Semester and Thesis Projects

The ETH AI Center offers a wide range of semester and thesis projects for students at ETH Zurich, as well as other universities. Please see the list below for projects that are currently available.

How do you publish a thesis or semester project with the ETH AI Center?

Academia: If you are affiliated with the ETH AI Center as faculty member, post- or doctoral fellow, please add the following affiliation to your Sirop account. If you tag your thesis project with this affiliation, it should appear in the list below.
- 'ETH Competence Center - ETH AI Center (ETHZ)'
Industry: If you represent a company that has a corporate partnership with the ETH AI Center, please contact .

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We provide a external page template for thesis projects accouncements that you can use for your upcoming project & upload to external page SiROP.
Is your thesis project still missing from the list below? We are constantly adding new thesis projects that are available within the ETH AI Center.
Are you a student? Check out our Semester and Thesis projects below!

Engineering Wearable Platforms

This project focuses on the design, engineering, and prototyping of next-generation wearable technologies. The goal is to create microfluidic platforms that enable automatic sampling, reliable fluid control, and seamless integration of diverse sensing modalities.

Keywords

Wearables, materials, microfluidics

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Internship , Master Thesis , Student Assistant / HiWi

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Published since: 2025-09-16 , Earliest start: 2025-09-21 , Latest end: 2026-09-01

Applications limited to Balgrist Campus , ETH Zurich , University of Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne , Empa , Zurich University of Applied Sciences

Organization ETH Competence Center - ETH AI Center

Hosts Dosnon Lucas

Topics Engineering and Technology

Reproduction Study Of Reinforcement-Learning Control for Intelligent Road Transportation Systems

A growing branch of research is concerned with traffic control based on deep and reinforcement learning, to leverage measurement data and learn the complex, non-linear dynamics of road transportation systems. The goal of this assignment is to better understand the challenges, obstacles, and ways of working when reproducing reinforcement learning based controllers from the literature, and to derive guidance for facilitation of future reproducibility studies. Moreover, best practices for publishing more reproducible research are of interest.

Keywords

Reinforcement Learning; Reproducibility; Python; SUMO; TRACI

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Semester Project , Course Project , Internship , Bachelor Thesis , Master Thesis , Student Assistant / HiWi , ETH Zurich (ETHZ)

