Student Projects

Currently, the following student projects are available. Please contact the responsible supervisor and apply with your CV and transcripts.

In case you have project ideas related to any of these projects, take the opportunity and propose your own project!

We also offer Master Thesis and or exchange semesters at reknown universities around the world. Please contact us in case of interest!

Studies on Mechatronics

We offer students also to conduct their Studies on Mechatronics at our lab. In general, we recommend to do the Studies on Mechatronics in combination with the Bachelor Thesis, either as prepartory work the semester beofre or as extended study in parallel. If you want to do it independently, yiou can find prroposed projets also in the list below. Please directly apply with corresponsponding supervisor.

Humanoid Forceful Interaction

Developing a robust RL method for whole-body behaviors in forceful Human-Object Interaction

Keywords

Human-Object Interaction (HOI), Forceful Manipulation, Whole-Body Control (WBC), Reinforcement Learning

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Semester Project , Master Thesis

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Published since: 2026-03-20 , Earliest start: 2026-04-13 , Latest end: 2026-10-31

Organization Robotic Systems Lab

Hosts Fuchioka Yuni

Topics Information, Computing and Communication Sciences

Building Authentic Articulated Assets from Real-world Footage

Zero-shot sim assets creation with fine-grained articulation

Keywords

Simulation, 3D reconstruction

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Semester Project

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Published since: 2026-03-19 , Earliest start: 2026-03-14

Applications limited to ETH Zurich , University of Zurich

Organization Robotic Systems Lab

Hosts Chen Changan , Sun Boyang

Topics Information, Computing and Communication Sciences

Scene graphs meet implicit representations

Explore novel, hierarchical spatial memory representations for flexible robot autonomy

Keywords

Hierarchical scene graphs, implicit representations, RL

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Master Thesis

Description

When it comes to the input of low-level locomotion and navigation policies, learnings from the bitter lesson push researchers to slowly move away from explicit, world-centric representations towards implicit, egocentric representations. The belief is that this imposes the least bias on the representation, and thus leads to best performance. In order to make robots more capable, we then give them a memory or history of such embeddings as an augmented input. However, in many cases these embeddings are not spatially or temporally indexed, they are just a sequence of recent egocentric observations that turn out to be very local by design and express context only at a single level of abstraction. The goal of the present project would be to explore: • Spatial indexing of embedded representations in order to improve the ability to localize embeddings in space • Spatial pooling / compression of embeddings to generate novel implicit representations expressing contexts at higher levels of abstraction. The proposed idea is aiming for a marriage between scene graphs and implicit representations. The idea is to index/retrieve embeddings not just as a local history, but in space by projecting and averaging implicit representations in 3D. The hierarchy aspect would be an extension of this idea, and acknowledge the fact that different tasks benefit from different granularities of context representation. The challenge would be to find a meaningful pooling/averaging strategy that produces embeddings at a higher level of abstraction. In general, this would enable 1) more powerful memories that enable indexing in space (and possibly also time), and 2) more powerful policies that are informed about relevant context at multiple, different levels of abstraction. The thesis would be conducted at the RAI Institute, a relatively young, top-notch robotics research institute with representation in Zurich. It would be conducted under a collaboration framework between RAI and ETH Zurich. Strong engineering support and a best-in-class robotics research laboratory make this opportunity second to none. For an example of the recent achievements of the institute, please consider our online video channel. Selection is highly competitive. Potential candidates are invited to submit their CV and grade sheet, after which students will be invited to an on-site interview.

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Published since: 2026-03-19 , Earliest start: 2026-03-23

Organization Robotic Systems Lab

Hosts Kneip Laurent

Topics Information, Computing and Communication Sciences , Engineering and Technology

LLM-based scene graph construction and navigation

Use LLM priors for efficient scene graph construction and navigation in urban environments

Keywords

Open vocabulary scene graphs, urban navigation, planning

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Master Thesis

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Published since: 2026-03-19 , Earliest start: 2026-03-23

Organization Robotic Systems Lab

Hosts Kneip Laurent

Topics Information, Computing and Communication Sciences , Engineering and Technology

Learning-based robot state estimation

Achieve high-quality state estimation on any platform via conditioned neural representations

Keywords

Learning-based state estimation

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Master Thesis

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Published since: 2026-03-19 , Earliest start: 2026-03-23

Organization Robotic Systems Lab

Hosts Kneip Laurent

Topics Information, Computing and Communication Sciences , Engineering and Technology

RAI Project: Ultra Mobile Parkour

This project pushes the limits of robotic agility through Ultra-Mobile Parkour. Moving beyond basic walking, we are developing agents that don’t just "navigate" obstacles — they conquer them. By leveraging high-fidelity simulations and Deep Reinforcement Learning (DRL), we aim to bridge the gap between static movement and high-velocity "flow."

