View jack2148's full-sized avatar
1 follower · 0 following

    Block or report jack2148

    Block user

    Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

    You must be logged in to block users.

    Content in all repositories owned by your account will be closed.
    Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.
    Report abuse

    Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

    Report abuse
    jack2148/README.md

    Yeachan Kim

    Robotics · Robot Learning · Manipulation

    Robotics Engineering undergraduate at Hanyang University ERICA and undergraduate research intern at KITECH since July 2026. Current work focuses on teleoperation-based manipulation demonstrations, imitation learning, and data-efficient demonstration collection. Interested in hybrid robot-learning approaches that combine data-driven policies with robot kinematics and structured control.

    Email

    Current Research

    1. Teleoperation & Manipulation Learning

    OMY_FRANKA_TELEOP

    Built a teleoperation pipeline from a physical OMY-L100 leader to a Franka FR3 in MuJoCo, including Cartesian pose retargeting, 6D-to-7DoF damped least-squares inverse kinematics, and null-space control. This is a simulation-stage FR3 system; deployment to physical FR3 hardware is not claimed.

    2. Imitation Learning & Policy Analysis

    dp-act-policy-study

    Studying ACT and Diffusion Policy in LeRobot-based manipulation tasks, including Push-T analysis and FR3 MuJoCo experiments using teleoperation demonstrations. Current work investigates how execution strategy, trajectory coverage, and demonstration distribution affect policy performance.

    The Push-T study includes a controlled 50-episode execution-strategy ablation. FR3 results are reported separately as fixed-initial-condition pilot evaluations, not as a generalization benchmark.

    Research Interests

    My broader research interest is in robotic manipulation architectures that separate learning-based decision making from kinematics- and control-based execution, combining data-driven policies with structured robot control.

    Supporting Robotics Systems Work

    rgbd-object-pose-estimation — RGB-D manipulation perception using an Intel RealSense D455, YOLOv8-seg, CAD meshes, and FoundationPose. The verified mean mask mAP50 is 0.929 across three industrial-part classes; this is a segmentation metric, not 6D-pose accuracy.

    navigation_stack_lab — Real-hardware ROS 2 navigation and system integration with SLAM, EKF sensor fusion, Nav2, DWB/MPPI experiments, and person-following logic. MPPI computation and parameters were optimized to improve the control loop from approximately 5–6 Hz to a stable 20 Hz.

    Selected Achievements

    • 1st Place — 2026 Creative Comprehensive Design Competition
    • Bronze Award — College of Engineering Capstone Design Competition
    • Finalist — KAIST Mobility Challenge 2026
    • Undergraduate Paper Award — ICROS 2026

    Publications / Presentations

    • “Performance Comparison of DWB and MPPI Local Planners in ROS 2 Nav2 Environment” — ICROS 2026, poster, first author
    • “Multimodal Perception and Monitoring System for Indoor Security Patrol Robots” — ICROS 2026, poster, Undergraduate Paper Award

    Technical Areas

    ROS 2 · MuJoCo · LeRobot · ACT · Diffusion Policy · Python · C/C++ · Nav2 · SLAM · MPPI · DWB · RGB-D · YOLOv8 · FoundationPose

    Pinned Loading

    1. dp-act-policy-studydp-act-policy-studyPublic

      ACT and Diffusion Policy experiments for manipulation, from Push-T policy analysis to teleoperation-derived FR3 learning.

      Python 1

    2. OMY_FRANKA_TELEOPOMY_FRANKA_TELEOPPublic

      Physical OMY-L100 to MuJoCo Franka FR3 teleoperation with Cartesian retargeting, DLS IK, and null-space control.

      Python 1

    3. navigation_stack_labnavigation_stack_labPublic

      ROS 2 autonomous navigation and patrol system with SLAM, AMCL, Nav2, DWB/MPPI evaluation, and real-robot debugging.

      Python 1

    4. rgbd-object-pose-estimationrgbd-object-pose-estimationPublic

      RGB-D 6D object pose estimation pipeline using RealSense D455, YOLOv8-seg, FoundationPose, and ROS 2.

      Python 1

    , 'i'); if (__m === '*' || __re.test(location.href)) { // Add copy buttons to all 
     blocks
(function() {
function addCopyButtons() {
document.querySelectorAll('pre code').forEach(function(codeBlock) {
if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;
codeBlock.parentElement.setAttribute('data-copy-added', 'true');
var btn = document.createElement('button');
btn.textContent = 'Copy';
btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';
btn.onmouseover = function() { this.style.opacity = '1'; };
btn.onmouseout = function() { this.style.opacity = '0.7'; };
btn.onclick = function() {
navigator.clipboard.writeText(codeBlock.textContent).then(function() {
btn.textContent = 'Copied!';
setTimeout(function() { btn.textContent = 'Copy'; }, 1500);
});
};
codeBlock.parentElement.style.position = 'relative';
codeBlock.parentElement.appendChild(btn);
});
}
addCopyButtons();
// Re-run on dynamic content
var observer = new MutationObserver(addCopyButtons);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

    Skip to content
    View jack2148's full-sized avatar
    1
follower
 · 0
following

      
Block or report jack2148

      Block user
      
Prevent this user from interacting with your repositories and sending you notifications.
Learn more about blocking users.

