Research Projects | NHURO Nexus<\/title>\n <style>\n :root {\n --primary: #2c3e50;\n --secondary: #e67e22;\n --info: #3498db;\n --check: #27ae60;\n --research: #e74c3c;\n --neural: #8e44ad;\n --hint-bg: #fdf2e9;\n --code-bg: #1e1e1e;\n }\n body { font-family: 'Segoe UI', sans-serif; line-height: 1.6; color: #333; background: #fdfdfd; margin: 0; padding: 0; }\n .header { background: var(--primary); color: white; padding: 40px 20px; text-align: center; border-bottom: 5px solid var(--neural); }\n .container { max-width: 1100px; margin: 20px auto; padding: 20px; background: white; border-radius: 8px; box-shadow: 0 2px 10px rgba(0,0,0,0.05); }\n .project-card { border: 1px solid #eee; border-radius: 8px; padding: 25px; margin-bottom: 40px; border-top: 5px solid var(--secondary); }\n .project-tag { display: inline-block; background: var(--info); color: white; padding: 3px 12px; border-radius: 4px; font-size: 0.85em; margin-bottom: 10px; font-weight: bold; }\n .section-header { font-weight: bold; color: var(--primary); margin-top: 15px; display: block; border-bottom: 1px solid #eee; margin-bottom: 5px; }\n .hint-box { background: var(--hint-bg); border-left: 4px solid var(--secondary); padding: 12px; margin: 10px 0; font-size: 0.92em; }\n .grid-box { display: grid; grid-template-columns: 1fr 1fr; gap: 10px; margin: 10px 0; }\n .eval-box { background: #eafaf1; border-left: 4px solid var(--check); padding: 12px; margin: 0; font-size: 0.92em; }\n .ref-section { margin-top: 20px; padding-top: 15px; border-top: 1px dashed #ccc; background: #f8f9fa; padding: 10px; border-radius: 4px; }\n .ref-link { display: block; color: var(--info); text-decoration: none; font-size: 0.85em; margin-bottom: 5px; padding-left: 5px; }\n .ref-link:hover { text-decoration: underline; color: var(--primary); }\n h2 { color: var(--primary); margin-top: 0; }\n .abbr-tag { color: var(--secondary); font-weight: bold; font-family: monospace; }\n .lab-resources { background: #eee; padding: 15px; border-radius: 6px; margin-bottom: 30px; border: 1px solid #ddd; }\n .hierarchy-box { background: #f4ecf7; border-left: 5px solid #9b59b6; padding: 15px; margin: 20px 0; border-radius: 4px; }\n pre { background: var(--code-bg); color: #dcdcdc; padding: 15px; border-radius: 6px; overflow-x: auto; font-size: 0.85em; line-height: 1.4; }\n .nav-link { display: inline-block; margin-top: 40px; text-decoration: none; color: var(--info); font-weight: bold; }\n code { background: #f4f4f4; padding: 2px 4px; border-radius: 4px; font-family: monospace; }\n <\/style>\n<\/head>\n<body>\n\n<div class=\"header\">\n <h1>NHURO Research Sandbox<\/h1>\n <p>University of Aizu | Advanced Computer Systems Lab | AY2026 Graduation Thesis Framework<\/p>\n<\/div>\n\n<div class=\"container\">\n \n <div class=\"hierarchy-box\">\n <strong>\ud83d\udcc2 Master SDK Source Hierarchy (Base Structure)<\/strong><br>\n <p style=\"font-size: 0.9em; margin-bottom: 10px;\">Students must adhere to this architecture. New nodes and interfaces should be integrated into the specified directories to ensure system compatibility.<\/p>\n <pre>\nnhuro_project\/\n\u251c\u2500\u2500 voice_bridge.py <-- (T1) Sensory Gateway (System Python)\n\u2514\u2500\u2500 nhuro_ws\/ <-- ROS 2 Workspace (Mamba Env)\n \u2514\u2500\u2500 src\/\n \u251c\u2500\u2500 nhuro_interfaces\/ <-- ADD NEW .msg \/ .action FILES HERE\n \u2502 \u251c\u2500\u2500 action\/ Wave.action, Walk.action\n \u2502 \u2514\u2500\u2500 msg\/\n \u2514\u2500\u2500 nhuro_driver\/ <-- ADD NEW RESEARCH NODES HERE\n \u2514\u2500\u2500 nhuro_driver\/\n \u251c\u2500\u2500 voice_parser_node.py <-- (T2) The Brain\n \u251c\u2500\u2500 nhuro_action_node.py <-- (T3) The Muscles\n \u2514\u2500\u2500 nhuro\/ <-- Hardware Library\n \u251c\u2500\u2500 robot.py\n \u2514\u2500\u2500 bus_servo.py\n <\/pre>\n <p style=\"font-size: 0.85em;\">Detailed setup instructions are available in <a href=\"https:\/\/giatec.io\/aizuhand\/index.php\/nexus-initialization\/\" style=\"color: #3498db; font-weight: bold;\">Module 0: Nexus Initialization<\/a>.<\/p>\n <\/div>\n\n <div class=\"lab-resources\">\n <strong>\ud83d\udee0\ufe0f Available Laboratory Resources:<\/strong>\n <ul>\n <li><strong>Sensors:<\/strong> Raspberry Pi Camera Module, USB Microphones, MPU6050 IMU.<\/li>\n <li><strong>Actuators:<\/strong> Bus-Servos (ttyAMA0), Motorized Active Grasper (Replacement Kit).<\/li>\n <li><strong>Software Environment:<\/strong> ROS 2 Humble, PyTorch, YOLOv8-tiny, Librosa.