Keywords

UMV, RL, parkour, reinforcement learning, ultra mobile, agile, research, robotics, wheeled, dynamic

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Master Thesis

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Published since: 2026-03-12

Applications limited to EPFL - Ecole Polytechnique Fédérale de Lausanne , ETH Zurich

Organization Robotic Systems Lab

Hosts Jenelten Fabian, Dr.

Topics Information, Computing and Communication Sciences

Enhancing Long-Range Navigation with VLMs

This project aims to enhance the WildOS [1] navigation framework by integrating a Vision-Language Model (VLM) [2,3] to perform semantic reasoning over a sparse topological graph. While standard WildOS excels at geometric navigation and open-vocabulary object search, it often lacks the high-level context needed to navigate "semantic traps" like dead-ends or complex roadblocks. By storing VLM-generated node summaries and image captions within the graph, the robot can transition from simple pattern matching to logical spatial querying. This allows the agent to interpret the environment's affordances—such as identifying if a path is "blocked by a gate" versus "temporarily obstructed by a person"—to make more informed recovery decisions.

Keywords

Foundation Models, Computer Vision, Navigation, Perception, Vision-Language Models, VLMs, Robotics, ROS, Field Robotics, Machine Learning,

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Master Thesis

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Published since: 2026-03-09 , Earliest start: 2026-03-09 , Latest end: 2026-10-31

Applications limited to ETH Zurich

Organization Robotic Systems Lab

Hosts Patil Vaishakh , Patel Manthan

Topics Information, Computing and Communication Sciences , Engineering and Technology

3D reconstruction with open-vocabulary reconstruction priors

Push the limits of arbitrary online video reconstruction by combining the most recent, prior-supported real-time Simultaneous Localization And Mapping (SLAM) methods with automatic backend regularization techniques.

Keywords

Structure from Motion Visual SLAM

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Master Thesis , ETH Zurich (ETHZ)

Description

In recent years, the advent of learning-based methods has led to interesting novel structure from motion modules that provide end-to-end 3D structure, camera pose, and camera intrinsics estimation from arbitrary image sets [1,2]. While it is reasonable to assume that these representation implicitly impose geometric priors onto regularly encountered scene elements (e.g. chairs, tables, etc.), the imposition of these priors gets lost during the typical global back-end optimization step [3,4]. The imposition of geometric priors in back-end optimization is a topic with long-standing history, and nowadays encompasses both classical techniques such as piece-wise planarity or Manhattan world assumptions, as well as modern learning-based representations such as neural shape priors [5]. While certainly interesting, these approaches suffer from two important limitations: • Classical approaches are limited to a small set of pre-defined regularities that may be encountered in a certain class of environments (typically man-made). It is furthermore difficult to decide without further cues which parts of an environment should be regularized. • More modern approaches such as deep shape priors are typically trained on specific classes of objects, only, and are thus limited to certain object-level application. The goal of the present thesis is to investigate the following topics: • Flexible discovery of regularities (surface regularities, repetitive scene elements) in large scale outdoor reconstruction scenarios using LLM/VLMs • Extraction of geometric surface properties from grounded open-set features • Automatic formulation of regularization priors using LLMs • Inclusion into back-end reconstruction for improved global reconstruction results The proposed thesis will be conducted at the Robotics and AI (RAI) Institute, a new top-notch partner institute of Boston Dynamics pushing the boundaries of control and perception in robotics. For an example of the recent achievements of the institute, please consider our online video channel: Stunting with Reinforcement Learning The down-stream task of the present project will be the creation of geometric models with high fidelity surfaces for inclusion into simulation environments. Selection will be highly selective. Potential candidates are invited to submit their CV and grade sheet, after which students will be invited to an on-site interview. References [1] Grounding image matching in 3D with Mast3r, Vincent Leroy, Yohann Cabon, and Jerome Revaud [2] VGGT: Visual Geoemtry Grounded Transformer, Wang, Chen, Karaev, Vedaldi, Rupprecht, Novotny [3] MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors, Murai, Dexheimer, Davison [4] VGGT-SLAM: Dense RGB SLAM Optimized on the SL(4) Manifold, Dominic Maggio, Hyungtae Lim, Luca Carlone [5] DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation, Park, Florence, Straub, Newcombe, Lovegrove