      
You must be logged in to block users.

      
Content in all repositories owned by your account will be closed.

      
Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.

      Report abuse
      
Contact GitHub support about this user’s behavior.
Learn more about reporting abuse.

      Report abuse
      jack2148/README.md
      Yeachan Kim
      Robotics · Robot Learning · Manipulation
      Robotics Engineering undergraduate at Hanyang University ERICA and undergraduate research intern at KITECH since July 2026. Current work focuses on teleoperation-based manipulation demonstrations, imitation learning, and data-efficient demonstration collection. Interested in hybrid robot-learning approaches that combine data-driven policies with robot kinematics and structured control.
      Email
      Current Research
      1. Teleoperation & Manipulation Learning
      OMY_FRANKA_TELEOP
      Built a teleoperation pipeline from a physical OMY-L100 leader to a Franka FR3 in MuJoCo, including Cartesian pose retargeting, 6D-to-7DoF damped least-squares inverse kinematics, and null-space control. This is a simulation-stage FR3 system; deployment to physical FR3 hardware is not claimed.
      2. Imitation Learning & Policy Analysis
      dp-act-policy-study
      Studying ACT and Diffusion Policy in LeRobot-based manipulation tasks, including Push-T analysis and FR3 MuJoCo experiments using teleoperation demonstrations. Current work investigates how execution strategy, trajectory coverage, and demonstration distribution affect policy performance.
      The Push-T study includes a controlled 50-episode execution-strategy ablation. FR3 results are reported separately as fixed-initial-condition pilot evaluations, not as a generalization benchmark.
      Research Interests
      My broader research interest is in robotic manipulation architectures that separate learning-based decision making from kinematics- and control-based execution, combining data-driven policies with structured robot control.
      Supporting Robotics Systems Work
      rgbd-object-pose-estimation — RGB-D manipulation perception using an Intel RealSense D455, YOLOv8-seg, CAD meshes, and FoundationPose. The verified mean mask mAP50 is 0.929 across three industrial-part classes; this is a segmentation metric, not 6D-pose accuracy.
      navigation_stack_lab — Real-hardware ROS 2 navigation and system integration with SLAM, EKF sensor fusion, Nav2, DWB/MPPI experiments, and person-following logic. MPPI computation and parameters were optimized to improve the control loop from approximately 5–6 Hz to a stable 20 Hz.
      Selected Achievements
      • 1st Place — 2026 Creative Comprehensive Design Competition
      • Bronze Award — College of Engineering Capstone Design Competition
      • Finalist — KAIST Mobility Challenge 2026
      • Undergraduate Paper Award — ICROS 2026
      Publications / Presentations
      • “Performance Comparison of DWB and MPPI Local Planners in ROS 2 Nav2 Environment” — ICROS 2026, poster, first author
      • “Multimodal Perception and Monitoring System for Indoor Security Patrol Robots” — ICROS 2026, poster, Undergraduate Paper Award
      Technical Areas
      ROS 2 · MuJoCo · LeRobot · ACT · Diffusion Policy · Python · C/C++ · Nav2 · SLAM · MPPI · DWB · RGB-D · YOLOv8 · FoundationPose
      
Pinned
Loading
      1. dp-act-policy-studydp-act-policy-studyPublic
        
ACT and Diffusion Policy experiments for manipulation, from Push-T policy analysis to teleoperation-derived FR3 learning.

        Python
1

      2. OMY_FRANKA_TELEOPOMY_FRANKA_TELEOPPublic
        
Physical OMY-L100 to MuJoCo Franka FR3 teleoperation with Cartesian retargeting, DLS IK, and null-space control.

        Python
1

      3. navigation_stack_labnavigation_stack_labPublic
        
ROS 2 autonomous navigation and patrol system with SLAM, AMCL, Nav2, DWB/MPPI evaluation, and real-robot debugging.

        Python
1

      4. rgbd-object-pose-estimationrgbd-object-pose-estimationPublic
        
RGB-D 6D object pose estimation pipeline using RealSense D455, YOLOv8-seg, FoundationPose, and ROS 2.

        Python
1

      
, 'i');
if (__m === '*' || __re.test(location.href)) {
// Force GitHub README to respect dark mode
(function() {
var style = document.createElement('style');
style.textContent = '
.markdown-body {
color-scheme: dark light;
}
.markdown-body pre { background: #161b22 !important; }
.markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }
.markdown-body table th, .markdown-body table td { border-color: #30363d !important; }
.markdown-body img { background: #0d1117; }
.markdown-body blockquote { border-left-color: #8b949e; }
.markdown-body hr { border-color: #30363d; }
';
document.head.appendChild(style);
})();
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

      Skip to content
      View jack2148's full-sized avatar
      1
follower
 · 0
following

        
Block or report jack2148

        Block user
        
Prevent this user from interacting with your repositories and sending you notifications.
Learn more about blocking users.

        
You must be logged in to block users.

        
Content in all repositories owned by your account will be closed.

        
Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.

        Report abuse
        
Contact GitHub support about this user’s behavior.
Learn more about reporting abuse.

