|
Welcome! My name is Yunhai Han and I am a Robotics PhD student at Georgia Institute of Technology, advised under Prof. Harish Ravichandar. My research focus at Gatech is about structured robot learning. I am also honoured to be an awardee of Robotics PhD fellowship from Georgia Tech's Institute for Robotics and Intelligent Machines (IRIM). Before coming to Georgia Tech, I received my M.S. / B.S. in Mechanical Engineering from UCSD, 2021 and Yanshan University, 2019, respectively. I'm open to discussions and collaborations, so feel free to drop me an email if you are interested. Email / CV(Apr 2024) / Github / Google Scholar / Linkdin / Other Info |
|
|
I have a broad interest in robotics, especially robot learning for manipulation tasks under complex environments, i.e., at home. I enjoy developing algorithms for robotic applications and I would like to explore how robots could learn and perceive the world to achieve high-level automation skills in dynamical environments. My love to robotics stems from a famous Japanese anime series:Code Geass(コードギアス 反逆のルルーシュ). I was dreaming of building a macha when I was a high school student. |
|
Kelin Yu (CS M.S., 2022-2024, Now PhD at UMD CS) |
|
|
|
Yunhai Han, Zhenyang Chen, and Harish Ravichandar Under review by RA-L When human acquire physical skills (e.g., tennis) from experts, we tend to first learn from merely observing the expert. But this is often insufficient. We then engage in practice, where we try to emulate the expert and ensure that our actions produce similar effects on our environment. Inspired by this observation, we introduce Combining IMitation and Emulation for Motion Refinement (CIMER) -- a two-stage framework to learn dexterous prehensile manipulation skills from state-only observations. CIMER's first stage involves imitation: simultaneously encode the complex interdependent motions of the robot hand and the object in a structured dynamical system. This results in a reactive motion generation policy that provides a reasonable motion prior, but lacks the ability to reason about contact effects due to the lack of action labels. The second stage involves emulation: learn a motion refinement policy via reinforcement that adjusts the robot hand's motion prior such that the desired object motion is reenacted. CIMER is both task-agnostic (no task-specific reward design or shaping) and intervention-free (no additional teleoperated or labeled demonstrations). [arXiv][Webpage][Code] |
|
Kelin Yu*, Yunhai Han, Matthew Zhu, and Ye Zhao (* Yu is my master student advisee) Best Paper at NeurIPS Touch Processing Workshop & Poster at CoRL Deployable Workshop, 2023 In this work, we introduce "MimicTouch", a novel framework that mimics human’s tactile-guided control strategy. In this framework, we initially collect multi-modal tactile datasets from human demonstrators, incorporating human tactile-guided control strategies for task completion. The subsequent step involves instructing robots through imitation learning using multi-modal sensor data and retargeted human motions. To further mitigate the embodiment gap between humans and robots, we employ online residual reinforcement learning on the physical robot. This ongoing work will pave the way for a broader spectrum of tactile-guided robotic applications. [arXiv] |
|
Yunhai Han, Mandy Xie, Ye Zhao, and Harish Ravichandar Accepted by CoRL 2023 (Oral) This work enables to learn dexterous manipulation skills from a few pieces of demonstration data. Dexterous manipulation is known to be very complex due to the absence of well-studied analytical models and high DoFs. Most existing work addresses this challenge via training a Reinforcement learning (RL) agent with fine-tuned shaped rewards. Motivated by the fact that complex nonlinear dynamics underlie dexterous manipulation and Koopman operators are simple yet powerful control-theoretic structures that help represent complex nonlinear dynamics as linear systems in higher-dimensional spaces, we propose a Koopman operator-based framework (KODex) and demonstrate that it is surprisingly effective for learning dexterous manipulation tasks and offers a number of unique benefits. [arXiv][OpenReview][Webpage][Code] |
