"><span>Kelin Yu</span></h1>
<p class="email">Email: kyu85 [at] umd [dot] edu <br>
<a href="cv.pdf">CV</a>
<a href="https://scholar.google.com/citations?user=zVdZJRwAAAAJ&hl=en">Google Scholar</a>
<a href="https://twitter.com/ColinYu14116982">Twitter/X</a>
<a href="https://github.com/ColinYu1">Github</a>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
<div id="main-more-container">
<div id="main-bio-container">
<p>
I am a second-year Ph.D. student in Computer Science at University of Maryland, where I work with
Prof. <a href="https://ruohangao.github.io/" target="_blank">Ruohan Gao</a>. I also closely work with Prof. <a href="https://prg.cs.umd.edu/" target="_blank">Yiannis Aloimonos</a> and Prof. <a href="https://tokekar.com/" target="_blank">Pratap Tokekar</a>.
I am interested in MultiSensory Robot Learning, Learning from Cross-embodiment Human Videos, and Robot Manipulation. <br /><br />
I received my master's degree in Computer Science at <a href="https://www.cc.gatech.edu/">Georgia Tech</a> in May 2024, advised by
Ph.D. mentor <a href="https://y8han.github.io/" target="_blank">Yunhai Han</a>,
Prof. <a href="https://www.me.gatech.edu/faculty/zhao" target="_blank">Ye Zhao</a> and
Prof. <a href="https://faculty.cc.gatech.edu/~danfei/" target="_blank">Danfei Xu</a>.
Previously, I obtained dual bachelor's degree in Electrical Engineering and Mathematics from Georgia Tech in 2022 and
was a Robotics Software Engineering intern at Amazon. <br /><br />
I'm open to discussions and collaborations, so feel free to drop me an email if you are interested.
</p>
</div>
<div id="news-container">
<h5 class="subtitle">News</h5>
<div class="news-scroller" role="region" aria-label="Latest updates" tabindex="0">
<ul class="news-list">
<li class="news-item">
<span class="date">2026/6</span>
One paper accepted by ECCV 2026
</li>
<li class="news-item">
<span class="date">2026/3</span>
Selected as one of the <a href="https://www.qualcomm.com/research/university-relations/innovation-fellowship/2026-north-america" target="_blank"><span class="highlight">Qualcomm Innovation Fellowship Finalist</span></a> with <a href="https://y8han.github.io/" target="_blank">Yunhai Han</a></span>
</li>
<li class="news-item">
<span class="date">2026/2</span>
LG-SAIL is accepted by TMLR with <span class="highlight">J2C certification</span>, will be presented at NeurIPS 2026</span>
</li>
<li class="news-item">
<span class="date">2026/2</span>
I am going to join Amazon Personal Robotics Group as a Research Intern in June 2026</span>
</li>
<li class="news-item">
<span class="date">2025/10</span>
Invited talk at Shanghai Jiaotong University
</li>
<li class="news-item">
<span class="date">2025/9</span>
Control is accepted by ICCV CDEL Workshop with
<span class="highlight">Oral Presentation</span> —
<a href="https://colinyu1.github.io/controltac/" target="_blank">link</a>
</li>
<li class="news-item">
<span class="date">2025/6</span>
GenFlowRL is accepted by ICCV 2025 —
<a href="https://colinyu1.github.io/genflowrl/" target="_blank">link</a>
</li>
<li class="news-item">
<span class="date">2025/5</span>
GenFlowRL is presented at ICRA FMNS Workshop with
<span class="highlight">Spotlight and Best Paper Nomination</span> —
<a href="https://colinyu1.github.io/genflowrl/" target="_blank">link</a>
</li>
<li class="news-item">
<span class="date">2025/4</span>
Invited talk at UMD Robotics Symposium —
<a href="https://robotics.umd.edu/symposium" target="_blank">link</a>
</li>
<li class="news-item">
<span class="date">2024/8</span>
Graduated from Georgia Tech and started my Ph.D. at UMD with Prof. Ruohan Gao
</li>
<li class="news-item">
<span class="date">2024/8</span>
MimicTouch accepted by CoRL 2024 —
<a href="https://arxiv.org/abs/2310.16917" target="_blank">link</a>
</li>
<li class="news-item">
<span class="date">2024/4</span>
One paper published at IEEE T-Mech —