<\/li>\n <\/ul>\n <\/div>\n\n <div class=\"project-card\" style=\"border-top-color: var(--research);\">\n <span class=\"project-tag\">AI & COMPUTER VISION<\/span>\n <h2><span class=\"abbr-tag\">NHURO-Vision:<\/span> Edge-AI Based Real-Time Object Recognition and Tracking for Humanoid Robotic Security<\/h2>\n \n <span class=\"section-header\">Problem Description<\/span>\n <p>This project addresses the limitation of hard-coded environmental sensing. Students must enable NHURO to identify and track dynamic objects in real-time using limited onboard compute.<\/p>\n \n <div class=\"hint-box\">\n <strong>Project Blueprint & New Files:<\/strong>\n <ul>\n <li><code>nhuro_interfaces\/msg\/Detection.msg<\/code>: Define custom data types for object labels and bounding boxes.<\/li>\n <li><code>nhuro_driver\/vision_node.py<\/code>: Implement a node that wraps the YOLOv8-tiny inference engine.<\/li>\n <li><strong>Design Hint:<\/strong> Optimize the model for the Raspberry Pi and bridge output to the <code>nhuro_voice_text<\/code> topic.<\/li>\n <\/ul>\n <\/div>\n\n <div class=\"eval-box\">\n <strong>Evaluation Standards:<\/strong> Inference Speed (FPS), Mean Average Precision (mAP), and CPU utilization.\n <\/div>\n\n <div class=\"ref-section\">\n <strong>References:<\/strong>\n <a class=\"ref-link\" href=\"https:\/\/www.cv-foundation.org\/openaccess\/content_cvpr_2016\/papers\/Redmon_You_Only_Look_CVPR_2016_paper.pdf\" target=\"_blank\">\ud83d\udcc4 You Only Look Once:\nUnified, Real-Time Object Detection<\/a>\n <a class=\"ref-link\" href=\"https:\/\/pjreddie.com\/darknet\/yolo\/\" target=\"_blank\">\ud83d\udcc4 YOLO: Real-Time Object Detection Framework<\/a>\n <a class=\"ref-link\" href=\"https:\/\/ieeexplore.ieee.org\/document\/10631278\" target=\"_blank\">\ud83d\udcc4 Energy-Efficient Optimal Mode Selection for Edge AI Inference via Integrated Sensing-Communication-Computation (IEEE Transactions)<\/a>\n <a class=\"ref-link\" href=\"https:\/\/ieeexplore.ieee.org\/document\/10187693\" target=\"_blank\">\ud83d\udcc4 Design and Control of a Small Humanoid Equipped With Flight Unit and Wheels for Multimodal Locomotion<\/a>\n <\/div>\n <\/div>\n\n <div class=\"project-card\" style=\"border-top-color: var(--primary);\">\n <span class=\"project-tag\">CONTROL & KINEMATICS<\/span>\n <h2><span class=\"abbr-tag\">NHURO-Dance:<\/span> Development of a Rhythmic Motion Synchronization Framework for Multimodal Humanoid Interaction<\/h2>\n \n <span class=\"section-header\">Problem Description<\/span>\n <p>The challenge is to align high-latency ROS 2 action execution with low-latency audio beats to create fluid, rhythmic entertainment routines.<\/p>\n\n <div class=\"hint-box\">\n <strong>Project Blueprint & New Files:<\/strong>\n <ul>\n <li><code>nhuro_driver\/choreography_node.py<\/code>: Create an Action Client that sequences multiple goals (Wave, Walk, Bow).<\/li>\n <li><code>nhuro_driver\/audio_analyzer.py<\/code>: Implement real-time beat detection using <code>librosa<\/code>.<\/li>\n <li><strong>Design Hint:<\/strong> Adjust action <code>step_duration<\/code> parameters dynamically based on detected BPM.<\/li>\n <\/ul>\n <\/div>\n\n <div class=\"eval-box\">\n <strong>Evaluation Standards:<\/strong> Rhythmic correctness (ms deviation from beat) and transition smoothness between discrete actions.\n <\/div>\n\n <div class=\"ref-section\">\n <strong>References:<\/strong>\n <a class=\"ref-link\" href=\"https:\/\/www2.kobe-u.ac.jp\/~tazaki\/docs\/tazaki_ral2025.pdf\" target=\"_blank\">\ud83d\udcc4 Key Pose-Based Dynamic Humanoid Motion Generation Using Parallel Multi-Fidelity Model\nPredictive Control (IEEE)<\/a> \n<a class=\"ref-link\" href=\"https:\/\/control.ros.org\/master\/doc\/ros2_control\/controller_manager\/doc\/userdoc.html\" target=\"_blank\">\ud83d\udcc4 ROS 2 Control: Synchronizing Multiple Actuators<\/a>\n <a class=\"ref-link\" href=\"https:\/\/ieeexplore.ieee.org\/document\/6628446?utm_source=copilot.com\" target=\"_blank\">\ud83d\udcc4 Autonomous Humanoid Robot Dance Generation System<\/a>\n <a class=\"ref-link\" href=\"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-642-04052-8_4\" target=\"_blank\">\ud83d\udcc4 Dance Motion Control of a Humanoid Robot Based on\nReal-Time Tempo Tracking from Musical Audio Signals <\/a>\n <\/div>\n <\/div>\n\n <div class=\"project-card\" style=\"border-top-color: var(--check);\">\n <span class=\"project-tag\">ASSISTIVE MANIPULATION<\/span>\n <h2><span class=\"abbr-tag\">NHURO-Care:<\/span> Integration and Force-Limited Control of an Active Bionic Grasper for Assistive Humanoid Tasks<\/h2>\n \n <span class=\"section-header\">Problem Description<\/span>\n <p>This project focuses on the integration of a motorized active grasper to allow for physical interaction with light objects like medicine bottles.<\/p>\n\n <div class=\"hint-box\">\n <strong>Project Blueprint & New Files:<\/strong>\n <ul>\n <li><code>nhuro_interfaces\/action\/Grasp.action<\/code>: Define the goal (open\/close) and feedback (pressure\/torque).