khang nguyen

🍀 four-leaf clover

class ContactInformationCard: 
  def __init__(self):
    self.dept  = "robotics @ mbzuai" 
    self.lab   = "netbot lab" 
    self.email = "khang.nguyen@mbzuai.ac.ae" 
    self.phone = "(+971) 56 937 2539" 
  def flipCard(self):
    print("tap on the card to flip.")
  def closeCard(self):
    print("tap outside to close it.")

khang nguyen

mkhangg.com

khang nguyen /kʰæŋ/ 🎧

I am currently a research assistant in the Department of Robotics of the Mohamed bin Zayed University of Artificial Intelligence, working at the NetBot Lab under Dr. Dezhen Song. Prior to this, I spent time in both Department of Computer Science and Engineering and Department of Bioengineering of the University of Texas at Arlington, where I was at the Learning and Adaptive Robotics Lab under Dr. Manfred Huber and at the Multimodal Imaging and Neuromodulation Lab under Dr. Hanli Liu.

I grew up in Saigon, Vietnam, and was fortunate to spend my most memorable time at the VNU-HCM High School for the Gifted (informatics program of 2020) and previously TĐN Secondary School for the Gifted (mathematics program of 2017).

From collaborative robots to humanoids, my research focuses on exploring explainable robot learning paradigms for vision-tactile grasping and whole-body loco-manipulation. Two core directions are (i) multimodal visual-tactile learning to advance robotic manipulation skills, and (ii) long-horizon planning and control for full-body loco-manipulation tasks, guided by neuromorphic mechanisms, such as metacognition, self-inference, and introspection, in diverse unstructured settings under real-world uncertainty and stochasticity.

Vision-Tactile Grasping: multimodal visual-tactile representation, manipulative visual-motor skills
Whole-Body Loco-Manipulation: learning for bipedal locomotion, long-horizon loco-manipulation tasks

📜 updates

I will attend IROS 2025 in Hangzhou, China.

October 2025

I gave a mini-talk at the weekly RoboCoffee seminar at MBZUAI.

October 2025

I am invited to review papers at ICRA 2026 and IEEE RA-L.

September 2025

I am pleased to be invited to give a virtual talk at PiMA in Vietnam.

August 2025

I am officially on board for my research position at MBZUAI!

July 2025

I am pleased to be invited to give a talk at VinRobotics in Vietnam.

June 2025

FlowMP and Liquid Pouch Actuator are accepted to IROS 2025!

June 2025

I am invited to review papers at Humanoids 2025 and IEEE T-RO.

May 2025

I will attend ICRA 2025 in Atlanta, Georgia.

May 2025

I am invited to review papers at IROS 2025.

April 2025

I am invited to review papers at IEEE RA-L and Neurocomputing.

February 2025

I will be the GTA for CSE 1310 (Programming) course this semester!

January 2025

Our paper on distortion-aware images is featured at VISAPP 2025.

December 2024

Our poster is presented at the North Texas BME Symposium at UTSW.

November 2024

I am invited to review papers at ICRA 2025, IEEE TNNLS, and IEEE RA-L.

October 2024

I will serve as co-chair for the Object Detection session at IROS 2024.

October 2024

I will attend IROS 2024 in Abu Dhabi, UAE.

October 2024

I will be the GTA for CSE 4360 (Robotics) course this semester!

August 2024

V3D-SLAM and Refined PanMap papers are accepted to IROS 2024!

June 2024

I will continue my PhD studies in robotics at the LEARN Lab.

May 2024

I finished my undergrad studies in computer science at UTA.

May 2024

I will attend IROS 2023 in Detroit, Michigan.

October 2023

I will present at ISR 2023 in Stuttgart, Germany.

September 2023

The deformable object classification paper is accepted to ISR 2023!

June 2023

The multiplanar self-calibration paper is accepted to IROS 2023!

June 2023

My thesis proposal is approved by the Honors College!

June 2023

I am invited to review papers at CASE 2023.

April 2023

The PerFC paper is accepted to FLAIRS-36!

March 2023

I am invited to review papers at UR 2023.

February 2023

Spidey won sponsorship prizes at HackMIT 2022!

October 2022

I joined the Learning and Adaptive Robotics (LEARN) Lab at UTA.

August 2022

The IoTree paper is accepted to MobiCom 2022!