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Published since: 2026-03-01 , Earliest start: 2025-07-01

Organization Robotic Systems Lab

Hosts Kneip Laurent

Topics Engineering and Technology

Event-based feature detection for highly dynamic tracking

Event cameras are an exciting new technology enabling sensing of highly dynamic content over a broad range of illumination conditions. The present thesis explores novel, sparse, event-driven paradigms for detecting structure and motion patterns in raw event streams.

Keywords

Event camera, neuromorphic sensing, feature detection, computer vision

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Master Thesis

Description

Event cameras are a relatively new, vision-based exteroceptive sensor relying on standard CMOS technology. Unlike normal cameras, event cameras do not measure absolute brightness in a frame-by-frame manner, but relative changes of the pixel-level brightness. Essentially, every pixel of an event camera independently observes the local brightness pattern, and when the latter experiences a relative change of minimum amount with respect to a previous value, a measurement is triggered in the form of a time-stamped event indicating the image location as well as the polarity of the change (brighter or darker) [2]. The pixels act asynchronously and can potentially fire events at a very high rate. Owing to their design, event cameras do not suffer from the same artifacts as regular cameras, but continue to perform well under high dynamics or challenging illumination conditions. Event cameras currently enjoy growing popularity and they represent a new, interesting alternative for exteroceptive sensing in robotics when facing scenarios with high dynamics and/or challenging conditions. The focus of the present thesis lies on 3D motion estimation with event cameras, and in particular aims at event-driven, computationally efficient methods that can trigger motion hypotheses from sparse raw events. Initial theoretical advances in this direction have been presented in recent literature [3,4,5], though these methods are still limited in terms of the assumptions that they make. The present thesis will push the boundaries by proposing novel both geometry and learning-based representations. The proposed thesis will be conducted at the Robotics and AI Institute, a new top-notch partner institute of Boston Dynamics pushing the boundaries of control and perception in robotics. Selection is highly competitive. Potential candidates are invited to submit their CV and grade sheet, after which students will be invited to an on-site interview. [1] Event-Based, 6-DOF Camera Tracking from Photometric Depth Maps, TPAMI 40(10):2402-2412, 2017 [2] The Silicon Retina, 264(5): 76-83, 1991 [3] A 5-Point Minimal Solver for Event Camera Relative Motion Estimation. In Proceedings of the International Conference on Computer Vision (ICCV), 2023 [4] An n-point linear solver for line and motion estimation with event cameras. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024. [5] Full-DoF Egomotion Estimation for Event Cameras Using Geometric Solvers, Arxiv: https://arxiv.org/html/2503.03307v1

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Published since: 2026-03-01 , Earliest start: 2025-03-17

Organization Robotic Systems Lab

Hosts Kneip Laurent

Topics Engineering and Technology

Soft object reconstruction

This project consists of reconstructing soft object along with their appearance, geometry, and physical properties from image data for inclusion in reinforcement learning frameworks for manipulation tasks.

Keywords

Computer Vision, Structure from Motion, Image-based Reconstruction, Physics-based Reconstruction