<\/li>\n <li><code>nhuro_driver\/nhuro\/robot.py<\/code>: Update the hardware library with a <code>move_grasper()<\/code> method.<\/li>\n <li><strong>Design Hint:<\/strong> Physically swap the static hand for the motorized replacement kit and map the new Servo ID.<\/li>\n <\/ul>\n <\/div>\n\n <div class=\"eval-box\">\n <strong>Evaluation Standards:<\/strong> Grasp success rate (%), load-bearing capacity (grams), and torque accuracy to prevent crushing objects.\n <\/div>\n\n <div class=\"ref-section\">\n <strong>Academic References:<\/strong>\n <a class=\"ref-link\" href=\"https:\/\/www.mdpi.com\/2076-0825\/11\/1\/22\" target=\"_blank\">\ud83d\udcc4 Design of Low-Cost Bionic Grippers for Assistive Robots<\/a>\n <a class=\"ref-link\" href=\"https:\/\/arxiv.org\/pdf\/2603.08142\" target=\"_blank\">\ud83d\udcc4 Force Control in Humanoid Hand Manipulation (IEEE)<\/a>\n <a class=\"ref-link\" href=\"https:\/\/arxiv.org\/abs\/2603.08142\" target=\"_blank\">\ud83d\udcc4 Multifingered force-aware control for humanoid robots<\/a>\n <a class=\"ref-link\" href=\"https:\/\/ieeexplore.ieee.org\/document\/9810841\" target=\"_blank\">\ud83d\udcc4 Multifingered Robot Hand Compliant Manipulation Based on Human\u2011in\u2011the\u2011Loop Learning\u2011Control<\/a>\n <\/div>\n <\/div>\n\n <div class=\"project-card\" style=\"border-top-color: var(--neural);\">\n <span class=\"project-tag\">NEURAL NETWORKS & CONTROL<\/span>\n <h2><span class=\"abbr-tag\">NHURO-Brain:<\/span> Design of an Artificial Neural Network Controller for Dynamic Gait Optimization in Bipedal Humanoids<\/h2>\n \n <span class=\"section-header\">Problem Description<\/span>\n <p>Hard-coded patterns are unstable on uneven terrain. The student must develop a neural controller that adjusts the gait dynamically based on IMU feedback.<\/p>\n\n <div class=\"hint-box\">\n <strong>Project Blueprint & New Files:<\/strong>\n <ul>\n <li><code>nhuro_driver\/imu_listener_node.py<\/code>: Collect orientation data ($pitch$, $roll$) from the MPU6050.<\/li>\n <li><code>nhuro_driver\/neural_gait_node.py<\/code>: Run a trained ANN to predict optimal servo angle offsets.<\/li>\n <li><strong>Design Hint:<\/strong> Train the model using PyTorch and deploy for real-time inference on the Pi.<\/li>\n <\/ul>\n <\/div>\n\n <div class=\"eval-box\">\n <strong>Evaluation Standards:<\/strong> Balance stability (variance in IMU data), inference latency (ms), and successful walk distance without falling.\n <\/div>\n\n <div class=\"ref-section\">\n <strong>Academic References:<\/strong>\n <a class=\"ref-link\" href=\"https:\/\/ieeexplore.ieee.org\/document\/895253\" target=\"_blank\">\ud83d\udcc4 Design of central pattern generator for humanoid robot walking based on multi-objective GA (IEEE)<\/a>\n <a class=\"ref-link\" href=\"https:\/\/www.mdpi.com\/2227-7390\/13\/6\/954\" target=\"_blank\">\ud83d\udcc4 ZNN\u2011Based Gait Optimization for Humanoid Robots with ALIP and Inequality Constraints<\/a>\n <a class=\"ref-link\" href=\"https:\/\/arxiv.org\/pdf\/2401.16889\" target=\"_blank\">\ud83d\udcc4 Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control<\/a>\n <a class=\"ref-link\" href=\"https:\/\/ieeexplore.ieee.org\/document\/10875312\" target=\"_blank\">\ud83d\udcc4 Bipedal Robot Gait Control Strategies Based on Reinforcement Learning<\/a>\n <\/div>\n <\/div>\n\n <div class=\"project-card\" style=\"border-top-color: var(--info);\">\n <span class=\"project-tag\">HUMAN-ROBOT INTERACTION<\/span>\n <h2><span class=\"abbr-tag\">NHURO-Interface:<\/span> Development of a Low-Latency Web Dashboard and Digital Twin for Remote Humanoid Telemetry<\/h2>\n \n <span class=\"section-header\">Problem Description<\/span>\n <p>Students must build a low-latency web dashboard to visualize \"internal thoughts\" (voice logs) and \"physical state\" (step count).<\/p>\n\n <div class=\"hint-box\">\n <strong>Project Blueprint & New Files:<\/strong>\n <ul>\n <li><code>~\/nhuro_project\/dashboard\/app.js<\/code>: Implement a WebSocket client using <code>roslibjs<\/code>.<\/li>\n <li><code>~\/nhuro_project\/dashboard\/index.html<\/code>: Design a visual UI to show step counts and voice logs.<\/li>\n <li><strong>Design Hint:<\/strong> Use the <code>rosbridge_suite<\/code> to communicate between the browser and the ROS workspace.<\/li>\n <\/ul>\n <\/div>\n\n <div class=\"eval-box\">\n <strong>Evaluation Standards:<\/strong> UI Refresh rate (Hz), data synchronization accuracy, and cross-compatibility.