<a href="https://ieeexplore.ieee.org/document/10552075" target="_blank">link</a>
</li>
<li class="news-item">
<span class="date">2023/12</span>
MimicTouch won the <span class="highlight">Best Paper</span> at NeurIPS TouchProcessing Workshop —
<a href="https://sites.google.com/view/mimictouch/" target="_blank">link</a>
</li>
<li class="news-item">
<span class="date">2023/12</span>
Invited oral talk at NeurIPS TouchProcessing Workshop —
<a href="https://sites.google.com/view/mimictouch/" target="_blank">link</a>
</li>
</ul>
</div>
</div>
<div id="mentoring-collab-container">
<h5 class="subtitle">Mentored Students</h5>
<ul class="people-list">
<li><a href="https://dongyuluo.github.io/" target="_blank">Dongyu Luo</a> — Undergraduate, HKU, 2025–</li>
<li><a href="https://haodezhang.github.io/" target="_blank">Haode Zhang</a> — Undergraduate, SJTU, 2025–</li>
</ul>
<h5 class="subtitle move-up">External Collaborators (Past & Present)</h5>
<ul class="people-list">
<li><a href="https://y8han.github.io/" target="_blank">Yunhai Han</a> — GT Robotics Ph.D. in Prof. Harish Ravichandar's Lab</li>
<li><a href="https://tx-leo.github.io/" target="_blank">Zhi (Leo) Wang</a> — UMD CS Ph.D. in Prof. Yiannis Aloimonos's Lab</li>
<li><a href="https://sheng-eatamath.github.io/" target="_blank">Sheng Zhang</a> — UMD CS Ph.D. in Prof. Heng Huang's Lab</li>
<li><a href="https://amirshahid.github.io/" target="_blank">Amir-Hossein Shahidzadeh</a> — UMD CS Ph.D. in Prof. Yiannis Aloimonos's Lab</li>
<li><a href="https://sites.google.com/view/shuocheng" target="_blank">Shuo Cheng</a> — GT CS Ph.D. in Prof. Danfei Xu's Lab</li>
<li>Qixian Wang — UIUC ME Ph.D. in Prof. Mattia Gazzola's Lab</li>
<li><a href="https://sites.google.com/view/vaibhavsaxena" target="_blank">Vaibhav Saxena</a> — GT CS Ph.D. in Prof. Danfei Xu's Lab</li>
</ul>
</div>
<div id="main-pub-container">
<h5 class="subtitle">Publications</h5>
<p class="legend">🟨 Highlighted entries are <b>representative works</b> </p>
<p class="subtitle-aux"><span class="note">(* indicates equal contribution)</span></p>
<div id="main-pub-card-container" class="hide">
<!-- NEW: Afford2Act entry (representative) -->
<!-- OmniTacTune (first; representative) -->
<div class="pub-card rep" data-year="2026" data-selected="true">
<div class="row">
<div class="col-l col-xs-12 col-lg-3">
<img src="images/omnitact.jpg" onerror="this.style.display='none'" width="100%"/>
</div>
<div class="col-r col-xs-12 col-lg-9">
<div class="pub-card-body">
<h5 class="title">OmniTacTune: Policy-Agnostic Real-World RL for Tactile Residual Adaptation of Visual Policies </h5>
<h6 class="authors">
<u>Kelin Yu*</u>,
<a href="https://haodezhang.github.io/">Haode Zhang*</a>,
<a href="https://harishravichandar.com/">Harish Ravichandar</a>,
<a href="https://y8han.github.io/">Yunhai Han</a>,
<a href="https://ruohangao.github.io/">Ruohan Gao</a>,
<a> (Haode is my undergraduate mentee)</a>
</h6>
<p class="info">
<span class="conference">In Submission</span> <br>
<a href="https://colinyu1.github.io/omnitactune-site/static/paper.pdf">Paper</a> /
<a href="https://colinyu1.github.io/omnitactune-site/">Website</a>
</p>
OmniTacTune uses a two-stage RL design that first warm-starts tactile-aware learning from autonomous base-policy rollouts, and then learns a lightweight tactile residual policy through online interaction. This design requires no offline tactile demonstrations or base-policy finetuning, making tactile adaptation efficient and low-cost. OmniTacTune generalizes across diverse contact-rich tasks, visual base policies from both human videos and teleoperated demos, and tactile representations. Across four contact-rich real-world tasks, it improves weak visual base policies from 5-40% success to 85-100% within 40-80 minutes, demonstrating an efficient way to adapt tactile feedback to scalable visual robot policies.