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Master Thesis

Description

As 3D reconstruction [2,3], real-time data-driven rendering [4,5], and learning-based control technologies [6,7] are becoming more mature, recent efforts in reinforcement learning are moving towards end-to-end policies that directly consume images in order to generate control commands [8]. However, many of the simulated environments are limited to a composition of rigid objects. In recent years, the inclusion of differentiable particle-based simulation borrowed from computer graphics has enabled the inclusion of non-rigid or even fluid elements. Ideally, we can generate such representations from real world data in order to extend data-driven world simulators to arbitrary new objects with complex physical behavior. The present thesis focuses on this problem and aims at reconstructing soft objects in terms of their geometry, appearance, and physical behavior. The goal is to make use of the Material Point Method (MPM) in combination with vision-based cues and physical priors in order to reconstruct accurate 3D models of soft objects. The developed models will finally be included into an RL learning environment such as Isaac-Gym in order to train novel manipulation policies for soft objects. The proposed thesis will be conducted at the Robotics and AI Institute, a new top-notch partner institute of Boston Dynamics pushing the boundaries of control and perception in robotics. Selection is highly competitive. Potential candidates are invited to submit their CV and grade sheet, after which students will be invited to an on-site interview. [1] Modeling of Deformable Objects for Robotic Manipulation: A Tutorial and Review, Front. Robot. AI, 7, 2020 [2] Global Structure-from-Motion Revisited, ECCV 2024 [3] MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors, CVPR 2025 [4] NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis, CVPR 2020 [5] 3D Gaussian Splatting for Real-Time Radiance Field Rendering, SIGGRAPH 2023 [6] Learning to walk in minutes using massively parallel deep reinforcement learning, CoRL 2022 [7] Champion-Level Drone Racing Using Deep Reinforcement Learning, Nature, 2023 [8] π0: A Vision-Language-Action Flow Model for General Robot Control, Arxiv: https://arxiv.org/abs/2410.24164

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Published since: 2026-03-01 , Earliest start: 2025-03-17

Organization Robotic Systems Lab

Hosts Kneip Laurent

Topics Engineering and Technology

Generalist policies for robotic bike racing

This project addresses the problem of overfitting in autonomous robotic racing by developing generalist reinforcement learning (RL) policies. Conducted in collaboration between the RAI Institute and ETH Zurich, the research focuses on creating robust track embeddings for complex terrains. These embeddings will condition policies for a bike-like ultra-mobile vehicle (UMV), enabling it to autonomously race on previously unknown mountain-bike trails. The ultimate goal is to deploy these policies on physical hardware to achieve competitive, real-world performance via zero-shot transfer or minimal simulation refinement.

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Master Thesis

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Published since: 2026-03-01

Organization Robotic Systems Lab

Hosts Jenelten Fabian, Dr.

Topics Engineering and Technology

Three Degrees of Freedom, Parallel Mechanism-Based Leg Mechanisms and Fabrications

Design and fabricate a novel three-degree-of-freedom parallel-kinematics robotic leg that enables highly dynamic behaviors. The project focuses on manufacturable mechanism design, integrated actuators, and linear-drive architectures, resulting in a hardware platform for future legged robotics research and control studies.

Keywords

robotics, mechanical engineering, hardware, reinforcement learning, learning from demonstration, Parallel Mechanisms, Actuator Design, Legged Robotics, Intelligent Robotics

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Semester Project , Bachelor Thesis , Master Thesis

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Published since: 2026-02-23 , Earliest start: 2026-03-01 , Latest end: 2026-12-31

Organization Robotic Systems Lab

Hosts Tanaka Yusuke , Baines Robert

Topics Information, Computing and Communication Sciences , Engineering and Technology

Exploring Exotic Wheels For Wheeled-Legged Robots

Wheeled-legged robots combine the advantages of wheels, i.e., efficient high-speed locomotion, with obstacle negotiation and rough terrain capabilities of legs. In this study, different exotic wheel concepts should be researched, evaluated, and possibly implemented on a real robot to potentially boost efficiency, manipulation, and functionality.

Keywords

Wheeled-Legged Robots, Wheels, Robotics, Mechanical and Hardware Design, Manipulation, Tires

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Published since: 2026-02-18

Organization Robotic Systems Lab

Hosts Klemm Victor

Topics Information, Computing and Communication Sciences , Engineering and Technology

Hierarchical Reasoning Models (HRM) for Robotics

How to properly deal with long-horizon tasks is still an unsolved problem in robot learning. Recent advances in reasoning models present Hierarchical Reasoning Models (HRM), inspired by the hierarchical and multi-timescale processing in the human brain. This fairly open project should implement and test state of the art solutions for long horizon planning that require reasoning in the context of robotics, especially robotics motion control.