\n <\/div>\n\n <div class=\"ref-section\">\n <strong>Academic References:<\/strong>\n <a class=\"ref-link\" href=\"https:\/\/sciota-robotics.com\/2024\/09\/09\/robotics-ui\/\" target=\"_blank\">\ud83d\udcc4 Introducing Robotics UI: A Web Interface Solution for ROS 2 Robots <\/a>\n <a class=\"ref-link\" href=\"https:\/\/robotwebtools.github.io\/\" target=\"_blank\">\ud83d\udcc4 Documentation: Robot Web Tools (Humble)<\/a>\n <a class=\"ref-link\" href=\"https:\/\/ieeexplore.ieee.org\/document\/10269714\" target=\"_blank\">\ud83d\udcc4 Low\u2011Latency Communications for Digital Twin Empowered Systems<\/a>\n <a class=\"ref-link\" href=\"https:\/\/arxiv.org\/pdf\/2409.04639\" target=\"_blank\">\ud83d\udcc4 2. High\u2011Speed and Impact\u2011Resilient Teleoperation of Humanoid Robots<\/a>\n <\/div>\n <\/div>\n\n <div class=\"project-card\" style=\"border-top-color: #f1c40f;\">\n <span class=\"project-tag\">ROBOTIC DYNAMICS & SAFETY<\/span>\n <h2><span class=\"abbr-tag\">NHURO-Safe:<\/span> Autonomous Fall Detection and Recovery Strategies for Humanoid Robots via Inertial Sensory Feedback<\/h2>\n \n <span class=\"section-header\">Problem Description<\/span>\n <p>Bipedal robots are prone to falling. The student must detect falls and execute autonomous \"Stand-Up\" sequences via IMU monitoring.<\/p>\n\n <div class=\"hint-box\">\n <strong>Project Blueprint & New Files:<\/strong>\n <ul>\n <li><code>nhuro_driver\/fall_manager_node.py<\/code>: Monitor pitch\/roll thresholds to detect balance loss.<\/li>\n <li><code>nhuro_interfaces\/action\/StandUp.action<\/code>: Define the multi-phase stand-up sequence.<\/li>\n <li><strong>Design Hint:<\/strong> Develop high-torque servo patterns to safely return the robot to a neutral pose.<\/li>\n <\/ul>\n <\/div>\n\n <div class=\"eval-box\">\n <strong>Evaluation Standards:<\/strong> Detection latency (ms), success rate of stand-up recovery, and prevention of servo overheating.\n <\/div>\n\n <div class=\"ref-section\">\n <strong>Academic References:<\/strong>\n \n <a class=\"ref-link\" href=\"https:\/\/ieeexplore.ieee.org\/document\/7353740\" target=\"_blank\">\n \ud83d\udcc4 Fall Detection and Standing Up Strategy for Humanoids\n <\/a>\n\n <a class=\"ref-link\" href=\"https:\/\/ieeexplore.ieee.org\/document\/9065191\" target=\"_blank\">\n \ud83d\udcc4 Reflex Balance Schemes and Fast Fall Impact Mitigation Mechanisms\n <\/a>\n\n <a class=\"ref-link\" href=\"https:\/\/ieeexplore.ieee.org\/document\/6651510\" target=\"_blank\">\n \ud83d\udcc4 Multi\u2011Phase Trajectory Optimization for Post\u2011Fall Autonomous Humanoid Recovery\n <\/a>\n\n <a class=\"ref-link\" href=\"https:\/\/www.frontiersin.org\/articles\/10.3389\/frobt.2020.00072\/full\" target=\"_blank\">\n \ud83d\udcc4 Whole\u2011Body Control for Humanoid Fall Mitigation\n <\/a>\n<\/div>\n\n <\/div>\n\n <a href=\"https:\/\/giatec.io\/aizuhand\/index.php\/nhuro-nexus\/\" class=\"nav-link\">← Return to NHURO Nexus Portal<\/a>\n<\/div>\n\n<div style=\"text-align: center; padding: 20px; color: #888; font-size: 0.8em;\">\n © 2026 NHURO Nexus | Advanced Computing Systems Lab | Abderazek Ben Abdallah, Z. Wang\n<\/div>\n\n<\/body>\n<\/html>\n","protected":false},"excerpt":{"rendered":"<p>Research Projects | NHURO Nexus NHURO Research Sandbox University of Aizu | Advanced Computer Systems Lab | AY2026 Graduation Thesis Framework \ud83d\udcc2 Master SDK Source Hierarchy (Base Structure) Students must adhere to this architecture. New nodes and interfaces should be integrated into the specified directories to ensure system compatibility. nhuro_project\/ \u251c\u2500\u2500 voice_bridge.py<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"saved_in_kubio":false,"footnotes":""},"class_list":["post-641","page","type-page","status-publish","hentry"],"kubio_ai_page_context":{"short_desc":"","purpose":"general"},"_links":{"self":[{"href":"https:\/\/giatec.io\/aizuhand\/index.php\/wp-json\/wp\/v2\/pages\/641","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/giatec.io\/aizuhand\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/giatec.io\/aizuhand\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/giatec.io\/aizuhand\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/giatec.io\/aizuhand\/index.php\/wp-json\/wp\/v2\/comments?post=641"}],"version-history":[{"count":45,"href":"https:\/\/giatec.io\/aizuhand\/index.php\/wp-json\/wp\/v2\/pages\/641\/revisions"}],"predecessor-version":[{"id":708,"href":"https:\/\/giatec.io\/aizuhand\/index.php\/wp-json\/wp\/v2\/pages\/641\/revisions\/708"}],"wp:attachment":[{"href":"https:\/\/giatec.io\/aizuhand\/index.php\/wp-json\/wp\/v2\/media?parent=641"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}