</div>
</div>
</div>
</div>
<!-- ControlTac (co-first; representative) -->
<div class="pub-card rep" data-year="2024" data-selected="true">
<div class="row">
<div class="col-l col-xs-12 col-lg-3">
<img src="images/controltac.jpg" width="100%"/>
</div>
<div class="col-r col-xs-12 col-lg-9">
<div class="pub-card-body">
<h5 class="title">ControlTac: Scaling Tactile Data with Physically Controlled Tactile Image Generation </h5>
<h6 class="authors">
<a href="https://dongyuluo.github.io/">Dongyu Luo*</a>,
<u>Kelin Yu*</u>,
<a href="https://amirshahid.github.io/">Amir-Hossein Shahidzadeh</a>,
<a href="https://users.umiacs.umd.edu/~fermulcm/">Cornelia Fermuler</a>,
<a href="https://robotics.umd.edu/clark/faculty/350/Yiannis-Aloimonos">Yiannis Aloimonos</a>,
<a href="https://ruohangao.github.io/">Ruohan Gao</a>,
<a> (Dongyu is my undergraduate mentee)</a>
</h6>
<p class="info">
<span class="conference">ICCV CDEL Workshop 2025 (<font color="red"> Oral </font>)</span> <br>
<a href="https://arxiv.org/abs/2510.01433">Paper</a> /
<a href="https://dongyuluo.github.io/controltac/">Website</a> /
<span class="conference">In Submission</span>
</p>
Vision-based tactile sensing has been widely used in perception, reconstruction, and robotic manipulation. However, collecting large-scale tactile data remains costly due to the localized nature of sensor-object interactions and inconsistencies across sensor instances. Existing approaches to scaling tactile data, such as simulation and free-form tactile generation, often suffer from unrealistic output and poor transferability to downstream tasks. To address this, we propose ControlTac, a two-stage controllable framework that generates realistic tactile images conditioned on a single reference tactile image, contact force, and contact position.
</div>
</div>
</div>
</div>
<!-- HumanEgo -->
<div class="pub-card rep" data-year="2026" data-selected="true">
<div class="row">
<div class="col-l col-xs-12 col-lg-3">
<img src="images/humanego.jpg" onerror="this.style.display='none'" width="100%"/>
</div>
<div class="col-r col-xs-12 col-lg-9">
<div class="pub-card-body">
<h5 class="title">HumanEgo: Zero-Shot Robot Learning from Minutes of Human Egocentric Videos </h5>
<h6 class="authors">
<a href="https://tx-leo.github.io/">Zhi Wang</a>,
<a href="https://bottle101.github.io/">Botao He</a>,
<u>Kelin Yu</u>,
<a href="https://sjlee.cc/">Seungjae Lee</a>,
<a href="https://ruohangao.github.io/">Ruohan Gao</a>,
<a href="https://furong-huang.com/">Furong Huang</a>,
<a href="https://robotics.umd.edu/clark/faculty/350/Yiannis-Aloimonos">Yiannis Aloimonos</a>
</h6>
<p class="info">
<a href="https://arxiv.org/abs/2605.24934">Paper</a> /
<a href="https://humanego-ai.github.io/">Website</a> /
<a href="https://github.com/HumanEgo-ai/HumanEgo">Code</a> /
<a href="https://youtu.be/0BMRwF9HACQ">Video</a> /
<span class="conference">In Submission</span>
</p>
HumanEgo learns robot manipulation policies from minutes of raw human egocentric videos by lifting demonstrations into entity-level hand-object interaction representations and training a flow matching policy with dense auxiliary objectives. The framework is robot-data-free, hardware-agnostic, data-efficient, and zero-shot transferable across different robot setups.