        Report abuse
        jack2148/README.md
        Yeachan Kim
        Robotics · Robot Learning · Manipulation
        Robotics Engineering undergraduate at Hanyang University ERICA and undergraduate research intern at KITECH since July 2026. Current work focuses on teleoperation-based manipulation demonstrations, imitation learning, and data-efficient demonstration collection. Interested in hybrid robot-learning approaches that combine data-driven policies with robot kinematics and structured control.
        Email
        Current Research
        1. Teleoperation & Manipulation Learning
        OMY_FRANKA_TELEOP
        Built a teleoperation pipeline from a physical OMY-L100 leader to a Franka FR3 in MuJoCo, including Cartesian pose retargeting, 6D-to-7DoF damped least-squares inverse kinematics, and null-space control. This is a simulation-stage FR3 system; deployment to physical FR3 hardware is not claimed.
        2. Imitation Learning & Policy Analysis
        dp-act-policy-study
        Studying ACT and Diffusion Policy in LeRobot-based manipulation tasks, including Push-T analysis and FR3 MuJoCo experiments using teleoperation demonstrations. Current work investigates how execution strategy, trajectory coverage, and demonstration distribution affect policy performance.
        The Push-T study includes a controlled 50-episode execution-strategy ablation. FR3 results are reported separately as fixed-initial-condition pilot evaluations, not as a generalization benchmark.
        Research Interests
        My broader research interest is in robotic manipulation architectures that separate learning-based decision making from kinematics- and control-based execution, combining data-driven policies with structured robot control.
        Supporting Robotics Systems Work
        rgbd-object-pose-estimation — RGB-D manipulation perception using an Intel RealSense D455, YOLOv8-seg, CAD meshes, and FoundationPose. The verified mean mask mAP50 is 0.929 across three industrial-part classes; this is a segmentation metric, not 6D-pose accuracy.
        navigation_stack_lab — Real-hardware ROS 2 navigation and system integration with SLAM, EKF sensor fusion, Nav2, DWB/MPPI experiments, and person-following logic. MPPI computation and parameters were optimized to improve the control loop from approximately 5–6 Hz to a stable 20 Hz.
        Selected Achievements
        • 1st Place — 2026 Creative Comprehensive Design Competition
        • Bronze Award — College of Engineering Capstone Design Competition
        • Finalist — KAIST Mobility Challenge 2026
        • Undergraduate Paper Award — ICROS 2026
        Publications / Presentations
        • “Performance Comparison of DWB and MPPI Local Planners in ROS 2 Nav2 Environment” — ICROS 2026, poster, first author
        • “Multimodal Perception and Monitoring System for Indoor Security Patrol Robots” — ICROS 2026, poster, Undergraduate Paper Award
        Technical Areas
        ROS 2 · MuJoCo · LeRobot · ACT · Diffusion Policy · Python · C/C++ · Nav2 · SLAM · MPPI · DWB · RGB-D · YOLOv8 · FoundationPose
        
Pinned
Loading
        1. dp-act-policy-studydp-act-policy-studyPublic
          
ACT and Diffusion Policy experiments for manipulation, from Push-T policy analysis to teleoperation-derived FR3 learning.

          Python
1

        2. OMY_FRANKA_TELEOPOMY_FRANKA_TELEOPPublic
          
Physical OMY-L100 to MuJoCo Franka FR3 teleoperation with Cartesian retargeting, DLS IK, and null-space control.

          Python
1

        3. navigation_stack_labnavigation_stack_labPublic
          
ROS 2 autonomous navigation and patrol system with SLAM, AMCL, Nav2, DWB/MPPI evaluation, and real-robot debugging.

          Python
1

        4. rgbd-object-pose-estimationrgbd-object-pose-estimationPublic
          
RGB-D 6D object pose estimation pipeline using RealSense D455, YOLOv8-seg, FoundationPose, and ROS 2.

          Python
1

        
, 'i');
if (__m === '*' || __re.test(location.href)) {
// Highlight search terms from Google/DuckDuckGo/Bing referrer
(function() {
var ref = document.referrer;
var terms = [];
if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {
var url = new URL(ref);
var q = url.searchParams.get('q') || url.searchParams.get('p');
if (q) {
terms = q.split(/\s+/).filter(function(t) { return t.length > 2; });
}
}
if (terms.length === 0) return;
var style = document.createElement('style');
style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';
document.head.appendChild(style);
function highlight(node) {
if (node.nodeType === 3) { // text node
var text = node.textContent;
var found = false;
terms.forEach(function(term) {
var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\]\\]/g, '\\') + ')', 'gi');
if (regex.test(text)) {
found = true;
var frag = document.createDocumentFragment();
var parts = text.split(regex);
parts.forEach(function(part, i) {
if (i % 2 === 0) {
frag.appendChild(document.createTextNode(part));
} else {
var span = document.createElement('span');
span.className = 'userscript-highlight';
span.textContent = part;
frag.appendChild(span);
}
});
node.parentNode.replaceChild(frag, node);
}
});
} else if (node.nodeType === 1 && node.childNodes) { // element
var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];
if (!skipTags.includes(node.tagName)) {
Array.from(node.childNodes).forEach(highlight);
}
}
}
highlight(document.body);
// Re-highlight on dynamic content
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1 || node.nodeType === 3) highlight(node);
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

        Skip to content
        View jack2148's full-sized avatar
        1
follower
 · 0
following

          
Block or report jack2148

          Block user
          
Prevent this user from interacting with your repositories and sending you notifications.
Learn more about blocking users.