{"id":641,"date":"2026-05-14T03:23:43","date_gmt":"2026-05-14T03:23:43","guid":{"rendered":"https:\/\/giatec.io\/aizuhand\/?page_id=641"},"modified":"2026-05-21T06:39:48","modified_gmt":"2026-05-21T06:39:48","slug":"researh-projects","status":"publish","type":"page","link":"https:\/\/giatec.io\/aizuhand\/index.php\/researh-projects\/","title":{"rendered":"Researh Projects"},"content":{"rendered":"\n\n\n\n \n \n Research Projects | NHURO Nexus<\/title>\n <style>\n :root {\n --primary: #2c3e50;\n --secondary: #e67e22;\n --info: #3498db;\n --check: #27ae60;\n --research: #e74c3c;\n --neural: #8e44ad;\n --hint-bg: #fdf2e9;\n --code-bg: #1e1e1e;\n }\n body { font-family: 'Segoe UI', sans-serif; line-height: 1.6; color: #333; background: #fdfdfd; margin: 0; padding: 0; }\n .header { background: var(--primary); color: white; padding: 40px 20px; text-align: center; border-bottom: 5px solid var(--neural); }\n .container { max-width: 1100px; margin: 20px auto; padding: 20px; background: white; border-radius: 8px; box-shadow: 0 2px 10px rgba(0,0,0,0.05); }\n .project-card { border: 1px solid #eee; border-radius: 8px; padding: 25px; margin-bottom: 40px; border-top: 5px solid var(--secondary); }\n .project-tag { display: inline-block; background: var(--info); color: white; padding: 3px 12px; border-radius: 4px; font-size: 0.85em; margin-bottom: 10px; font-weight: bold; }\n .section-header { font-weight: bold; color: var(--primary); margin-top: 15px; display: block; border-bottom: 1px solid #eee; margin-bottom: 5px; }\n .hint-box { background: var(--hint-bg); border-left: 4px solid var(--secondary); padding: 12px; margin: 10px 0; font-size: 0.92em; }\n .grid-box { display: grid; grid-template-columns: 1fr 1fr; gap: 10px; margin: 10px 0; }\n .eval-box { background: #eafaf1; border-left: 4px solid var(--check); padding: 12px; margin: 0; font-size: 0.92em; }\n .ref-section { margin-top: 20px; padding-top: 15px; border-top: 1px dashed #ccc; background: #f8f9fa; padding: 10px; border-radius: 4px; }\n .ref-link { display: block; color: var(--info); text-decoration: none; font-size: 0.85em; margin-bottom: 5px; padding-left: 5px; }\n .ref-link:hover { text-decoration: underline; color: var(--primary); }\n h2 { color: var(--primary); margin-top: 0; }\n .abbr-tag { color: var(--secondary); font-weight: bold; font-family: monospace; }\n .lab-resources { background: #eee; padding: 15px; border-radius: 6px; margin-bottom: 30px; border: 1px solid #ddd; }\n .hierarchy-box { background: #f4ecf7; border-left: 5px solid #9b59b6; padding: 15px; margin: 20px 0; border-radius: 4px; }\n pre { background: var(--code-bg); color: #dcdcdc; padding: 15px; border-radius: 6px; overflow-x: auto; font-size: 0.85em; line-height: 1.4; }\n .nav-link { display: inline-block; margin-top: 40px; text-decoration: none; color: var(--info); font-weight: bold; }\n code { background: #f4f4f4; padding: 2px 4px; border-radius: 4px; font-family: monospace; }\n <\/style>\n<\/head>\n<body>\n\n<div class=\"header\">\n <h1>NHURO Research Sandbox<\/h1>\n <p>University of Aizu | Advanced Computer Systems Lab | AY2026 Graduation Thesis Framework<\/p>\n<\/div>\n\n<div class=\"container\">\n \n <div class=\"hierarchy-box\">\n <strong>\ud83d\udcc2 Master SDK Source Hierarchy (Base Structure)<\/strong><br>\n <p style=\"font-size: 0.9em; margin-bottom: 10px;\">Students must adhere to this architecture. New nodes and interfaces should be integrated into the specified directories to ensure system compatibility.<\/p>\n <pre>\nnhuro_project\/\n\u251c\u2500\u2500 voice_bridge.py <-- (T1) Sensory Gateway (System Python)\n\u2514\u2500\u2500 nhuro_ws\/ <-- ROS 2 Workspace (Mamba Env)\n \u2514\u2500\u2500 src\/\n \u251c\u2500\u2500 nhuro_interfaces\/ <-- ADD NEW .msg \/ .action FILES HERE\n \u2502 \u251c\u2500\u2500 action\/ Wave.action, Walk.action\n \u2502 \u2514\u2500\u2500 msg\/\n \u2514\u2500\u2500 nhuro_driver\/ <-- ADD NEW RESEARCH NODES HERE\n \u2514\u2500\u2500 nhuro_driver\/\n \u251c\u2500\u2500 voice_parser_node.py <-- (T2) The Brain\n \u251c\u2500\u2500 nhuro_action_node.py <-- (T3) The Muscles\n \u2514\u2500\u2500 nhuro\/ <-- Hardware Library\n \u251c\u2500\u2500 robot.py\n \u2514\u2500\u2500 bus_servo.py\n <\/pre>\n <p style=\"font-size: 0.85em;\">Detailed setup instructions are available in <a href=\"https:\/\/giatec.io\/aizuhand\/index.php\/nexus-initialization\/\" style=\"color: #3498db; font-weight: bold;\">Module 0: Nexus Initialization<\/a>.<\/p>\n <\/div>\n\n <div class=\"lab-resources\">\n <strong>\ud83d\udee0\ufe0f Available Laboratory Resources:<\/strong>\n <ul>\n <li><strong>Sensors:<\/strong> Raspberry Pi Camera Module, USB Microphones, MPU6050 IMU.<\/li>\n <li><strong>Actuators:<\/strong> Bus-Servos (ttyAMA0), Motorized Active Grasper (Replacement Kit).<\/li>\n <li><strong>Software Environment:<\/strong> ROS 2 Humble, PyTorch, YOLOv8-tiny, Librosa.