</div>
</div>
</div>
</div>
<!-- OdorSpaces -->
<div class="pub-card" data-year="2026" data-selected="true">
<div class="row">
<div class="col-l col-xs-12 col-lg-3">
<img src="images/fire.jpg" onerror="this.style.display='none'" width="100%"/>
</div>
<div class="col-r col-xs-12 col-lg-9">
<div class="pub-card-body">
<h5 class="title">OdorSpaces: Visual-Olfactory Embodied Navigation in 3D Environments </h5>
<h6 class="authors">
<a href="https://changyeon2.github.io/">Changyeon Lee</a>,
<u>Kelin Yu</u>,
<a>Zheng Wei</a>,
<a>Shuna Ni</a>,
<a href="https://ruohangao.github.io/">Ruohan Gao</a>
</h6>
<p class="info">
<span class="conference">In Submission</span> <br>
</p>
We introduce OdorSpaces, a 3D visual-olfactory navigation framework that augments Habitat-Sim with physics-based odor dynamics simulated by FDS (Fire Dynamics Simulator). OdorSpaces defines two physically distinct navigation scenarios: a fire scenario, where buoyant carbon dioxide (CO<sub>2</sub>) rises and forms upper-region odor fields, and a gas-leak scenario, where dense propane gas (C<sub>3</sub>H<sub>8</sub>) settles and spreads near the floor. We train multimodal reinforcement learning agents that use visual observations and olfactory measurements, and show that they outperform chemotaxis, biomimetic plume tracking, and history-based odor-gradient estimation baselines across both scenarios.
</div>
</div>
</div>
</div>
<!-- SceneOrchestra -->
<div class="pub-card" data-year="2026" data-selected="true">
<div class="row">
<div class="col-l col-xs-12 col-lg-3">
<img src="images/1781806586152.jpg" onerror="this.onerror=null;this.src='1781806586152.jpg'" width="100%"/>
</div>
<div class="col-r col-xs-12 col-lg-9">
<div class="pub-card-body">
<h5 class="title">SceneOrchestra: Efficient Agentic 3D Scene Synthesis via Full Tool-Call Trajectory Generation </h5>
<h6 class="authors">
<a>Yun He</a>,
<u>Kelin Yu</u>,
<a>Matthias Zwicker</a>
</h6>
<p class="info">
<span class="conference">ECCV 2026</span> <br>
<a href="https://arxiv.org/pdf/2604.19907" target="_blank">Paper</a>
</p>
We propose SceneOrchestra, a trainable orchestration framework that optimizes the tool-call execution flow and eliminates the step-by-step review loop, improving both efficiency and output quality. SceneOrchestra consists of an orchestrator and a discriminator, which we fine-tune with a two-phase training strategy.