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**Background Information:** The control of road transportation systems aims to improve traffic efficiency by reducing congestion. It involves intersection management with traffic lights, perimeter control on a urban network level, and variable speed limit control and ramp metering for highway. Intelligent road transportation systems are hard to model, and different descriptions on macroscopic state level, down to microscopic vehicle levels exist, which make their control challenging. Besides analytic control methods, a growing branch of research is concerned with control based on deep and reinforcement learning, to leverage measurement data and learn the complex, non-linear dynamics of road transportation systems. **Task Description:** In this assignment, we would like to systematically reproduce, implement and benchmark reinforcement learning based control systems. A project typically involves following tasks: • Getting familiar with open source traffic simulator SUMO and Python • Building a network and creating a pipeline to train a control model on it • Choose one control problem and related papers, reimplement and reproduce their results • Documenting challenges for reproducibility, and assumptions made on the way **Goal Statement:** The goal of this assignment is to better understand the challenges, obstacles, and ways of working when reproducing reinforcement learning based controllers from the literature, and to derive guidance for facilitation of future reproducibility studies. Moreover, best practices for publishing more reproducible research are of interest. Ultimately, we would like to: • Score the reproducibility of a paper (1-10) with a report justifying why (max 3 pages) • List missing information or assumptions that were crucial for reproduction • Document reproducibility evolution (1st attempt, 2nd attempt, etc.) • Guidelines / lessons learned when trying to reproduce works **Related Literature (Papers to Reproduce):** - **Ramp Metering:** - Pan, Tianlu, et al. "Integrated optimal control strategies for freeway traffic mixed with connected automated vehicles: A model-based reinforcement learning approach." Transportation research part C: emerging technologies 123 (2021): 102987. - Wang, Chong, et al. "Integrated traffic control for freeway recurrent bottleneck based on deep reinforcement learning." IEEE Transactions on Intelligent Transportation Systems 23.9 (2022): 15522-15535. - Sun, Dingshan, Anahita Jamshidnejad, and Bart De Schutter. "A novel framework combining MPC and deep reinforcement learning with application to freeway traffic control." IEEE Transactions on Intelligent Transportation Systems 25.7 (2024): 6756-6769. - Belletti, Francois, et al. "Expert level control of ramp metering based on multi-task deep reinforcement learning." IEEE Transactions on Intelligent Transportation Systems 19.4 (2017): 1198-1207. - Han, Yu, et al. "A physics-informed reinforcement learning-based strategy for local and coordinated ramp metering." Transportation Research Part C: Emerging Technologies 137 (2022): 103584. - Fares, Ahmed, and Walid Gomaa. "Freeway ramp-metering control based on reinforcement learning." 11th IEEE International Conference on Control & Automation (ICCA). IEEE, 2014. - Schmidt-Dumont, Thorsten, and Jan H. van Vuuren. "Decentralised reinforcement learning for ramp metering and variable speed limits on highways." IEEE Transactions on Intelligent Transportation Systems 14.8 (2015): 1. - Lu, Chao, et al. "Coordinated ramp metering with equity consideration using reinforcement learning." Journal of Transportation Engineering, Part A: Systems 143.7 (2017): 04017028. - Fares, Ahmed, and Walid Gomaa. "Multi-agent reinforcement learning control for ramp metering." Progress in Systems Engineering: Proceedings of the Twenty-Third International Conference on Systems Engineering. Cham: Springer International Publishing, 2015. - **Intersection Management:** - Antonio, Guillen-Perez, and Cano Maria-Dolores. "Multi-agent deep reinforcement learning to manage connected autonomous vehicles at tomorrow's intersections." IEEE Transactions on Vehicular Technology 71.7 (2022): 7033-7043. - Klimke, Marvin, Benjamin Völz, and Michael Buchholz. "Automatic intersection management in mixed traffic using reinforcement learning and graph neural networks." 2023 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2023. - Karthikeyan, P., Wei-Lun Chen, and Pao-Ann Hsiung. "Autonomous intersection management by using reinforcement learning." Algorithms 15.9 (2022): 326. - Gunarathna, Udesh, et al. "Real-time intelligent autonomous intersection management using reinforcement learning." 2022 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2022. - **Variable Speed Limit:** - Ke, Zemian, et al. "Enhancing transferability of deep reinforcement learning-based variable speed limit control using transfer learning." IEEE Transactions on Intelligent Transportation Systems 22.7 (2020): 4684-4695. - Kušić, Krešimir, et al. "An overview of reinforcement learning methods for variable speed limit control." Applied Sciences 10.14 (2020): 4917. - Li, Zhibin, et al. "Reinforcement learning-based variable speed limit control strategy to reduce traffic congestion at freeway recurrent bottlenecks." IEEE transactions on intelligent transportation systems 18.11 (2017): 3204-3217. - Kušić, Krešimir, et al. "Extended variable speed limit control using multi-agent reinforcement learning." 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2020. - Gregurić, Martin, Krešimir Kušić, and Edouard Ivanjko. "Impact of deep reinforcement learning on variable speed limit strategies in connected vehicles environments." Engineering applications of artificial intelligence 112 (2022): 104850. - **Perimeter Control:** - Chen, C., et al. "Data efficient reinforcement learning and adaptive optimal perimeter control of network traffic dynamics." Transportation Research Part C: Emerging Technologies 142 (2022): 103759. - Zhou, Dongqin, and Vikash V. Gayah. "Model-free perimeter metering control for two-region urban networks using deep reinforcement learning." Transportation Research Part C: Emerging Technologies 124 (2021): 102949. - Zhou, Dongqin, and Vikash V. Gayah. "Scalable multi-region perimeter metering control for urban networks: A multi-agent deep reinforcement learning approach." Transportation Research Part C: Emerging Technologies 148 (2023): 104033. Links: - Additional remarks: Group work possible, can also be divided into multiple projects Minimum credits: 9 / 11 or 24 ECTS (depending on project/thesis) Recommended lectures: Road Transport Systems (Verkehr 3), Transport Systems, Traffic Engineering, Microscopic Modelling and Simulation of Traffic Operations Additional information: Good skills in programming language Python are required for algorithm implementation and data analysis.