Keywords

Robot learning, hierarchical reasoning, machine intelligence, long-horizon planning

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Semester Project , Bachelor Thesis , Master Thesis

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Published since: 2026-02-18

Organization Robotic Systems Lab

Hosts Klemm Victor

Topics Information, Computing and Communication Sciences , Engineering and Technology

Large Scale Triangle Splatting

Imagine capturing an entire city from a moving car and turning it into a high-fidelity, interactive 3D world, one that doesn't just look real, but can be dropped directly into a game engine or a physics simulator. This project bridges the gap between cutting-edge AI reconstruction and the traditional graphics pipeline. You will build DiskChunTS, a system designed to reconstruct city-scale environments using Triangle Splatting. By adapting chunk-based memory management, we can stream massive datasets directly from disk, bypassing hardware limits.

Keywords

Gaussian Splatting, Robotics, Reconstruction

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Master Thesis

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Published since: 2026-02-13 , Earliest start: 2026-02-16

Applications limited to ETH Zurich

Organization Robotic Systems Lab

Hosts Speciale Pablo , Wilder-Smith Max , Patil Vaishakh

Topics Information, Computing and Communication Sciences

RL Finetuning for Generalized Locomotion

This project investigates the potential of reinforcement learning (RL) fine-tuning to develop a single, universal locomotion policy for quadruped or humanoid robots. Building on prior work in multi-terrain skill synthesis [1], we will probe the limits of generalization by systematically fine-tuning on an ever-expanding set of diverse environments. This incremental approach will test the hypothesis that a controller can learn to robustly navigate a vast range of terrains. As a potential extension, procedural terrain generation may be used to automatically create novel challenges, pushing the boundaries of policy robustness.

Keywords

Reinforcement Learning, Locomotion, Quadruped, Humanoid

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Master Thesis

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Published since: 2026-02-11 , Earliest start: 2026-02-01

Organization Robotic Systems Lab

Hosts Schwarke Clemens , He Junzhe

Topics Information, Computing and Communication Sciences

Learning 3D-Aware Locomotion with Depth Foundation Model

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Master Thesis

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Published since: 2026-01-27 , Earliest start: 2026-01-27

Organization Robotic Systems Lab

Hosts Zhang Chong , Schwarke Clemens , Patel Manthan

Topics Information, Computing and Communication Sciences

Pull or push? Unifying mobile manipulation skills for a quadrupedal manipulator

This thesis will focus on unifying multiple different mobile manipulation skills into a single controller for mobile manipulation with a quadrupedal manipulator

Keywords

mobile manipulation, robotics, control, reinforcement learning, quadrupedal manipulation

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Master Thesis

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Published since: 2026-01-26 , Earliest start: 2026-02-01

Applications limited to ETH Zurich

Organization Robotic Systems Lab

Hosts Dadiotis Ioannis

Topics Information, Computing and Communication Sciences , Engineering and Technology

Gaussian Splatting Super Resolution

Radiance fields like 3D Gaussian Splatting enable fast, high-fidelity scene reconstruction from real-time data. However, reconstruction quality improves in noticeable stages—e.g., coarse at 500 epochs, detailed at 5000. This project explores whether AI-based upscaling and denoising techniques can be used to predict and accelerate these improvements during training. By anticipating reconstruction quality transitions, we aim to reduce training time while maintaining visual fidelity—advancing the efficiency of radiance field learning.

Keywords

Gaussian Splatting, Computer Vision, Reconstruction

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Master Thesis

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Published since: 2026-01-26 , Earliest start: 2025-11-01 , Latest end: 2026-07-31

Applications limited to ETH Zurich

Organization Robotic Systems Lab

Hosts Patil Vaishakh , Wilder-Smith Max

Topics Information, Computing and Communication Sciences

BEV meets Semantic traversability

Enable Birds-Eye-View perception on autonomous mobile robots for human-like navigation.

Keywords

Semantic Traversability, Birds-Eye-View, Localization, SLAM, Object Detection

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Master Thesis , ETH Zurich (ETHZ)

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Published since: 2026-01-26

Applications limited to ETH Zurich

Organization Robotic Systems Lab

Hosts Gawel Abel

Topics Information, Computing and Communication Sciences , Engineering and Technology

Scene graphs for robot navigation and reasoning

Elevate semantic scene graphs to a new level and perform semantically-guided navigation and interaction with real robots at The AI Institute.