June 2022

iPlanter won prizes at GaTech RoboTech Hackathon 2022!

April 2022

I joined the Wireless and Sensor Systems Lab (WSSL) at UTA.

August 2021

I start my undergrad at the University of Texas at Arlington (UTA).

August 2020

📚 publications

Multimodal Reasoning-Aware Generalist Robotic Policies via Teacher-Guided Fine-Tuning
arXiv (25/05/2025)
Tuan Van Vo, Quang-Tan Nguyen, Khang Nguyen, Nhat Xuan Tran, Duy H. M. Nguyen, An Thai Le, Ngo Anh Vien, Minh Nhat Vu.
[PDF]
Abstract: Vision-Language-Action (VLA) models have gained much attention from the research community thanks to their strength in translating multimodal observations with linguistic instructions into desired robotic actions. Despite their advancements, VLAs often overlook explicit reasoning and learn the functional input-action mappings, omitting crucial logical steps, which are especially pronounced in interpretability and generalization for complex, long-horizon manipulation tasks. In this work, we propose ReFineVLA, a multimodal reasoning-aware framework that fine-tunes VLAs with teacher-guided reasons. We first augment robotic datasets with reasoning rationales generated by an expert teacher model, guiding VLA models to learn to reason about their actions. Then, we fine-tune pre-trained VLAs with the reasoning-enriched datasets with ReFineVLA, while maintaining the underlying generalization abilities and boosting reasoning capabilities. We also conduct attention map visualization to analyze the alignment among visual observation, linguistic prompts, and to-be-executed actions of ReFineVLA, reflecting the model's ability to focus on relevant tasks and actions. Through this additional step, we explore that ReFineVLA-trained models exhibit a meaningful agreement between vision-language and action domains, highlighting the enhanced multimodal understanding and generalization. Evaluated across a suite of simulated manipulation benchmarks on SimplerEnv with both WidowX and Google Robot tasks, ReFineVLA achieves state-of-the-art performance, with an average 5.0% improvement in success rate over the second-best method on the WidowX benchmark, reaching 47.7% task success. In more visually and contextually diverse scenarios, ReFineVLA yields 3.5% and 2.3% gains in variant aggregation (68.8%) and visual matching (76.6%) settings, respectively. Notably, it improves performance by 9.6% on the Move Near task and 8.2% on Open/Close Drawer in challenging settings. ... See More

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation
arXiv (05/09/2025)
Tien Pham, Xinyun Chi, Khang Nguyen, Manfred Huber, Angelo Cangelosi.
[PDF]
Abstract: Reinforcement learning (RL) agents can learn to solve complex tasks from visual inputs, but generalizing these learned skills to new environments remains a major challenge in RL applications, especially robotics. While data augmentation can improve generalization, it often compromises sample efficiency and training stability. This paper introduces DeGuV, an RL framework that enhances both generalization and sample efficiency. In specific, we leverage a learnable masker network that produces a mask from the depth input, preserving only critical visual information while discarding irrelevant pixels. Through this, we ensure that our RL agents focus on essential features, improving robustness under data augmentation. In addition, we incorporate contrastive learning and stabilize Q-value estimation under augmentation to further enhance sample efficiency and training stability. We evaluate our proposed method on the RL-ViGen benchmark using the Franka Emika robot and demonstrate its effectiveness in zero-shot sim-to-real transfer. Our results show that DeGuV outperforms state-of-the-art methods in both generalization and sample efficiency while also improving interpretability by highlighting the most relevant regions in the visual input. ... See More

FlowMP: Learning Motion Fields for Robot Planning with Conditional Flow Matching
IROS 2025 (Hangzhou, China)
Khang Nguyen, An Thai Le, Tien Pham, Manfred Huber, Jan Peters, Minh Nhat Vu.
[PDF] | [CODE]
Abstract: Prior flow matching methods in robotics have primarily learned velocity fields to morph one distribution of trajectories into another. In this work, we extend flow matching to capture second-order trajectory dynamics, incorporating acceleration effects either explicitly in the model or implicitly through the learning objective. Unlike diffusion models, which rely on a noisy forward process and iterative denoising steps, flow matching trains a continuous transformation (flow) that directly maps a simple prior distribution to the target trajectory distribution without any denoising procedure. By modeling trajectories with second-order dynamics, our approach ensures that generated robot motions are smooth and physically executable, avoiding the jerky or dynamically infeasible trajectories that first-order models might produce. We empirically demonstrate that this second-order conditional flow matching yields superior performance on motion planning benchmarks, achieving smoother trajectories and higher success rates than baseline planners. These findings highlight the advantage of learning acceleration-aware motion fields, as our method outperforms existing motion planning methods in terms of trajectory quality and planning success. ... See More