</div>
</div>
</div>
</div>
<div class="pub-card" data-year="2025" data-selected="true">
<div class="row">
<div class="col-l col-xs-12 col-lg-3">
<img src="images/afford2act.png" onerror="this.style.display='none'" width="100%"/>
</div>
<div class="col-r col-xs-12 col-lg-9">
<div class="pub-card-body">
<h5 class="title">Afford2Act: Affordance-Guided Automatic Keypoint Selection for Generalizable and Lightweight Robotic Manipulation </h5>
<h6 class="authors">
<a href="https://anukritisinghh.github.io/">Anukriti Singh</a>,
<a>Kasra Torshizi</a>,
<a>Khuzema Habib</a>,
<u>Kelin Yu</u>,
<a href="https://ruohangao.github.io/">Ruohan Gao</a>,
<a href="https://tokekar.com/">Pratap Tokekar</a>
</h6>
<p class="info">
<a href="https://arxiv.org/abs/2505.20498">Paper</a> /
<a href="https://afford2act.github.io/">Website</a> /
<span class="conference">In Submision</span> <br>
</p>
AFFORD2ACT, an affordance-guided framework that distills a minimal set of semantic 2D keypoints from a text prompt and a single image. AFFORD2ACT follows a three-stage pipeline: affordance filtering, category-level keypoint construction, and transformer-based policy learning with embedded gating to reason about the most relevant keypoints, yielding a compact 38-dimensional state policy that can be trained in 15 minutes, which performs well in real-time without proprioception or dense representations.
</div>
</div>
</div>
</div>
<!-- Continual Robot Learning (MOVED to 3rd; UPDATED to TMLR 2026; workshops removed; highlighted) -->
<div class="pub-card rep" data-year="2024" data-selected="true">
<div class="row">
<div class="col-l col-xs-12 col-lg-3">
<img src="images/lgsail.jpg" width="100%"/>
</div>
<div class="col-r col-xs-12 col-lg-9">
<div class="pub-card-body">
<h5 class="title">Continual Robot Learning via Language-Guided Skill Acquisition </h5>
<h6 class="authors">
<a>Zhaoyi Li*</a>,
<u>Kelin Yu*</u>,
<a href="https://sites.google.com/view/shuocheng">Shuo Cheng*</a>,
<a href="https://faculty.cc.gatech.edu/~danfei/">Danfei Xu</a>
</h6>
<p class="info">
<span class="conference">TMLR 2026 (<font color="red"> J2C Certification, Top 6.3%</font>), present at NeurIPS 2026</span></span> <br>
<a href="https://openreview.net/forum?id=oYRNxxGN9u&referrer=%5BAuthor%20Console%5D(%2Fgroup%3Fid%3DTMLR%2FAuthors%23your-submissions)"> Openreview</a> /
<a href="https://sites.google.com/view/continuallearning">Website</a>
</p>
We developed LG-SAIL (Language Models Guided Sequential, Adaptive, and Incremental Robot Learning), a framework that leverages Large Language Models (LLMs) to harmoniously integrate TAMP and DRL for continuous skill learning in long-horizon tasks. Our framework achieves automatic task decomposition, operator creation, and dense reward generation for efficiently acquiring the desired skills.
</div>
</div>
</div>
</div>
<!-- GenFlowRL (first; representative) -->
<div class="pub-card rep" data-year="2024" data-selected="true">
<div class="row">
<div class="col-l col-xs-12 col-lg-3">
<img src="images/main.png" width="100%"/>
</div>
<div class="col-r col-xs-12 col-lg-9">
<div class="pub-card-body">
<h5 class="title">GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning </h5>
<h6 class="authors">
<u>Kelin Yu*</u>,
<a href="https://sheng-eatamath.github.io/">Sheng Zhang*</a>,
<a>Harshit Soora</a>,
<a href="https://furong-huang.com/">Furong Huang</a>,
<a href="https://www.cs.umd.edu/~heng/">Heng Huang</a>,
<a href="https://tokekar.com/">Pratap Tokekar</a>,
<a href="https://ruohangao.github.io/">Ruohan Gao</a>
</h6>
<p class="info">
<span class="conference">ICCV 2025</span> <br>
<span class="conference">ICRA FMNS Workshop 2025 (<font color="red"> Spotlight, Best Paper Nomination </font>)</span> <br>
<a href="https://arxiv.org/abs/2508.11049">Paper</a> /
<a href="https://colinyu1.github.io/genflowrl/">Website</a> /
<a href="https://youtu.be/ohhBsjapZpE">Video</a>
</p>
Recent advances have demonstrated the potential of video generation models to guide robot learning by deriving effective robot actions through inverse dynamics. However, these methods heavily depend on the quality of generated data and struggle with fine-grained manipulation due to the lack of environment feedback. While video-based reinforcement learning improves policy robustness, it remains constrained by the artifacts of generated video and the challenges of collecting large-scale in-domain robot datasets for training diffusion models. Motivated by the above, we propose GenFlowRL, which derives shaped rewards from generated flow trained from cross-embodiment datasets.