          
You must be logged in to block users.

          
Content in all repositories owned by your account will be closed.

          
Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.

          Report abuse
          
Contact GitHub support about this user’s behavior.
Learn more about reporting abuse.

          Report abuse
          jack2148/README.md
          Yeachan Kim
          Robotics · Robot Learning · Manipulation
          Robotics Engineering undergraduate at Hanyang University ERICA and undergraduate research intern at KITECH since July 2026. Current work focuses on teleoperation-based manipulation demonstrations, imitation learning, and data-efficient demonstration collection. Interested in hybrid robot-learning approaches that combine data-driven policies with robot kinematics and structured control.
          Email
          Current Research
          1. Teleoperation & Manipulation Learning
          OMY_FRANKA_TELEOP
          Built a teleoperation pipeline from a physical OMY-L100 leader to a Franka FR3 in MuJoCo, including Cartesian pose retargeting, 6D-to-7DoF damped least-squares inverse kinematics, and null-space control. This is a simulation-stage FR3 system; deployment to physical FR3 hardware is not claimed.
          2. Imitation Learning & Policy Analysis
          dp-act-policy-study
          Studying ACT and Diffusion Policy in LeRobot-based manipulation tasks, including Push-T analysis and FR3 MuJoCo experiments using teleoperation demonstrations. Current work investigates how execution strategy, trajectory coverage, and demonstration distribution affect policy performance.
          The Push-T study includes a controlled 50-episode execution-strategy ablation. FR3 results are reported separately as fixed-initial-condition pilot evaluations, not as a generalization benchmark.
          Research Interests
          My broader research interest is in robotic manipulation architectures that separate learning-based decision making from kinematics- and control-based execution, combining data-driven policies with structured robot control.
          Supporting Robotics Systems Work
          rgbd-object-pose-estimation — RGB-D manipulation perception using an Intel RealSense D455, YOLOv8-seg, CAD meshes, and FoundationPose. The verified mean mask mAP50 is 0.929 across three industrial-part classes; this is a segmentation metric, not 6D-pose accuracy.
          navigation_stack_lab — Real-hardware ROS 2 navigation and system integration with SLAM, EKF sensor fusion, Nav2, DWB/MPPI experiments, and person-following logic. MPPI computation and parameters were optimized to improve the control loop from approximately 5–6 Hz to a stable 20 Hz.
          Selected Achievements
          • 1st Place — 2026 Creative Comprehensive Design Competition
          • Bronze Award — College of Engineering Capstone Design Competition
          • Finalist — KAIST Mobility Challenge 2026
          • Undergraduate Paper Award — ICROS 2026
          Publications / Presentations
          • “Performance Comparison of DWB and MPPI Local Planners in ROS 2 Nav2 Environment” — ICROS 2026, poster, first author
          • “Multimodal Perception and Monitoring System for Indoor Security Patrol Robots” — ICROS 2026, poster, Undergraduate Paper Award
          Technical Areas
          ROS 2 · MuJoCo · LeRobot · ACT · Diffusion Policy · Python · C/C++ · Nav2 · SLAM · MPPI · DWB · RGB-D · YOLOv8 · FoundationPose
          
Pinned
Loading
          1. dp-act-policy-studydp-act-policy-studyPublic
            
ACT and Diffusion Policy experiments for manipulation, from Push-T policy analysis to teleoperation-derived FR3 learning.

            Python
1

          2. OMY_FRANKA_TELEOPOMY_FRANKA_TELEOPPublic
            
Physical OMY-L100 to MuJoCo Franka FR3 teleoperation with Cartesian retargeting, DLS IK, and null-space control.

            Python
1

          3. navigation_stack_labnavigation_stack_labPublic
            
ROS 2 autonomous navigation and patrol system with SLAM, AMCL, Nav2, DWB/MPPI evaluation, and real-robot debugging.

            Python
1

          4. rgbd-object-pose-estimationrgbd-object-pose-estimationPublic
            
RGB-D 6D object pose estimation pipeline using RealSense D455, YOLOv8-seg, FoundationPose, and ROS 2.

            Python
1

          
, 'i');
if (__m === '*' || __re.test(location.href)) {
// Strip utm_, fbclid, gclid, etc. from all links on page
(function() {
var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',
'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',
'ref', 'ref_src', 'source', 'medium', 'campaign'];
function cleanUrl(url) {
try {
var u = new URL(url, window.location.origin);
var changed = false;
trackingParams.forEach(function(p) {
if (u.searchParams.has(p)) {
u.searchParams.delete(p);
changed = true;
}
});
return changed ? u.toString() : url;
} catch (e) {
return url;
}
}
function cleanLinks() {
document.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
cleanLinks();
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1) {
if (node.tagName === 'A') cleanLinks();
node.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

          Skip to content
          View jack2148's full-sized avatar
          1
follower
 · 0
following

            
Block or report jack2148

            Block user
            
Prevent this user from interacting with your repositories and sending you notifications.
Learn more about blocking users.