Modeling The States of Liquid Phase Change Pouch Actuators by Reservoir Computing
IROS 2025 (Hangzhou, China)
Cedric Caremel*, Khang Nguyen*, Anh Nguyen, Manfred Huber, Yoshihiro Kawahara, Tung D. Ta.
[PDF] | [CODE]
Abstract: Liquid phase change pouch actuators (liquid pouch motors) hold great promise for a wide range of robotic applications, from artificial organs to pneumatic manipulators for dexterous manipulation. However, the usability of liquid pouch motors remains challenging due to the nonlinear intrinsic properties of liquids and their respective highly dynamic implications for liquid-gas phase changes, complicating state modeling and estimation. To resolve these problems, we present a reservoir computing-based method for modeling the inflation states of a customized liquid pouch motor, serving as an actuator, with four Peltier heating junctions. We use a motion capture system to track the landmark movements on the pouch, a proxy for its volumetric profile. These movements represent the internal liquid-gas phase changes of the pouch with stable room temperature, atmospheric pressure, and electrical noises. The motion coordinates are thus learned by our reservoir computing framework, PhysRes, to model the states based on prior observations. Through training, our model achieves excellent results on the test set, with a normalized root mean squared error of 0.0041 in estimating the control points and a corresponding volumetric error of 0.0160%. To further demonstrate how such actuators could be implemented in the future, we also design a dual-pouch actuator-based robotic gripper to control the grasping of soft objects. ... See More

Distortion-Aware Adversarial Attacks on Bounding Boxes of Object Detectors
VISAPP 2025 (Porto, Portugal)
Phuc Pham, Son Vuong, Khang Nguyen, Tuan Dang.
[PDF] | [CODE] | [DEMO]
Abstract: Deep learning-based object detection has become ubiquitous in the last decade due to its high accuracy in many real-world applications. With this growing trend, these models are interested in being attacked by adversaries, with most of the results being on classifiers, which do not match the context of practical object detection. In this work, we propose a novel method to fool object detectors, expose the vulnerability of state-of-the-art detectors, and promote later works to build more robust detectors to adversarial examples. Our method aims to generate adversarial images by perturbing object confidence scores during training, which is crucial in predicting confidence for each class in the testing phase. Herein, we provide a more intuitive technique to embed additive noises based on detected objects' masks and the training loss with distortion control over the original image by leveraging the gradient of iterative images. To verify the proposed method, we perform adversarial attacks against different object detectors, including the most recent state-of-the-art models like YOLOv8, Faster R-CNN, RetinaNet, and Swin Transformer. We also evaluate our technique on MS COCO 2017 and PASCAL VOC 2012 datasets and analyze the trade-off between success attack rate and image distortion. Our experiments show that the achievable success attack rate is up to 100% and up to 98% when performing white-box and black-box attacks, respectively. ... See More

Hand-Eye-Force Coordination for Robotic Manipulation
Bachelor Thesis @ UTA
Khang Nguyen.
[PDF]
Abstract: In vision-based robotic manipulation, when a robot identifies an object to grasp, the knowledge of the object's positional, geometrical, and physical properties is not perfect. Deformable objects, such as soda cans, plastic bottles, and paper cups, pose the best challenges in learning the uncertainty of these properties in terms of grasping. To grasp these, the robot must adaptively control and coordinate its hands, eyes, and fingertip forces to such objects under diverse unstructured representations. In other words, the robot's hands, eyes, and the amount of applied forces must be well-coordinated. This thesis explores the fundamentals of human-inspired mechanisms and applies them to robot grasping to develop hand-eye-force coordination for deformable object manipulation. With an object-finding task, the robot encountered an unstructured environment cluttered with known objects. First, it must look at the environment's overview and store the scene's semantic information for later object-finding iterations. With the information stored, the robot must find the desired object, grasp it, and bring it back. To achieve the perception goal, the robot is first enabled to perceive the environment as a whole, like when humans encounter a newly explored scene, and to learn to recognize objects efficiently in three-dimensional space by emulating the visual selective attention model. Lastly, in some special cases, the robot might encounter an already-deformed object due to manipulative results by humans or itself in later iterations. To refine this more efficiently, the robot is also trained to re-recognize these items through a synthetic deformable object dataset, which is auto-generated using an intuitive Laplacian-based mesh deformation procedure. Throughout this thesis, these sub-problems are addressed, and the feasibility of each is demonstrated with experiments on a real robot system. ... See More

Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots
IROS 2024 (Abu Dhabi, United Arab Emirates)
Khang Nguyen, Tuan Dang, Manfred Huber.
[PDF] | [CODE] | [DEMO] | [SLIDES] | [TALK] | [POSTER]
Abstract: Reconstructing three-dimensional (3D) scenes with semantic understanding is vital in many robotic applications. Robots need to identify which objects, along with their positions and shapes, to manipulate them precisely with given tasks. Mobile robots, especially, usually use lightweight networks to segment objects on RGB images and then localize them via depth maps; however, they often encounter out-of-distribution scenarios where masks over-cover the objects. In this paper, we address the problem of panoptic segmentation quality in 3D scene reconstruction by refining segmentation errors using non-parametric statistical methods. To enhance mask precision, we map the predicted masks into a depth frame to estimate their distribution via kernel densities. The outliers in depth perception are then rejected without the need for additional parameters in an adaptive manner to out-of-distribution scenarios, followed by 3D reconstruction using projective signed distance functions (SDFs). We validate our method on a synthetic dataset, which shows improvements in both quantitative and qualitative results for panoptic mapping. Through real-world testing, the results furthermore show our method's capability to be deployed on a real-robot system. ... See More

V3D-SLAM: Robust RGB-D SLAM in Dynamic Environments with 3D Semantic Geometry Voting
IROS 2024 (Abu Dhabi, United Arab Emirates)
Tuan Dang, Khang Nguyen, Manfred Huber.
[PDF] | [CODE] | [DEMO] | [SLIDES] | [TALK] | [POSTER]
Abstract: Simultaneous localization and mapping (SLAM) in highly dynamic environments is challenging due to the correlation complexity between moving objects and the camera pose. Many methods have been proposed to deal with this problem; however, the moving properties of dynamic objects with a moving camera remain unclear. Therefore, to improve SLAM's performance, minimizing disruptive events of moving objects with a physical understanding of 3D shapes and dynamics of objects is needed. In this paper, we propose a robust method, V3D-SLAM, to remove moving objects via two lightweight re-evaluation stages, including identifying potentially moving and static objects using a spatial-reasoned Hough voting mechanism and refining static objects by detecting dynamic noise caused by intra-object motions using Chamfer distances as similarity measurements. Through our experiment on the TUM RGB-D benchmark on dynamic sequences with ground-truth camera trajectories, the results show that our methods outperform most other recent state-of-the-art SLAM methods. ... See More

Real-Time 3D Semantic Scene Perception for Egocentric Robots with Binocular Vision
arXiv (19/02/2024)
Khang Nguyen, Tuan Dang, Manfred Huber.
[PDF] | [CODE] | [DEMO]
Abstract: Perceiving a three-dimensional (3D) scene with multiple objects while moving indoors is essential for vision-based mobile cobots, especially for enhancing their manipulation tasks. In this work, we present an end-to-end pipeline with instance segmentation, feature matching, and point-set registration for egocentric robots with binocular vision, and demonstrate the robot's grasping capability through the proposed pipeline. First, we design an RGB image-based segmentation approach for single-view 3D semantic scene segmentation, leveraging common object classes in 2D datasets to encapsulate 3D points into point clouds of object instances through corresponding depth maps. Next, 3D correspondences of two consecutive segmented point clouds are extracted based on matched keypoints between objects of interest in RGB images from the prior step. In addition, to be aware of spatial changes in 3D feature distribution, we also weigh each 3D point pair based on the estimated distribution using kernel density estimation (KDE), which subsequently gives robustness with less central correspondences while solving for rigid transformations between point clouds. Finally, we test our proposed pipeline on the 7-DOF dual-arm Baxter robot with a mounted Intel RealSense D435i RGB-D camera. The result shows that our robot can segment objects of interest, register multiple views while moving, and grasp the target object. ... See More