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Published since: 2025-08-29 , Earliest start: 2025-08-29 , Latest end: 2026-12-31

Organization ETH Competence Center - ETH AI Center

Hosts Riehl Kevin

Topics Information, Computing and Communication Sciences , Commerce, Management, Tourism and Services , Behavioural and Cognitive Sciences

Master Thesis Opportunities in Agentic AI Applications at the ETH Agentic Systems Lab

Join the newly founded Agentic Systems Lab at ETH Zurich to develop AI systems with real-world impact. We are inviting 10–20 Master’s students to join our “Genesis Batch 2025” this semester (until the end of the year). Choose from three tailored tracks: Explorer (startup/industry collaboration), Builder (AI for education, science & healthcare), and Bring Your Own Project (develop your own AI venture). Collaborations with leading startups such as Browser-Use (San Francisco) and nunu.ai (Zurich) are already underway, with more to follow.

Keywords

AI, agentic systems, master thesis, ETH Zurich, startups, applied AI, AI ventures, research, healthcare AI, education AI, scientific applications, prototyping, automation

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Master Thesis , ETH Zurich (ETHZ)

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The newly founded **Agentic Systems Lab at ETH Zurich** is offering Master Thesis opportunities as part of our **Genesis Batch 2025**, a collaborative cohort of **10-20 highly motivated students** working on agentic AI. You can select from three distinct tracks: 🎯 **Explorer Track – Applied AI Discovery & Prototyping** - Collaborate with startups and industry partners. - Identify opportunities for AI-driven automation. - Build and evaluate agentic system prototypes. - Current partners include Browser-Use (San Francisco) and nunu.ai (Zürich), with more to come. 🔨 **Builder Track – Advancing AI in Academia & Healthcare** - Contribute to AI projects in education, science, and healthcare. - Collaborate with lab researchers on impactful real-world applications. - Example projects include AI-driven solutions for student evaluation, health coaching, and reviews of academic research. 🚀 **Bring Your Own Project** - Advance Your AI Venture - Ideal for students with an existing prototype or early-stage AI venture. - Use your thesis to strengthen your product, validate it, and prepare for publication or launch. The **emerging field of agentic AI** offers exciting but still largely unexplored research territory. A core outcome of your thesis should be the design, implementation, and evaluation of a working agentic prototype, deployed in a real-world context. You will be expected to evaluate its effectiveness through empirical methods — such as controlled experiments, user studies, or analysis of key performance indicators — and frame your findings in relation to relevant academic literature. Central **research questions** may include: - Which types of agentic system designs are more effective under specific conditions? - How do different user contexts (e.g. education, healthcare, entrepreneurship) influence agent behavior and impact? - What trade-offs emerge between automation, control, and user trust? - How can agentic systems be designed to balance initiative and user control across different domains (e.g., healthcare vs. entrepreneurship)? - What design patterns or interaction modalities (e.g., chat, voice, embedded agents) foster the highest engagement or task completion rates in agentic systems? - Which architectural choices (e.g., reactive vs. goal-based agents, centralised vs. decentralised control) lead to better performance in dynamic environments? Students are expected to formulate such questions based on prior research and address them using their own system evaluations. We strongly support and encourage participants to aim for high-quality conference or journal publications as a tangible outcome of their thesis work. We’re launching this opportunity as a cohort model to promote peer learning, collaboration, and community under the **Genesis Batch 2025**. **Application deadline:** August 31, 2025. **Start date:** No later than October 2025. Duration: 6 months Application details and criteria: https://www.agenticsystemslab.org/