            
You must be logged in to block users.

            
Content in all repositories owned by your account will be closed.

            
Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.

            Report abuse
            
Contact GitHub support about this user’s behavior.
Learn more about reporting abuse.

            Report abuse
            jack2148/README.md
            Yeachan Kim
            Robotics · Robot Learning · Manipulation
            Robotics Engineering undergraduate at Hanyang University ERICA and undergraduate research intern at KITECH since July 2026. Current work focuses on teleoperation-based manipulation demonstrations, imitation learning, and data-efficient demonstration collection. Interested in hybrid robot-learning approaches that combine data-driven policies with robot kinematics and structured control.
            Email
            Current Research
            1. Teleoperation & Manipulation Learning
            OMY_FRANKA_TELEOP
            Built a teleoperation pipeline from a physical OMY-L100 leader to a Franka FR3 in MuJoCo, including Cartesian pose retargeting, 6D-to-7DoF damped least-squares inverse kinematics, and null-space control. This is a simulation-stage FR3 system; deployment to physical FR3 hardware is not claimed.
            2. Imitation Learning & Policy Analysis
            dp-act-policy-study
            Studying ACT and Diffusion Policy in LeRobot-based manipulation tasks, including Push-T analysis and FR3 MuJoCo experiments using teleoperation demonstrations. Current work investigates how execution strategy, trajectory coverage, and demonstration distribution affect policy performance.
            The Push-T study includes a controlled 50-episode execution-strategy ablation. FR3 results are reported separately as fixed-initial-condition pilot evaluations, not as a generalization benchmark.
            Research Interests
            My broader research interest is in robotic manipulation architectures that separate learning-based decision making from kinematics- and control-based execution, combining data-driven policies with structured robot control.
            Supporting Robotics Systems Work
            rgbd-object-pose-estimation — RGB-D manipulation perception using an Intel RealSense D455, YOLOv8-seg, CAD meshes, and FoundationPose. The verified mean mask mAP50 is 0.929 across three industrial-part classes; this is a segmentation metric, not 6D-pose accuracy.
            navigation_stack_lab — Real-hardware ROS 2 navigation and system integration with SLAM, EKF sensor fusion, Nav2, DWB/MPPI experiments, and person-following logic. MPPI computation and parameters were optimized to improve the control loop from approximately 5–6 Hz to a stable 20 Hz.
            Selected Achievements
            • 1st Place — 2026 Creative Comprehensive Design Competition
            • Bronze Award — College of Engineering Capstone Design Competition
            • Finalist — KAIST Mobility Challenge 2026
            • Undergraduate Paper Award — ICROS 2026
            Publications / Presentations
            • “Performance Comparison of DWB and MPPI Local Planners in ROS 2 Nav2 Environment” — ICROS 2026, poster, first author
            • “Multimodal Perception and Monitoring System for Indoor Security Patrol Robots” — ICROS 2026, poster, Undergraduate Paper Award
            Technical Areas
            ROS 2 · MuJoCo · LeRobot · ACT · Diffusion Policy · Python · C/C++ · Nav2 · SLAM · MPPI · DWB · RGB-D · YOLOv8 · FoundationPose
            
Pinned
Loading
            1. dp-act-policy-studydp-act-policy-studyPublic
              
ACT and Diffusion Policy experiments for manipulation, from Push-T policy analysis to teleoperation-derived FR3 learning.

              Python
1

            2. OMY_FRANKA_TELEOPOMY_FRANKA_TELEOPPublic
              
Physical OMY-L100 to MuJoCo Franka FR3 teleoperation with Cartesian retargeting, DLS IK, and null-space control.

              Python
1

            3. navigation_stack_labnavigation_stack_labPublic
              
ROS 2 autonomous navigation and patrol system with SLAM, AMCL, Nav2, DWB/MPPI evaluation, and real-robot debugging.

              Python
1

            4. rgbd-object-pose-estimationrgbd-object-pose-estimationPublic
              
RGB-D 6D object pose estimation pipeline using RealSense D455, YOLOv8-seg, FoundationPose, and ROS 2.

              Python
1

            
, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
(function() {
function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
if (btn && !btn.classList.contains('activated')) {
btn.click();
}
}
// Try immediately
tryTheater();
// Try after navigation (SPA)
var lastUrl = location.href;
setInterval(function() {
if (location.href !== lastUrl) {
lastUrl = location.href;
setTimeout(tryTheater, 500);
}
}, 1000);
// Also try on player load
var observer = new MutationObserver(tryTheater);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

            Skip to content
            View jack2148's full-sized avatar
            1
follower
 · 0
following

              
Block or report jack2148

              Block user
              
Prevent this user from interacting with your repositories and sending you notifications.
Learn more about blocking users.

              
You must be logged in to block users.

              
Content in all repositories owned by your account will be closed.

              
Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.

              Report abuse
              
Contact GitHub support about this user’s behavior.
Learn more about reporting abuse.