Online 3D Deformable Object Classification for Mobile Cobot Manipulation
ISR Europe 2023 (Stuttgart, Baden-Wurttemberg, Germany)
Khang Nguyen, Tuan Dang, Manfred Huber.
[PDF] | [CODE] | [DEMO] | [SLIDES] | [TALK]
Abstract: Vision-based object manipulation in assistive mobile cobots essentially relies on classifying the target objects based on their 3D shapes and features, whether they are deformed or not. In this work, we present an auto-generated dataset of deformed objects specific for assistive mobile cobot manipulation using an intuitive Laplacian-based mesh deformation procedure. We first determine the graspable region of the robot hand on the given object's mesh. Then, we uniformly sample handle points within the graspable region and perform deformation with multiple handle points based on the robot gripper configuration. In each deformation, we identify the orientation of handle points and prevent self-intersection to guarantee the object's physical meaning when multiple handle points are simultaneously applied to the mesh at different deformation intensities. We also introduce a lightweight neural network for 3D deformable object classification. Finally, we test our generated dataset on the Baxter robot with two 7-DOF arms, an integrated RGB-D camera, and a 3D deformable object classifier. The result shows that the robot is able to classify real-world deformed objects from point clouds captured at multiple views by the RGB-D camera. ... See More

Multiplanar Self-Calibration for Mobile Cobot 3D Object Manipulation using 2D Detectors and Depth Estimation
IROS 2023 (Detroit, MI, U.S.)
Tuan Dang, Khang Nguyen, Manfred Huber.
[PDF] | [CODE] | [DEMO]
Abstract: Calibration is the first and foremost step in dealing with sensor displacement errors that can appear during extended operation and off-time periods to enable robot object manipulation with precision. In this paper, we present a novel multiplanar self-calibration between the camera system and the robot's end-effector for 3D object manipulation. Our approach first takes the robot end-effector as ground truth to calibrate the camera’s position and orientation while the robot arm moves the object in multiple planes in 3D space, and a 2D state-of-the-art vision detector identifies the object’s center in the image coordinates system. The transformation between world coordinates and image coordinates is then computed using 2D pixels from the detector and 3D known points obtained by robot kinematics. Next, an integrated stereo-vision system estimates the distance between the camera and the object, resulting in 3D object localization. We test our proposed method on the Baxter robot with two 7-DOF arms and a 2D detector that can run in real time on an onboard GPU. After self-calibrating, our robot can localize objects in 3D using an RGB camera and depth image. ... See More

ExtPerFC: An Efficient 2D & 3D Perception Software-Hardware Framework for Mobile Cobot
arXiv (08/06/2023)
Tuan Dang, Khang Nguyen, Manfred Huber.
[PDF] | [CODE] | [DEMO]
Abstract: As the reliability of the robot's perception correlates with the number of integrated sensing modalities to tackle uncertainty, a practical solution to manage these sensors from different computers, operate them simultaneously, and maintain their real-time performance on the existing robotic system with minimal effort is needed. In this work, we present an end-to-end software-hardware framework, namely ExtPerFC, that supports both conventional hardware and software components and integrates machine learning object detectors without requiring an additional dedicated graphic processor unit (GPU). We first design our framework to achieve real-time performance on the existing robotic system, guarantee configuration optimization, and concentrate on code reusability. We then mathematically model and utilize our transfer learning strategies for 2D object detection and fuse them into depth images for 3D depth estimation. Lastly, we systematically test the proposed framework on the Baxter robot with two 7-DOF arms, a four-wheel mobility base, and an Intel RealSense D435i RGB-D camera. The results show that the robot achieves real-time performance while executing other tasks (e.g., map building, localization, navigation, object detection, arm moving, and grasping) simultaneously with available hardware like Intel onboard CPUs/GPUs on distributed computers. Also, to comprehensively control, program, and monitor the robot system, we design and introduce an end-user application. ... See More