|
Michael E. Cao⋆, Jonas Warnke⋆, Yunhai Han, Xinpei Ni, Ye Zhao, and Samuel Coogan (* Equal contributions) Accepted by SSRR, 2022 This work introduces a high-level controller synthesis framework that enables teams of heterogeneous agents to assist each other in resolving environmental conflicts that appear at runtime. This conflict resolution method is built upon temporal-logic-based reactive synthesis to guarantee safety and task completion under specific environment assumptions. Additionally, we implement the proposed framework on a physical multi-agent robotic system to demonstrate its viability for real world applications. [arXiv][Video] |
|
Yunhai Han and Sonia Martínez Accepted by L-CSS and ACC, 2022 This work proposes a numerical method to verify differential privacy in estimation with performance guarantees. To achieve differential privacy, a mechanism (estimator) is turned into a stochastic mapping; which makes it hard to distinguish outputs produced by close inputs. While obtaining theoretical conditions that guarantee privacy may be possible, verifying these in practice can be hard. [arXiv][slides] |
|
Henrik Christensen, David Paz, Hengyuan Zhang, Dominique Meyer, Hao Xiang, Yunhai Han, Yuhan Liu, Andrew Liang, Zheng Zhong, Shiqi Tang Accepted by Autonomous Intelligent System, Springer, 2021 My teammates at AVL lab and I warpped up the technical details when we explored and developed robust autonomous car systems and architectures for mail-delivery and micro-transit applications on UCSD campus. In this paper, we first tell the details of the initial design experiments and the main lessons/short-comings from the deployments. Then, we present the various approaches to address these challenges. Finally, we outline our future work. My work for this journal paper mainly involves the auto-calibration system for urban autonomous driving. [Springer] |
|
Yunhai Han, Rahul Batra, Nathan Boyd, Tuo Zhao, Yu She, Seth Hutchinson, and Ye Zhao Under review by T-MECH 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. [arXiv] [Webpage] [Video] |
|
Yunhai Han, Fei Liu and Michael C. Yip Spotlight presentation at IROS Workshop, 2020 This framework continuously tracks the motion of manipulator and simulates the tissue deformation in presence of collision detection. The deformation energy can be computed for the control and planning task. [arXiv] [slides][Talk] |
|
Yunhai Han*, Yuhan Liu*, David, paz and Henrik Christensen (* Equal contributions) Accepted by ICRA, 2021 For use of cameras on an intelligent vehicle,driving over a major bump could challenge the calibration. It isthen of interest to do dynamic calibration. What structures canbe used for calibration? How about using traffic signs that yourecognize? In this paper an approach is presented for dynamic camera calibration based on recognition of stop signs. [arXiv] [Dataset] |
|
Fei Liu*, Zihan Li*, Yunhai Han, Jingpei Lu, Florian Richter and Michael C. Yip (* Equal contributions) Accepted by ICRA, 2021 Autonomy in robotic surgery is very challenging in unstructured environments, especially when interacting with soft tissues. In this work, we propose an online, continuous, registration method to bridge from 3D visual perception to position-based dynamics modeling of tissues. [arXiv][Video] |
|
|
|
RoboMaster The RoboMaster program is a platform for robotic competitions and academic exchange founded by Da-Jiang Innovations (DJI). Each team has to design and build a squad of multiple-purpose robots for skirmish combats. I was the vision group leader of YSU Eagle. My group were mainly responsible for the system design of visual components (including object tracking, range estimation and serial port communication) and the PID stability adjustment of the gimbal unit on the mobile tank (to prevent bumps and collisions during movement). [RoboMaster Overview][Our robots] |
|
ICRA-2021
AIM-2021
ICRA-2022
IROS-2022
ACC-2022
SSRR-2022
ICRA-2023 |
|
MAE145, Robotic Planning & Estimation, UCSD
MAE146, Introduction to ML Algorithms, UCSD |
|
Research Assistant, Georgia Institute of Technology |
|
Running: Once finished half-marathon (21km) within 2 hours |
|
|