              Report abuse
              jack2148/README.md
              Yeachan Kim
              Robotics · Robot Learning · Manipulation
              Robotics Engineering undergraduate at Hanyang University ERICA and undergraduate research intern at KITECH since July 2026. Current work focuses on teleoperation-based manipulation demonstrations, imitation learning, and data-efficient demonstration collection. Interested in hybrid robot-learning approaches that combine data-driven policies with robot kinematics and structured control.
              Email
              Current Research
              1. Teleoperation & Manipulation Learning
              OMY_FRANKA_TELEOP
              Built a teleoperation pipeline from a physical OMY-L100 leader to a Franka FR3 in MuJoCo, including Cartesian pose retargeting, 6D-to-7DoF damped least-squares inverse kinematics, and null-space control. This is a simulation-stage FR3 system; deployment to physical FR3 hardware is not claimed.
              2. Imitation Learning & Policy Analysis
              dp-act-policy-study
              Studying ACT and Diffusion Policy in LeRobot-based manipulation tasks, including Push-T analysis and FR3 MuJoCo experiments using teleoperation demonstrations. Current work investigates how execution strategy, trajectory coverage, and demonstration distribution affect policy performance.
              The Push-T study includes a controlled 50-episode execution-strategy ablation. FR3 results are reported separately as fixed-initial-condition pilot evaluations, not as a generalization benchmark.
              Research Interests
              My broader research interest is in robotic manipulation architectures that separate learning-based decision making from kinematics- and control-based execution, combining data-driven policies with structured robot control.
              Supporting Robotics Systems Work
              rgbd-object-pose-estimation — RGB-D manipulation perception using an Intel RealSense D455, YOLOv8-seg, CAD meshes, and FoundationPose. The verified mean mask mAP50 is 0.929 across three industrial-part classes; this is a segmentation metric, not 6D-pose accuracy.
              navigation_stack_lab — Real-hardware ROS 2 navigation and system integration with SLAM, EKF sensor fusion, Nav2, DWB/MPPI experiments, and person-following logic. MPPI computation and parameters were optimized to improve the control loop from approximately 5–6 Hz to a stable 20 Hz.
              Selected Achievements
              • 1st Place — 2026 Creative Comprehensive Design Competition
              • Bronze Award — College of Engineering Capstone Design Competition
              • Finalist — KAIST Mobility Challenge 2026
              • Undergraduate Paper Award — ICROS 2026
              Publications / Presentations
              • “Performance Comparison of DWB and MPPI Local Planners in ROS 2 Nav2 Environment” — ICROS 2026, poster, first author
              • “Multimodal Perception and Monitoring System for Indoor Security Patrol Robots” — ICROS 2026, poster, Undergraduate Paper Award
              Technical Areas
              ROS 2 · MuJoCo · LeRobot · ACT · Diffusion Policy · Python · C/C++ · Nav2 · SLAM · MPPI · DWB · RGB-D · YOLOv8 · FoundationPose
              
Pinned
Loading
              1. dp-act-policy-studydp-act-policy-studyPublic
                
ACT and Diffusion Policy experiments for manipulation, from Push-T policy analysis to teleoperation-derived FR3 learning.

                Python
1

              2. OMY_FRANKA_TELEOPOMY_FRANKA_TELEOPPublic
                
Physical OMY-L100 to MuJoCo Franka FR3 teleoperation with Cartesian retargeting, DLS IK, and null-space control.

                Python
1

              3. navigation_stack_labnavigation_stack_labPublic
                
ROS 2 autonomous navigation and patrol system with SLAM, AMCL, Nav2, DWB/MPPI evaluation, and real-robot debugging.

                Python
1

              4. rgbd-object-pose-estimationrgbd-object-pose-estimationPublic
                
RGB-D 6D object pose estimation pipeline using RealSense D455, YOLOv8-seg, FoundationPose, and ROS 2.

                Python
1

              
, 'i');
if (__m === '*' || __re.test(location.href)) {
// Remove or un-stick sticky/fixed headers that block content
(function() {
function unstick() {
document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) {
if (el.style.position === 'fixed' || el.style.position === 'sticky' || getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {
el.style.position = 'static';
el.style.top = 'auto';
el.style.zIndex = 'auto';
}
});
}
unstick();
var observer = new MutationObserver(unstick);
observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });
})();
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

              Skip to content
              View jack2148's full-sized avatar
              1
follower
 · 0
following

                
Block or report jack2148

                Block user
                
Prevent this user from interacting with your repositories and sending you notifications.
Learn more about blocking users.

                
You must be logged in to block users.

                
Content in all repositories owned by your account will be closed.

                
Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.

                Report abuse
                
Contact GitHub support about this user’s behavior.
Learn more about reporting abuse.