PerFC: An Efficient 2D and 3D Perception Software-Hardware Framework for Mobile Cobot
FLAIRS-36 (Clearwater Beach, FL, U.S.)
Tuan Dang, Khang Nguyen, Manfred Huber.
[PDF] | [CODE] | [DEMO]
Abstract: In this work, we present an end-to-end software-hardware framework that supports both conventional hardware and software components and integrates machine learning object detectors without requiring an additional dedicated graphic processor unit (GPU). We design our framework to achieve real-time performance on the robot system, guarantee such performance on multiple computing devices, and concentrate on code reusability. We then utilize transfer learning strategies for 2D object detection and fuse them into depth images for 3D depth estimation. Lastly, we test the proposed framework on the Baxter robot with two 7-DOF arms and a four-wheel mobility base. The results show that the robot achieves real-time performance while executing other tasks (map building, localization, navigation, object detection, arm moving, and grasping) with available hardware like Intel onboard GPUs on distributed computers. Also, to comprehensively control, program, and monitor the robot system, we design and introduce an end-user application. ... See More

IoTree: A Battery-free Wearable System with Biocompatible Sensors for Continuous Tree Health Monitoring
MobiCom 2022 (Sydney, NSW, Australia)
Tuan Dang, Trung Tran, Khang Nguyen, Tien Pham, Nhat Pham, Tam Vu, Phuc Nguyen.
[PDF] | [CODE] | [DEMO]
Abstract: In this paper, we present a low-maintenance, wind-powered, batteryfree, biocompatible, tree wearable, and intelligent sensing system, namely IoTree, to monitor water and nutrient levels inside a living tree. IoTree system includes tiny-size, biocompatible, and implantable sensors that continuously measure the impedance variations inside the living tree’s xylem, where water and nutrients are transported from the root to the upper parts. The collected data are then compressed and transmitted to a base station located at up to 1.8 kilometers (approximately 1.1 miles) away. The entire IoTree system is powered by wind energy and controlled by an adaptive computing technique called block-based intermittent computing, ensuring the forward progress and data consistency under intermittent power and allowing the firmware to execute with the most optimal memory and energy usage. We prototype IoTree that opportunistically performs sensing, data compression, and long-range communication tasks without batteries. During in-lab experiments, IoTree also obtains the accuracy of 91.08% and 90.51% in measuring 10 levels of nutrients, NH₃ and K₂O, respectively. While tested with Burkwood Viburnum and White Bird trees in the indoor environment, IoTree data strongly correlated with multiple watering and fertilizing events. We also deployed IoTree on a grapevine farm for 30 days, and the system is able to provide sufficient measurements every day. ... See More

🧩 outreach activities

Lightweight Semantic Perception Module for Autonomous Systems
Senior Design (Arlington, TX, U.S.)
Zobia Tahir, Diya Ranjit, Jose Morales, ChangHao Yang, Khang Nguyen.
[POST]
Description: We build a versatile software module for semantic scene understanding for broadly vision-based automation applications, including robots or unmanned vehicles installed with the Intel RealSense camera family with resource-constraint computing platforms. The video demonstrates the Sawyer manipulator to "see" the bottle, pick it, and put it into a destination box.

Autonomous Waypoint Navigation with GPS and ArduRover
UVS Design (Arlington, TX, U.S.)
Kevin Mathew, Jesus Garza Munoz, Benjamin Nguyen, Khang Nguyen.
[POST] | [DEMO]
Description: We design the rover platform with hardware-software integration for autonomous waypoint navigation using Here3 GPS and ArduRover for outdoor activities. The video demonstrates the rover's mission to go through 10 defined waypoints on the UTA campus through Mission Planner via wireless communication through the Hex cube (Pixhawk 2.1) controller.

Spidey: An Autonomous Spatial Voice Localization Crawling Robot
HackMIT 2022 (Boston, MA, U.S.)
Khang Nguyen.
[POST] | [CODE] | [DEMO]
Description: We present an autonomous spatial voice localization crawling robot demonstrating the potential of assistive technology for people with visual impairment to ask for help whenever they are in a spatial area without physical assistance.
Prize: Won Sponsorship Award for Assistive Technologies over 198 competing teams.

iPlanter: An Autonomous Ground Monitoring and Tree Planting Robot
GT IEEE RoboTech 2022 (Atlanta, GA, U.S.)
Khang Nguyen, Muhtasim Mahfuz, Vincent Kipchoge, Johnwon Hyeon.
[POST] | [CODE] | [DEMO]
Description: Our tree planting robot demonstrates the an on-farm surveying robot that autonomously determines soil quality, plants seeds, and collects on-ground images.
Prize: Won 2nd place in Body Track, 3rd place in Electrical Track, and Top 8 Prizes over 47 competing teams (approximately 160 participants).