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Published since: 2025-07-25 , Earliest start: 2025-07-28 , Latest end: 2026-03-31

Organization ETH Competence Center - ETH AI Center

Hosts Da Conceição Barata Filipe

Topics Engineering and Technology , Education

RA position: CoMind: Joint Interaction Understanding through Intent Prediction

Understanding human intent and anticipating future actions is crucial for enabling seamless human-robot interaction in real-world tasks. Intention prediction not only facilitates smoother collaboration with robots but also represents a fundamental challenge in the development of intelligent systems. In this project, we aim to collect a dedicated interaction dataset using the Meta Aria glasses and use the collected data to develop multimodal learning models that are capable of predicting human intent and anticipating future behaviors across a variety of tasks.

Keywords

egocentric vision, intent prediction, anticipation, multimodal learning

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Semester Project , Master Thesis , Student Assistant / HiWi , ETH Zurich (ETHZ)

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Published since: 2025-07-14 , Earliest start: 2025-07-14

Organization Computer Vision and Geometry Group

Hosts Wang Xi , Kaufmann Manuel , Chen Jiaqi , Gavryushin Alexey

Topics Information, Computing and Communication Sciences

ZipMPC for Vision-Based Autonomous Racing

This project aims to extend the ZipMPC framework, a method that enables computationally efficient long-horizon model predictive control (MPC) using a compressed, context-aware cost function combined with short-horizon MPC, by incorporating vision-based context. Specifically, we propose to learn the context pertaining to reference trajectory from images, enabling long-horizon vision-informed control, while maintaining the efficiency and constraint satisfaction of short-horizon MPC.

Keywords

Model predictive control, vision-based control, imitation learning, context-dependent control

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Master Thesis

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Published since: 2025-07-08

Organization ETH Competence Center - ETH AI Center

Hosts Trisovic Jelena

Topics Information, Computing and Communication Sciences , Engineering and Technology

Brain Machine Interface - Visual Neuroprosthetics

Join the Sensors Group at the Institute of Neuroinformatics (INI) to develop next-generation visual neuroprosthetics and advance the future of brain-machine interfaces! Topics Include: - develop neural networks that learn optimal stimulation patterns - utilize recurrent/spiking neural networks for creating stimulation patterns - implementing real-time computation on embedded platforms (FPGA, uC, jetson) - investigating closed-loop control strategies for electrical brain-stimulation Application process: Write us about your interests, include CV and transcript, and we can arrange a meeting. We can supervise students from UZH and ETH. We offer semester projects as well as bachelor's and master's theses projects.

Keywords

brain machine interface, visual neuroprosthetics, bmi, neural networks, Real-time computation, Embedded platforms, FPGA, Closed-loop control, neural recording analysis, Control systems, Deep learning, Verilog, Vivado, hls4ml, Hardware acceleration, Jetson, VR, Android, Unity/Blender

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Semester Project , Master Thesis , ETH Zurich (ETHZ)

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Published since: 2025-06-17 , Earliest start: 2025-02-01 , Latest end: 2026-12-31

Applications limited to ETH Zurich , University of Zurich

Organization ETH Competence Center - ETH AI Center

Hosts Moure Pehuen , Liu Shih-Chii , Hahn Niklas

Topics Information, Computing and Communication Sciences , Engineering and Technology

Learning a Simulation-Trained Safety Critic for Safe Online Learning in Legged Robots

This project focuses on developing a safety critic—a model that predicts the safety of robot states—to enable safe online learning on legged robotic hardware. The safety critic is trained in simulation using labeled data from diverse robot behaviors, identifying states likely to lead to failure (e.g., falls). Once trained, the critic is deployed alongside a learning policy to restrict unsafe exploration, either by filtering dangerous actions or shaping the reward function. The goal is to allow adaptive behavior on real hardware while minimizing physical risk.