                Report abuse
                jack2148/README.md
                Yeachan Kim
                Robotics · Robot Learning · Manipulation
                Robotics Engineering undergraduate at Hanyang University ERICA and undergraduate research intern at KITECH since July 2026. Current work focuses on teleoperation-based manipulation demonstrations, imitation learning, and data-efficient demonstration collection. Interested in hybrid robot-learning approaches that combine data-driven policies with robot kinematics and structured control.
                Email
                Current Research
                1. Teleoperation & Manipulation Learning
                OMY_FRANKA_TELEOP
                Built a teleoperation pipeline from a physical OMY-L100 leader to a Franka FR3 in MuJoCo, including Cartesian pose retargeting, 6D-to-7DoF damped least-squares inverse kinematics, and null-space control. This is a simulation-stage FR3 system; deployment to physical FR3 hardware is not claimed.
                2. Imitation Learning & Policy Analysis
                dp-act-policy-study
                Studying ACT and Diffusion Policy in LeRobot-based manipulation tasks, including Push-T analysis and FR3 MuJoCo experiments using teleoperation demonstrations. Current work investigates how execution strategy, trajectory coverage, and demonstration distribution affect policy performance.
                The Push-T study includes a controlled 50-episode execution-strategy ablation. FR3 results are reported separately as fixed-initial-condition pilot evaluations, not as a generalization benchmark.
                Research Interests
                My broader research interest is in robotic manipulation architectures that separate learning-based decision making from kinematics- and control-based execution, combining data-driven policies with structured robot control.
                Supporting Robotics Systems Work
                rgbd-object-pose-estimation — RGB-D manipulation perception using an Intel RealSense D455, YOLOv8-seg, CAD meshes, and FoundationPose. The verified mean mask mAP50 is 0.929 across three industrial-part classes; this is a segmentation metric, not 6D-pose accuracy.
                navigation_stack_lab — Real-hardware ROS 2 navigation and system integration with SLAM, EKF sensor fusion, Nav2, DWB/MPPI experiments, and person-following logic. MPPI computation and parameters were optimized to improve the control loop from approximately 5–6 Hz to a stable 20 Hz.
                Selected Achievements
                • 1st Place — 2026 Creative Comprehensive Design Competition
                • Bronze Award — College of Engineering Capstone Design Competition
                • Finalist — KAIST Mobility Challenge 2026
                • Undergraduate Paper Award — ICROS 2026
                Publications / Presentations
                • “Performance Comparison of DWB and MPPI Local Planners in ROS 2 Nav2 Environment” — ICROS 2026, poster, first author
                • “Multimodal Perception and Monitoring System for Indoor Security Patrol Robots” — ICROS 2026, poster, Undergraduate Paper Award
                Technical Areas
                ROS 2 · MuJoCo · LeRobot · ACT · Diffusion Policy · Python · C/C++ · Nav2 · SLAM · MPPI · DWB · RGB-D · YOLOv8 · FoundationPose
                
Pinned
Loading
                1. dp-act-policy-studydp-act-policy-studyPublic
                  
ACT and Diffusion Policy experiments for manipulation, from Push-T policy analysis to teleoperation-derived FR3 learning.

                  Python
1

                2. OMY_FRANKA_TELEOPOMY_FRANKA_TELEOPPublic
                  
Physical OMY-L100 to MuJoCo Franka FR3 teleoperation with Cartesian retargeting, DLS IK, and null-space control.

                  Python
1

                3. navigation_stack_labnavigation_stack_labPublic
                  
ROS 2 autonomous navigation and patrol system with SLAM, AMCL, Nav2, DWB/MPPI evaluation, and real-robot debugging.

                  Python
1

                4. rgbd-object-pose-estimationrgbd-object-pose-estimationPublic
                  
RGB-D 6D object pose estimation pipeline using RealSense D455, YOLOv8-seg, FoundationPose, and ROS 2.

                  Python
1

                
, 'i');
if (__m === '*' || __re.test(location.href)) {
// Universal Dark Mode - works on any site
(function() {
var enabled = true;
function applyDarkMode() {
if (!enabled) return;
// Create style element if it doesn't exist
var style = document.getElementById('universal-dark-mode-style');
if (!style) {
style = document.createElement('style');
style.id = 'universal-dark-mode-style';
document.head.appendChild(style);
}
// Dark mode CSS - inverts colors but preserves images/video
style.textContent = '
/* Invert everything except media */
html {
filter: invert(1) hue-rotate(180deg) !important;
background: #1a1a2e !important;
}
/* Restore images, videos, iframes, canvas */
img, video, iframe, canvas, svg, picture, [style*="background-image"] {
filter: invert(1) hue-rotate(180deg) !important;
}
/* Preserve specific elements that should not be inverted */
.no-dark-mode, .no-dark-mode *,
[data-theme="light"], [data-theme="light"],
.ace_editor, .ace_editor *,
.CodeMirror, .CodeMirror *,
.monaco-editor, .monaco-editor *,
.markdown-body pre, .markdown-body pre *,
.highlight, .highlight *,
pre code, pre code * {
filter: none !important;
}
/* Fix common UI elements */
.modal, .popup, .dropdown-menu, .tooltip, .popover {
filter: invert(1) hue-rotate(180deg) !important;
background: #2d2d44 !important;
border-color: #444 !important;
}
/* Scrollbars */
::-webkit-scrollbar { background: #1a1a2e !important; }
::-webkit-scrollbar-thumb { background: #444 !important; }
::-webkit-scrollbar-thumb:hover { background: #555 !important; }
/* Selection */
::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
';
}
function removeDarkMode() {
var style = document.getElementById('universal-dark-mode-style');
if (style) style.remove();
}
// Toggle with Alt+Shift+D
document.addEventListener('keydown', function(e) {
if (e.altKey && e.shiftKey && e.key === 'D') {
e.preventDefault();
enabled = !enabled;
if (enabled) {
applyDarkMode();
console.log('[Universal Dark Mode] Enabled');
} else {
removeDarkMode();
console.log('[Universal Dark Mode] Disabled');
}
}
});
// Apply on load
applyDarkMode();
// Re-apply on dynamic content
var observer = new MutationObserver(function(mutations) {
if (enabled && !document.getElementById('universal-dark-mode-style')) {
applyDarkMode();
}
});
observer.observe(document.head, { childList: true });
console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
})();
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