</div>
</div>
</div>
</div>
<!-- MimicTouch (first; representative) -->
<div class="pub-card rep" data-year="2024" data-selected="true">
<div class="row">
<div class="col-l col-xs-12 col-lg-3">
<img src="data/projects/mimictouch.png" width="100%"/>
</div>
<div class="col-r col-xs-12 col-lg-9">
<div class="pub-card-body">
<h5 class="title">MimicTouch: Leveraging Multi-modal Human Tactile Demonstrations for Contact-rich Manipulation </h5>
<h6 class="authors">
<u>Kelin Yu*</u>,
<a href="https://y8han.github.io/">Yunhai Han*</a>,
<a>Qixian Wang</a>,
<a href="https://sites.google.com/view/vaibhavsaxena">Vaibhav Saxena</a>,
<a href="https://faculty.cc.gatech.edu/~danfei/">Danfei Xu</a>,
<a href="https://sites.google.com/site/yezhaout">Ye Zhao</a>
</h6>
<p class="info">
<span class="conference">CoRL 2024</span> <br>
<span class="conference">NeurIPS Touch Processing Workshop 2023 (<font color="red"> Best Paper Award </font>)</span> <br>
<span class="conference">CoRL Deployable Workshop 2023</span> <br>
<a href="https://arxiv.org/abs/2310.16917">Paper</a> /
<a href="https://sites.google.com/view/mimictouch/%E9%A6%96%E9%A1%B5">Website</a> /
<a href="https://youtu.be/RWHoZOUtQvg?si=OXFG8drB90v7rZ8L">Video</a>
</p>
We introduce MimicTouch, a novel framework for learning policies directly from demonstrations provided by human users with their hands. The key innovations are i) a human tactile data collection system which collects multi-modal tactile dataset for learning human's tactile-guided control strategy, ii) an imitation learning-based framework for learning human's tactile-guided control strategy through such data, and iii) an online residual RL framework to bridge the embodiment gap between the human hand and the robot gripper. Through comprehensive experiments, we highlight the efficacy of utilizing human's tactile-guided control strategy to resolve contact-rich manipulation tasks.
</div>
</div>
</div>
</div>
<!-- Vision-Tactile Transformer (co-first; representative) -->
<div class="pub-card rep" data-year="2024" data-selected="true">
<div class="row">
<div class="col-l col-xs-12 col-lg-3">
<img src="data/projects/transformer.png" width="100%"/>
</div>
<div class="col-r col-xs-12 col-lg-9">
<div class="pub-card-body">
<h5 class="title">Learning Generalizable Vision-Tactile Robotic Grasping Strategy for Deformable Objects via Transformer </h5>
<h6 class="authors">
<a href="https://y8han.github.io/">Yunhai Han*</a>,
<u>Kelin Yu*</u>,
<a>Rahul Batra</a>,
<a>Nathan Boyd</a>,
<a>Chaitanya Mehta</a>,
<a href="https://www.isye.gatech.edu/users/tuo-zhao">Tuo Zhao</a>,
<a href="https://www.purduemars.com/">Yu She</a>,
<a href="https://faculty.cc.gatech.edu/~seth/">Seth Hutchinson</a>,
<a href="https://sites.google.com/site/yezhaout">Ye Zhao</a>
</h6>
<p class="info">
<span class="conference">Transactions on Mechatronics 2024 (Long Version)</span> <br>
<span class="conference">AIM 2023 (Short Version)</span> <br>
<a href="https://ieeexplore.ieee.org/document/10552075">Paper</a> /
<a href="https://github.com/GTLIDAR/DeformableObjectsGrasping/tree/master/src/grasping_framework">Code</a>
</p>
Reliable robotic grasping with deformable objects remains a challenging task due to underactuated contact interactions with a gripper, unknown object dynamics, and variable object geometries. In this study, we propose a Transformer-based robot grasping framework for rigid grippers that leverage tactile information from a GelSight sensor for safe object grasping. The Transformer network learns physical feature embeddings from visual & tactile feedback and predict a final grasp through a multilayer perceptron (MLP) with grasping strength. Using these predictions, the gripper is commanded with an optimal force for safe grasping tasks.