Keywords

Scene graphs, SLAM, Navigation, Spacial Reasoning, 3D reconstruction, Semantics

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Master Thesis , ETH Zurich (ETHZ)

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Published since: 2026-01-26

Applications limited to ETH Zurich

Organization Robotic Systems Lab

Hosts Gawel Abel , Kneip Laurent

Topics Information, Computing and Communication Sciences , Engineering and Technology

Perceptive Arm Motion Planning and Control for Heavy Construction Machine Tasks

In this work we would utilize reinforcement learning, neural network actuator modeling, and perception for the control and arm motion planning of a 40ton excavator with a free-swinging gripper. The project will be in collaboration with Gravis Robotics, ETH spinoff working on the automation of heavy machinery.

Keywords

reinforcement learning, perception, hydraulics, excavator, manipulation, industry

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Semester Project , Collaboration , Master Thesis , ETH Zurich (ETHZ)

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This project aims to develop an advanced autonomous solution for material handling tasks performed by large-scale construction machinery, exemplified by the 75-ton Liebherr LH80 excavator. A primary application focus is the complex operation of loading and unloading ships, which demands highly precise movements to avoid collisions with surrounding obstacles. The inherent challenges include managing the complex dynamics of hydraulic actuators and the free-swinging end-effector joint, tasks currently achievable only by highly skilled human operators. Building upon previous research [1], this work will establish a comprehensive pipeline for 3D control and collision-free motion planning of the excavator's arm and cabin. Due to the difficulties in accurately modeling hydraulic systems, reinforcement learning (RL) algorithms are employed for joint control. To minimize unwanted swinging of the end-effector during operations, both high-level and low-level control strategies are learned within a single RL policy. A key focus of this project is the integration of perceptive information into the RL framework. This involves incorporating 3D sensing data from LiDAR scans and semantic segmentation from camera images. This rich sensory input is crucial for detecting and classifying the material being handled and ensuring safe operation in dynamic environments with moving objects. The ultimate goal is to replace existing sampling-based motion planners with a unified RL agent. This agent will be capable of perceptive planning and control, adeptly managing kinodynamic constraints and navigating arbitrary-shaped obstacles in real-time to achieve efficient and feasible motion plans. The project will be developed in collaboration with Gravis Robotics, an ETH spinoff from the Robotic Systems Lab (RSL) working on the automation of heavy machinery (https://gravisrobotics.com/). [1] Spinelli, F.A., Egli, P., Nubert, J., Nan, F., Bleumer, T., Goegler, P., Brockes, S., Hofmann, F. and Hutter, M., 2024. Reinforcement learning control for autonomous hydraulic material handling machines with underactuated tools.

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Published since: 2026-01-18 , Earliest start: 2025-07-07

Organization Robotic Systems Lab

Hosts Egli Pascal Arturo , Terenzi Lorenzo , Spinelli Filippo , Spinelli Filippo

Topics Information, Computing and Communication Sciences , Engineering and Technology

Urban Robot Navigation with Scene Graphs and Foundation Models

Solve key challenges of urban scene representations and enable robots to navigate to goals with language instructions using advanced perception capabilities of foundation models

Keywords

Navigation, Scene Representation, Vision Language Models, Robot Learning

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Master Thesis

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Over the past decades, robots have been able to navigate indoor and outdoor reliably with detailed geometric maps [1]. However, humans don’t need to recall fine-grained geometric details to achieve versatile navigation behaviors. Recent neural research discovered that humans rely on some kind of cognitive maps to navigate themselves around [2]. Can we bridge the gap of human and robots’ navigation capabilities by building such kinds of cognitive representations? Scene graphs, as a form of compact and expressive scene representation, have been shown useful for semantic indoor navigation in household scenarios in recent years [3,4]. However, previous scene graph construction methods focused heavily on the object- or room-level semantics, making it difficult to scale to outdoor urban environments like train stations, shopping malls, or commercial districts. Recent advances in Vision Language Models makes multimodal perception much more capable than before [5]. How to utilize these advances in foundation models and apply them to urban navigation is still an open question. In this project, we aim to use the frontier perception capabilities of VLMs to construct a compact representation of an urban environment, localize the robot in it, and enable the robot to navigate in the environment with this prior. In this project, you will get hands-on experience of solving a real-world robotic problem with both values in research and practicality. Selection will be highly selective. Potential candidates are invited to submit their CV and grade sheet, after which students will be invited to an on-site interview. References - [1] Improving Grid-based SLAM with Rao-Blackwellized Particle Filters by Adaptive Proposals and Selective Resampling, ICRA 2005 - [2] The cognitive map in humans: Spatial navigation and beyond, Nature neuroscience 2017 - [3] ConceptGraphs: Open-Vocabulary 3D Scene Graphs for Perception and Planning, ICRA 2024 - [4] Hierarchical Open-Vocabulary 3D Scene Graphs for Language-Grounded Robot Navigation, RSS 2024 - [5] Qwen3-VL Technical Report, 2025