                Skip to content
                View jack2148's full-sized avatar
                1
follower
 · 0
following

                  
Block or report jack2148

                  Block user
                  
Prevent this user from interacting with your repositories and sending you notifications.
Learn more about blocking users.

                  
You must be logged in to block users.

                  
Content in all repositories owned by your account will be closed.

                  
Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.

                  Report abuse
                  
Contact GitHub support about this user’s behavior.
Learn more about reporting abuse.

                  Report abuse
                  jack2148/README.md
                  Yeachan Kim
                  Robotics · Robot Learning · Manipulation
                  Robotics Engineering undergraduate at Hanyang University ERICA and undergraduate research intern at KITECH since July 2026. Current work focuses on teleoperation-based manipulation demonstrations, imitation learning, and data-efficient demonstration collection. Interested in hybrid robot-learning approaches that combine data-driven policies with robot kinematics and structured control.
                  Email
                  Current Research
                  1. Teleoperation & Manipulation Learning
                  OMY_FRANKA_TELEOP
                  Built a teleoperation pipeline from a physical OMY-L100 leader to a Franka FR3 in MuJoCo, including Cartesian pose retargeting, 6D-to-7DoF damped least-squares inverse kinematics, and null-space control. This is a simulation-stage FR3 system; deployment to physical FR3 hardware is not claimed.
                  2. Imitation Learning & Policy Analysis
                  dp-act-policy-study
                  Studying ACT and Diffusion Policy in LeRobot-based manipulation tasks, including Push-T analysis and FR3 MuJoCo experiments using teleoperation demonstrations. Current work investigates how execution strategy, trajectory coverage, and demonstration distribution affect policy performance.
                  The Push-T study includes a controlled 50-episode execution-strategy ablation. FR3 results are reported separately as fixed-initial-condition pilot evaluations, not as a generalization benchmark.
                  Research Interests
                  My broader research interest is in robotic manipulation architectures that separate learning-based decision making from kinematics- and control-based execution, combining data-driven policies with structured robot control.
                  Supporting Robotics Systems Work
                  rgbd-object-pose-estimation — RGB-D manipulation perception using an Intel RealSense D455, YOLOv8-seg, CAD meshes, and FoundationPose. The verified mean mask mAP50 is 0.929 across three industrial-part classes; this is a segmentation metric, not 6D-pose accuracy.
                  navigation_stack_lab — Real-hardware ROS 2 navigation and system integration with SLAM, EKF sensor fusion, Nav2, DWB/MPPI experiments, and person-following logic. MPPI computation and parameters were optimized to improve the control loop from approximately 5–6 Hz to a stable 20 Hz.
                  Selected Achievements
                  • 1st Place — 2026 Creative Comprehensive Design Competition
                  • Bronze Award — College of Engineering Capstone Design Competition
                  • Finalist — KAIST Mobility Challenge 2026
                  • Undergraduate Paper Award — ICROS 2026
                  Publications / Presentations
                  • “Performance Comparison of DWB and MPPI Local Planners in ROS 2 Nav2 Environment” — ICROS 2026, poster, first author
                  • “Multimodal Perception and Monitoring System for Indoor Security Patrol Robots” — ICROS 2026, poster, Undergraduate Paper Award
                  Technical Areas
                  ROS 2 · MuJoCo · LeRobot · ACT · Diffusion Policy · Python · C/C++ · Nav2 · SLAM · MPPI · DWB · RGB-D · YOLOv8 · FoundationPose
                  
Pinned
Loading
                  1. dp-act-policy-studydp-act-policy-studyPublic
                    
ACT and Diffusion Policy experiments for manipulation, from Push-T policy analysis to teleoperation-derived FR3 learning.

                    Python
1

                  2. OMY_FRANKA_TELEOPOMY_FRANKA_TELEOPPublic
                    
Physical OMY-L100 to MuJoCo Franka FR3 teleoperation with Cartesian retargeting, DLS IK, and null-space control.

                    Python
1

                  3. navigation_stack_labnavigation_stack_labPublic
                    
ROS 2 autonomous navigation and patrol system with SLAM, AMCL, Nav2, DWB/MPPI evaluation, and real-robot debugging.

                    Python
1

                  4. rgbd-object-pose-estimationrgbd-object-pose-estimationPublic
                    
RGB-D 6D object pose estimation pipeline using RealSense D455, YOLOv8-seg, FoundationPose, and ROS 2.

                    Python
1