</div>
</div>
</div>
</div>
<h5 class="subtitle">Work Experience<div data-year="2023" data-selected="true">
<div class="pub-card" data-year="2023" data-selected="true">
<div class="row">
<div class="col-l col-xs-12 col-lg-3">
<img src="data/projects/Amazon.png" width="100%"/>
</div>
<div class="col-r col-xs-12 col-lg-9">
<div class="pub-card-body">
<h5 class="title">Robotics Software Engineering intern
<p class="info">
<span class="conference">Amazon Robotics AI</span> /
</p>
Designed and built Calibration Drift Detector for our Industrial manipulator with Python, Open3D, OpenCV, and machine learning classifier.
Each multi-pick detected as a single pick costs 12$, and MEP/DEP package costs $0.05. My system saves potential thousands of dollars every day.
Used AWS tool (S3) to get past images and point clouds and implemented advanced computer vision algorithms and applied ML classifier to detect calibration drift.
</div>
</div>
</div>
</div>
</div>
<h5 class="subtitle">Awards<div data-year="2023" data-selected="true">
<div class="pub-card" data-year="2023" data-selected="true">
<div class="row">
<a><span class="highlight">Qualcomm Innovation Fellowship Finalist 2026</span></a>,
</div>
<div class="row">
<a><span class="highlight">ICRA FMNS Workshop Best Paper Nomination 2025</span></a>,
</div>
<div class="row">
<a>Dean’s Fellowship, University of Maryland, College Park, 2024-2026</a>,
</div>
<div class="row">
<a><span class="highlight">NeurIPS TouchProcessing Workshop Best Paper Award 2023</span></a>,
</div>
<div class="row">
<a>CoRL Travel Grant 2023</a>,
</div>
<div class="row">
<a>Graduate with Highest Honor at Georgia Tech 2022</a>,
</div>
</div>
</div>
<h5 class="subtitle">Professional Service<div data-year="2023" data-selected="true">
<div class="pub-card" data-year="2023" data-selected="true">
<div class="row">
<a>ICLR 2025, 2026</a>,
Reviewer
</div>
<div class="row">
<a>RA-L</a>,
Reviewer
</div>
<div class="row">
<a>ICCV 2025</a>,
Reviewer
</div>
<div class="row">
<a>ECCV 2026</a>,
Reviewer
</div>
<div class="row">
<a>NeurIPS 2024, 2025</a>,
Reviewer
</div>
<div class="row">
<a>RSS 2025</a>,
Reviewer
</div>
<div class="row">
<a>CoRL 2026</a>,
Reviewer
</div>
<div class="row">
<a>ICML 2025</a>,
Reviewer
</div>
<div class="row">
<a>ICRA 2025</a>,
Reviewer
</div>
<div class="row">
<a>CoRL Deployable Workshop, 2023</a>,
Reviewer
</div>
</div>
</div>
<h5 class="subtitle">Teaching Experience<div data-year="2023" data-selected="true">
<div class="pub-card" data-year="2023" data-selected="true">
<div class="row">
<a>CMSC 848M: Multimodal Computer Vision, University of Maryland</a>,
Spring 2025
<a>CMSC 421, Introduction to Artificial Intelligence, University of Maryland</a>,
Fall 2024
<a>CS 4476/6476, Computer Vision, Georgia Tech</a>,
Spring 2024, Fall 2023, Spring 2023, Fall 2022
<a>ECE 3741, Instrumentation and Electronics Laboratory, Georgia Tech</a>,
Spring 2021
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