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Published since: 2026-01-06 , Earliest start: 2026-01-01 , Latest end: 2026-08-31

Applications limited to ETH Zurich

Organization Robotic Systems Lab

Hosts Huang Chenguang , Kneip Laurent

Topics Information, Computing and Communication Sciences

Multistable robotic tensegrities for dynamic shape-shifting

This thesis will combine robotic hardware prototyping with rigorous first principles-based modeling. The goal: tap into tensegrities’ nonlinear properties, such as multistability and instability, to realize robotic modules (bodies, arms, legs, etc.) that form the basis of dynamic shape-shifting systems.

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Master Thesis

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Published since: 2025-12-04 , Earliest start: 2026-01-01 , Latest end: 2026-08-01

Organization Robotic Systems Lab

Hosts Baines Robert

Topics Information, Computing and Communication Sciences , Engineering and Technology

Reinforcement Learning for Excavation Planning In Terra

We aim to develop a reinforcement learning-based global excavation planner that can plan for the long term and execute a wide range of excavation geometries. The system will be deployed on our legged excavator.

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Keywords: Reinforcement learning, task planning

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Semester Project , Master Thesis

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Published since: 2025-12-01 , Earliest start: 2026-02-02 , Latest end: 2026-09-30

Organization Robotic Systems Lab

Hosts Terenzi Lorenzo

Topics Information, Computing and Communication Sciences

Perceptive Generalist Excavator Transformer

We want to develop a generalist digging agent that is able to do multiple tasks, such as digging and moving loose soil, and/or control multiple excavators. We plan to use a decoder only GPT model, trained on offline data and potentially online RL as posttraining, to accomplish these tasks.

Keywords

Offline reinforcement learning, transformers, autonomous excavation

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Published since: 2025-12-01 , Earliest start: 2026-02-02 , Latest end: 2026-10-31

Organization Robotic Systems Lab

Hosts Werner Lennart , Terenzi Lorenzo , Nan Fang

Topics Information, Computing and Communication Sciences

Reinforcement Learning for Particle-Based Excavation in Isaaclab / Newton

We want to train RL agents on our new particle simulator, accelerated on the GPU via warp in Isaaclab and Newton.

Keywords

particle simulation, omniverse, warp, reinforcement learning

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Semester Project , Master Thesis

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Published since: 2025-12-01 , Earliest start: 2026-02-01 , Latest end: 2026-07-31

Organization Robotic Systems Lab

Hosts Terenzi Lorenzo

Topics Information, Computing and Communication Sciences

Self-Supervised LiDAR–RGB Encoders for Autonomous Earthworks

This project adapts recent self-supervised 3D and 2D–3D representation learning methods (Sonata/Concerto-style encoders) to build a robust LiDAR–RGB backbone for earthmoving robots. The goal is a reusable encoder that powers perception for excavation, loading, traversability, and safety on real construction sites, and runs in real time on our robots.

Keywords

LiDAR–RGB fusion, Self-supervised learning, 2D–3D representations, Earthmoving robotics, Perception

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Semester Project , Master Thesis

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Published since: 2025-12-01 , Earliest start: 2026-02-02 , Latest end: 2026-08-31

Organization Robotic Systems Lab

Hosts Terenzi Lorenzo

Topics Engineering and Technology

Student Theses at RAI

The Robotic Systems Lab offers a number of student projects at RAI or in collaboration with RAI. Please check the individual projects and directly apply to the supervisors.

Note on plagiarism

We would like to suggest every student, irrespective of the type of project (Bachelor, Semester, Master, ...), to make himself/herself familiar with ETH rules regarding plagiarism