Keywords

safety critic, online learning

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Master Thesis

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Published since: 2025-06-16

Organization Robotic Systems Lab

Hosts Li Chenhao , Li Chenhao , Li Chenhao , Li Chenhao

Topics Engineering and Technology

Active System Identification for Efficient Online Adaptation

This project proposes a novel single-stage training framework for system identification in legged locomotion, addressing limitations in the conventional two-stage teacher-student paradigm. Traditionally, a privileged teacher policy is first trained with full information, followed by a student policy that learns to mimic the teacher using only state-action histories—resulting in suboptimal exploration and limited adaptability. In contrast, our method directly trains a policy to regress privileged information embeddings from its history while simultaneously optimizing for an active exploration objective. This objective is based on maximizing mutual information between the policy’s state-action trajectories and the privileged latent variables, encouraging exploration of diverse dynamics and enhancing online adaptability. The approach is expected to improve sample efficiency and robustness in deployment environments with variable dynamics.

Keywords

Active Exploration, System Identification, Online Adaptation

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Master Thesis

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Published since: 2025-06-06

Organization Robotic Systems Lab

Hosts Li Chenhao , Li Chenhao , Li Chenhao , Li Chenhao

Topics Engineering and Technology

Learning from Online Demonstrations via Video Diffusion for Local Navigation

This project introduces a framework for local navigation skill acquisition through online learning from demonstrations, bypassing the need for offline expert trajectories. Instead of relying on pre-collected data, we use video diffusion models conditioned on semantic text prompts to generate synthetic demonstration videos in real time. These generated sequences serve as reference behaviors, and the agent learns to imitate them via an image-space reward function. The navigation policy is built atop a low-level locomotion controller and targets deployment on legged platforms such as humanoids and quadrupeds. This approach enables semantically guided, vision-based navigation learning with minimal human supervision and strong generalization to diverse environments.

Keywords

Learning from Demonstrations, Video Diffusion, Semantic Conditioning

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Master Thesis

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**Work packages** Use video diffusion models to generate reference trajectories with semantic conditions. Train a local navigation policy by learning from demonstrations using the generated trajectories. Deployment on hardware. **Requirements** Strong programming skills in Python Experience in reinforcement learning and imitation learning frameworks **Publication** This project will mostly focus on algorithm design and system integration. Promising results will be submitted to robotics or machine learning conferences where outstanding robotic performances are highlighted. **Related literature** Peng, Xue Bin, et al. "Deepmimic: Example-guided deep reinforcement learning of physics-based character skills." ACM Transactions On Graphics (TOG) 37.4 (2018): 1-14. Li, C., Vlastelica, M., Blaes, S., Frey, J., Grimminger, F. and Martius, G., 2023, March. Learning agile skills via adversarial imitation of rough partial demonstrations. In Conference on Robot Learning (pp. 342-352). PMLR. Li, Chenhao, et al. "FLD: Fourier latent dynamics for Structured Motion Representation and Learning." Serifi, A., Grandia, R., Knoop, E., Gross, M. and Bächer, M., 2024, December. Vmp: Versatile motion priors for robustly tracking motion on physical characters. In Computer Graphics Forum (Vol. 43, No. 8, p. e15175). Fu, Z., Zhao, Q., Wu, Q., Wetzstein, G. and Finn, C., 2024. Humanplus: Humanoid shadowing and imitation from humans. arXiv preprint arXiv:2406.10454. He, T., Luo, Z., Xiao, W., Zhang, C., Kitani, K., Liu, C. and Shi, G., 2024, October. Learning human-to-humanoid real-time whole-body teleoperation. In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 8944-8951). IEEE. He, T., Luo, Z., He, X., Xiao, W., Zhang, C., Zhang, W., Kitani, K., Liu, C. and Shi, G., 2024. Omnih2o: Universal and dexterous human-to-humanoid whole-body teleoperation and learning. arXiv preprint arXiv:2406.08858. Albaba, M., Li, C., Diomataris, M., Taheri, O., Krause, A. and Black, M., 2025. Nil: No-data imitation learning by leveraging pre-trained video diffusion models. arXiv preprint arXiv:2503.10626.