<\/li>\n <\/ul>\n <\/div>\n\n <div class=\"project-card\" style=\"border-top-color: var(--research);\">\n <span class=\"project-tag\">AI & COMPUTER VISION<\/span>\n <h2><span class=\"abbr-tag\">NHURO-Vision:<\/span> Edge-AI Based Real-Time Object Recognition and Tracking for Humanoid Robotic Security<\/h2>\n \n <span class=\"section-header\">Problem Description<\/span>\n <p>This project addresses the limitation of hard-coded environmental sensing. Students must enable NHURO to identify and track dynamic objects in real-time using limited onboard compute.<\/p>\n \n <div class=\"hint-box\">\n <strong>Project Blueprint & New Files:<\/strong>\n <ul>\n <li><code>nhuro_interfaces\/msg\/Detection.msg<\/code>: Define custom data types for object labels and bounding boxes.<\/li>\n <li><code>nhuro_driver\/vision_node.py<\/code>: Implement a node that wraps the YOLOv8-tiny inference engine.<\/li>\n <li><strong>Design Hint:<\/strong> Optimize the model for the Raspberry Pi and bridge output to the <code>nhuro_voice_text<\/code> topic.<\/li>\n <\/ul>\n <\/div>\n\n <div class=\"eval-box\">\n <strong>Evaluation Standards:<\/strong> Inference Speed (FPS), Mean Average Precision (mAP), and CPU utilization.\n <\/div>\n\n <div class=\"ref-section\">\n <strong>References:<\/strong>\n <a class=\"ref-link\" href=\"https:\/\/www.cv-foundation.org\/openaccess\/content_cvpr_2016\/papers\/Redmon_You_Only_Look_CVPR_2016_paper.pdf\" target=\"_blank\">\ud83d\udcc4 You Only Look Once:\nUnified, Real-Time Object Detection<\/a>\n <a class=\"ref-link\" href=\"https:\/\/pjreddie.com\/darknet\/yolo\/\" target=\"_blank\">\ud83d\udcc4 YOLO: Real-Time Object Detection Framework<\/a>\n <a class=\"ref-link\" href=\"https:\/\/ieeexplore.ieee.org\/document\/10631278\" target=\"_blank\">\ud83d\udcc4 Energy-Efficient Optimal Mode Selection for Edge AI Inference via Integrated Sensing-Communication-Computation (IEEE Transactions)<\/a>\n <a class=\"ref-link\" href=\"https:\/\/ieeexplore.ieee.org\/document\/10187693\" target=\"_blank\">\ud83d\udcc4 Design and Control of a Small Humanoid Equipped With Flight Unit and Wheels for Multimodal Locomotion<\/a>\n <\/div>\n <\/div>\n\n <div class=\"project-card\" style=\"border-top-color: var(--primary);\">\n <span class=\"project-tag\">CONTROL & KINEMATICS<\/span>\n <h2><span class=\"abbr-tag\">NHURO-Dance:<\/span> Development of a Rhythmic Motion Synchronization Framework for Multimodal Humanoid Interaction<\/h2>\n \n <span class=\"section-header\">Problem Description<\/span>\n <p>The challenge is to align high-latency ROS 2 action execution with low-latency audio beats to create fluid, rhythmic entertainment routines.<\/p>\n\n <div class=\"hint-box\">\n <strong>Project Blueprint & New Files:<\/strong>\n <ul>\n <li><code>nhuro_driver\/choreography_node.py<\/code>: Create an Action Client that sequences multiple goals (Wave, Walk, Bow).<\/li>\n <li><code>nhuro_driver\/audio_analyzer.py<\/code>: Implement real-time beat detection using <code>librosa<\/code>.<\/li>\n <li><strong>Design Hint:<\/strong> Adjust action <code>step_duration<\/code> parameters dynamically based on detected BPM.<\/li>\n <\/ul>\n <\/div>\n\n <div class=\"eval-box\">\n <strong>Evaluation Standards:<\/strong> Rhythmic correctness (ms deviation from beat) and transition smoothness between discrete actions.\n <\/div>\n\n <div class=\"ref-section\">\n <strong>References:<\/strong>\n <a class=\"ref-link\" href=\"https:\/\/www2.kobe-u.ac.jp\/~tazaki\/docs\/tazaki_ral2025.pdf\" target=\"_blank\">\ud83d\udcc4 Key Pose-Based Dynamic Humanoid Motion Generation Using Parallel Multi-Fidelity Model\nPredictive Control (IEEE)<\/a> \n<a class=\"ref-link\" href=\"https:\/\/control.ros.org\/master\/doc\/ros2_control\/controller_manager\/doc\/userdoc.html\" target=\"_blank\">\ud83d\udcc4 ROS 2 Control: Synchronizing Multiple Actuators<\/a>\n <a class=\"ref-link\" href=\"https:\/\/ieeexplore.ieee.org\/document\/6628446?utm_source=copilot.com\" target=\"_blank\">\ud83d\udcc4 Autonomous Humanoid Robot Dance Generation System<\/a>\n <a class=\"ref-link\" href=\"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-642-04052-8_4\" target=\"_blank\">\ud83d\udcc4 Dance Motion Control of a Humanoid Robot Based on\nReal-Time Tempo Tracking from Musical Audio Signals <\/a>\n <\/div>\n <\/div>\n\n <div class=\"project-card\" style=\"border-top-color: var(--check);\">\n <span class=\"project-tag\">ASSISTIVE MANIPULATION<\/span>\n <h2><span class=\"abbr-tag\">NHURO-Care:<\/span> Integration and Force-Limited Control of an Active Bionic Grasper for Assistive Humanoid Tasks<\/h2>\n \n <span class=\"section-header\">Problem Description<\/span>\n <p>This project focuses on the integration of a motorized active grasper to allow for physical interaction with light objects like medicine bottles.<\/p>\n\n <div class=\"hint-box\">\n <strong>Project Blueprint & New Files:<\/strong>\n <ul>\n <li><code>nhuro_interfaces\/action\/Grasp.action<\/code>: Define the goal (open\/close) and feedback (pressure\/torque).<\/li>\n <li><code>nhuro_driver\/nhuro\/robot.py<\/code>: Update the hardware library with a <code>move_grasper()<\/code> method.<\/li>\n <li><strong>Design Hint:<\/strong> Physically swap the static hand for the motorized replacement kit and map the new Servo ID.<\/li>\n <\/ul>\n <\/div>\n\n <div class=\"eval-box\">\n <strong>Evaluation Standards:<\/strong> Grasp success rate (%), load-bearing capacity (grams), and torque accuracy to prevent crushing objects.\n <\/div>\n\n <div class=\"ref-section\">\n <strong>Academic References:<\/strong>\n <a class=\"ref-link\" href=\"https:\/\/www.mdpi.com\/2076-0825\/11\/1\/22\" target=\"_blank\">\ud83d\udcc4 Design of Low-Cost Bionic Grippers for Assistive Robots<\/a>\n <a class=\"ref-link\" href=\"https:\/\/arxiv.org\/pdf\/2603.08142\" target=\"_blank\">\ud83d\udcc4 Force Control in Humanoid Hand Manipulation (IEEE)<\/a>\n <a class=\"ref-link\" href=\"https:\/\/arxiv.org\/abs\/2603.08142\" target=\"_blank\">\ud83d\udcc4 Multifingered force-aware control for humanoid robots<\/a>\n <a class=\"ref-link\" href=\"https:\/\/ieeexplore.ieee.org\/document\/9810841\" target=\"_blank\">\ud83d\udcc4 Multifingered Robot Hand Compliant Manipulation Based on Human\u2011in\u2011the\u2011Loop Learning\u2011Control<\/a>\n <\/div>\n <\/div>\n\n <div class=\"project-card\" style=\"border-top-color: var(--neural);\">\n <span class=\"project-tag\">NEURAL NETWORKS & CONTROL<\/span>\n <h2><span class=\"abbr-tag\">NHURO-Brain:<\/span> Design of an Artificial Neural Network Controller for Dynamic Gait Optimization in Bipedal Humanoids<\/h2>\n \n <span class=\"section-header\">Problem Description<\/span>\n <p>Hard-coded patterns are unstable on uneven terrain. The student must develop a neural controller that adjusts the gait dynamically based on IMU feedback.<\/p>\n\n <div class=\"hint-box\">\n <strong>Project Blueprint & New Files:<\/strong>\n <ul>\n <li><code>nhuro_driver\/imu_listener_node.py<\/code>: Collect orientation data ($pitch$, $roll$) from the MPU6050.<\/li>\n <li><code>nhuro_driver\/neural_gait_node.py<\/code>: Run a trained ANN to predict optimal servo angle offsets.<\/li>\n <li><strong>Design Hint:<\/strong> Train the model using PyTorch and deploy for real-time inference on the Pi.<\/li>\n <\/ul>\n <\/div>\n\n <div class=\"eval-box\">\n <strong>Evaluation Standards:<\/strong> Balance stability (variance in IMU data), inference latency (ms), and successful walk distance without falling.\n <\/div>\n\n <div class=\"ref-section\">\n <strong>Academic References:<\/strong>\n <a class=\"ref-link\" href=\"https:\/\/ieeexplore.ieee.org\/document\/895253\" target=\"_blank\">\ud83d\udcc4 Design of central pattern generator for humanoid robot walking based on multi-objective GA (IEEE)<\/a>\n <a class=\"ref-link\" href=\"https:\/\/www.mdpi.com\/2227-7390\/13\/6\/954\" target=\"_blank\">\ud83d\udcc4 ZNN\u2011Based Gait Optimization for Humanoid Robots with ALIP and Inequality Constraints<\/a>\n <a class=\"ref-link\" href=\"https:\/\/arxiv.org\/pdf\/2401.16889\" target=\"_blank\">\ud83d\udcc4 Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control<\/a>\n <a class=\"ref-link\" href=\"https:\/\/ieeexplore.ieee.org\/document\/10875312\" target=\"_blank\">\ud83d\udcc4 Bipedal Robot Gait Control Strategies Based on Reinforcement Learning<\/a>\n <\/div>\n <\/div>\n\n <div class=\"project-card\" style=\"border-top-color: var(--info);\">\n <span class=\"project-tag\">HUMAN-ROBOT INTERACTION<\/span>\n <h2><span class=\"abbr-tag\">NHURO-Interface:<\/span> Development of a Low-Latency Web Dashboard and Digital Twin for Remote Humanoid Telemetry<\/h2>\n \n <span class=\"section-header\">Problem Description<\/span>\n <p>Students must build a low-latency web dashboard to visualize \"internal thoughts\" (voice logs) and \"physical state\" (step count).<\/p>\n\n <div class=\"hint-box\">\n <strong>Project Blueprint & New Files:<\/strong>\n <ul>\n <li><code>~\/nhuro_project\/dashboard\/app.js<\/code>: Implement a WebSocket client using <code>roslibjs<\/code>.<\/li>\n <li><code>~\/nhuro_project\/dashboard\/index.html<\/code>: Design a visual UI to show step counts and voice logs.<\/li>\n <li><strong>Design Hint:<\/strong> Use the <code>rosbridge_suite<\/code> to communicate between the browser and the ROS workspace.<\/li>\n <\/ul>\n <\/div>\n\n <div class=\"eval-box\">\n <strong>Evaluation Standards:<\/strong> UI Refresh rate (Hz), data synchronization accuracy, and cross-compatibility.