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Published since: 2025-06-06

Organization ETH Competence Center - ETH AI Center

Hosts Li Chenhao , Li Chenhao , Li Chenhao , Li Chenhao

Topics Information, Computing and Communication Sciences

Learning World Models for Legged Locomotion (Structured legged world model)

Model-based reinforcement learning learns a world model from which an optimal control policy can be extracted. Understanding and predicting the forward dynamics of legged systems is crucial for effective control and planning. Forward dynamics involve predicting the next state of the robot given its current state and the applied actions. While traditional physics-based models can provide a baseline understanding, they often struggle with the complexities and non-linearities inherent in real-world scenarios, particularly due to the varying contact patterns of the robot's feet with the ground. The project aims to develop and evaluate neural network-based models for predicting the dynamics of legged environments, focusing on accounting for varying contact patterns and non-linearities. This involves collecting and preprocessing data from various simulation environment experiments, designing neural network architectures that incorporate necessary structures, and exploring hybrid models that combine physics-based predictions with neural network corrections. The models will be trained and evaluated on prediction autoregressive accuracy, with an emphasis on robustness and generalization capabilities across different noise perturbations. By the end of the project, the goal is to achieve an accurate, robust, and generalizable predictive model for the forward dynamics of legged systems.

Keywords

forward dynamics, non-smooth dynamics, neural networks, model-based reinforcement learning

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Master Thesis

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Published since: 2025-06-06

Organization Robotic Systems Lab

Hosts Li Chenhao , Li Chenhao , Li Chenhao , Li Chenhao

Topics Engineering and Technology

Bridging the Gap: Enabling Soft Actor-Critic for High-Performance Legged Locomotion

Proximal Policy Optimization (PPO) has become the de facto standard for training legged robots, thanks to its robustness and scalability in massively parallel simulation environments like IsaacLab. However, alternative algorithms such as Soft Actor-Critic (SAC), while sample-efficient and theoretically appealing due to entropy maximization, have not matched PPO’s empirical success in this domain. This project aims to close that performance gap by developing and evaluating modifications to SAC that improve its stability, scalability, and sim-to-real transferability on legged locomotion tasks. We benchmark SAC against PPO using standardized pipelines and deploy learned policies on real-world quadruped hardware, pushing toward more flexible and efficient reinforcement learning solutions for legged robotics.

Keywords

Legged locomotion, Soft Actor-Critic, Reinforcement learning, Sim-to-real transfer

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Master Thesis

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Published since: 2025-05-30

Organization ETH Competence Center - ETH AI Center

Hosts Li Chenhao , Li Chenhao , Li Chenhao , Li Chenhao

Topics Information, Computing and Communication Sciences

Learning Terrain Traversal from Human Strategies for Agile Robotics

Teaching robots to walk on complex and challenging terrains, such as rocky paths, uneven ground, or cluttered environments, remains a fundamental challenge in robotics and autonomous navigation. Traditional approaches rely on handcrafted rules, terrain classification, or reinforcement learning, but they often struggle with generalization to real-world, unstructured environments.

Keywords

3D reconstruction, egocentric video, SMPL representation

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Semester Project , Master Thesis

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Published since: 2025-05-21 , Earliest start: 2025-05-26

Organization Computer Vision and Geometry Group

Hosts Wang Xi , Frey Jonas , Patel Manthan , Kaufmann Manuel , Li Chenhao

Topics Information, Computing and Communication Sciences