\n <\/div>\n\n <div class=\"ref-section\">\n <strong>Academic References:<\/strong>\n <a class=\"ref-link\" href=\"https:\/\/sciota-robotics.com\/2024\/09\/09\/robotics-ui\/\" target=\"_blank\">\ud83d\udcc4 Introducing Robotics UI: A Web Interface Solution for ROS 2 Robots <\/a>\n <a class=\"ref-link\" href=\"https:\/\/robotwebtools.github.io\/\" target=\"_blank\">\ud83d\udcc4 Documentation: Robot Web Tools (Humble)<\/a>\n <a class=\"ref-link\" href=\"https:\/\/ieeexplore.ieee.org\/document\/10269714\" target=\"_blank\">\ud83d\udcc4 Low\u2011Latency Communications for Digital Twin Empowered Systems<\/a>\n <a class=\"ref-link\" href=\"https:\/\/arxiv.org\/pdf\/2409.04639\" target=\"_blank\">\ud83d\udcc4 2. High\u2011Speed and Impact\u2011Resilient Teleoperation of Humanoid Robots<\/a>\n <\/div>\n <\/div>\n\n <div class=\"project-card\" style=\"border-top-color: #f1c40f;\">\n <span class=\"project-tag\">ROBOTIC DYNAMICS & SAFETY<\/span>\n <h2><span class=\"abbr-tag\">NHURO-Safe:<\/span> Autonomous Fall Detection and Recovery Strategies for Humanoid Robots via Inertial Sensory Feedback<\/h2>\n \n <span class=\"section-header\">Problem Description<\/span>\n <p>Bipedal robots are prone to falling. The student must detect falls and execute autonomous \"Stand-Up\" sequences via IMU monitoring.<\/p>\n\n <div class=\"hint-box\">\n <strong>Project Blueprint & New Files:<\/strong>\n <ul>\n <li><code>nhuro_driver\/fall_manager_node.py<\/code>: Monitor pitch\/roll thresholds to detect balance loss.<\/li>\n <li><code>nhuro_interfaces\/action\/StandUp.action<\/code>: Define the multi-phase stand-up sequence.<\/li>\n <li><strong>Design Hint:<\/strong> Develop high-torque servo patterns to safely return the robot to a neutral pose.<\/li>\n <\/ul>\n <\/div>\n\n <div class=\"eval-box\">\n <strong>Evaluation Standards:<\/strong> Detection latency (ms), success rate of stand-up recovery, and prevention of servo overheating.\n <\/div>\n\n <div class=\"ref-section\">\n <strong>Academic References:<\/strong>\n \n <a class=\"ref-link\" href=\"https:\/\/ieeexplore.ieee.org\/document\/7353740\" target=\"_blank\">\n \ud83d\udcc4 Fall Detection and Standing Up Strategy for Humanoids\n <\/a>\n\n <a class=\"ref-link\" href=\"https:\/\/ieeexplore.ieee.org\/document\/9065191\" target=\"_blank\">\n \ud83d\udcc4 Reflex Balance Schemes and Fast Fall Impact Mitigation Mechanisms\n <\/a>\n\n <a class=\"ref-link\" href=\"https:\/\/ieeexplore.ieee.org\/document\/6651510\" target=\"_blank\">\n \ud83d\udcc4 Multi\u2011Phase Trajectory Optimization for Post\u2011Fall Autonomous Humanoid Recovery\n <\/a>\n\n <a class=\"ref-link\" href=\"https:\/\/www.frontiersin.org\/articles\/10.3389\/frobt.2020.00072\/full\" target=\"_blank\">\n \ud83d\udcc4 Whole\u2011Body Control for Humanoid Fall Mitigation\n <\/a>\n<\/div>\n\n <\/div>\n\n <a href=\"https:\/\/giatec.io\/aizuhand\/index.php\/nhuro-nexus\/\" class=\"nav-link\">← Return to NHURO Nexus Portal<\/a>\n<\/div>\n\n<div style=\"text-align: center; padding: 20px; color: #888; font-size: 0.8em;\">\n © 2026 NHURO Nexus | Advanced Computing Systems Lab | Abderazek Ben Abdallah, Z. Wang\n<\/div>\n\n<\/body>\n<\/html>\n","protected":false},"excerpt":{"rendered":"<p>Research Projects | NHURO Nexus NHURO Research Sandbox University of Aizu | Advanced Computer Systems Lab | AY2026 Graduation Thesis Framework \ud83d\udcc2 Master SDK Source Hierarchy (Base Structure) Students must adhere to this architecture. New nodes and interfaces should be integrated into the specified directories to ensure system compatibility. nhuro_project\/ \u251c\u2500\u2500 voice_bridge.py<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"saved_in_kubio":false,"footnotes":""},"class_list":["post-641","page","type-page","status-publish","hentry"],"kubio_ai_page_context":{"short_desc":"","purpose":"general"},"_links":{"self":[{"href":"https:\/\/giatec.io\/aizuhand\/index.php\/wp-json\/wp\/v2\/pages\/641","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/giatec.io\/aizuhand\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/giatec.io\/aizuhand\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/giatec.io\/aizuhand\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/giatec.io\/aizuhand\/index.php\/wp-json\/wp\/v2\/comments?post=641"}],"version-history":[{"count":45,"href":"https:\/\/giatec.io\/aizuhand\/index.php\/wp-json\/wp\/v2\/pages\/641\/revisions"}],"predecessor-version":[{"id":708,"href":"https:\/\/giatec.io\/aizuhand\/index.php\/wp-json\/wp\/v2\/pages\/641\/revisions\/708"}],"wp:attachment":[{"href":"https:\/\/giatec.io\/aizuhand\/index.php\/wp-json\/wp\/v2\/media?parent=641"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}