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.
Informed Exploration in Reinforcement Learning via Sampling-Based Planning
Robust locomotion across diverse terrains remains one of the most challenging and exciting problems in legged robotics. While deep reinforcement learning (RL) has enabled impressive quadrupedal behaviors, generalization to unseen or complex terrains often suffers from low sample efficiency. To address this, researchers typically rely on labor-intensive curriculum design, where training terrains are manually structured to progress from easy to difficult, while trying to avoid catastrophic forgetting. These staged curricula allow the RL agent to master simple environments before tackling more complex ones. However, even with such manually designed curricula, RL training can take several days. This is partly because the agent does not retain knowledge about where it has succeeded or failed, nor does it use this experience to inform future exploration. This project aims to develop an automated approach to guide the RL exploration by leveraging sampling-based planning techniques. We formulate the training process as a graph expansion problem, wherein the terrain space is incrementally explored to maximize gains in locomotion robustness and performance. Can the Voronoi bias inherent in sampling-based planning steer the agent toward more effective and sample-efficient training trajectories? If successful, this approach could enable the use of real-world terrain scans for locomotion training, reducing dependence on expert-designed environments and substantially accelerating the training process. The project brings together concepts from sampling-based planning, adaptive curricula, and locomotion learning.
Keywords
Curriculum Learning, Quadruped Locomotion, Sampling-Based Planning, Reinforcement Learning
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Semester Project , Master Thesis
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Published since: 2025-12-10
Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne , University of Zurich
Organization Robotic Systems Lab
Hosts Schwarke Clemens , Klemm Victor , Qu Kaixian
Topics Information, Computing and Communication Sciences
Sign Language Interpreter
Sign language interpretation ensures communication remains inclusive, yet it is currently a task restricted to human experts. While modern humanoid hardware finally has the finger and arm dexterity to perform manipulation tasks, research exploring its applications in sign language communication is limited. We propose developing a control algorithm capable of listening to spoken language and translating it into sign language on a physical robot to act as an interpreter. In this project, you will focus on dynamic motion generation using reinforcement learning. This includes a variety of challenges from fields, requiring arm motion reconstruction from video, retargeting these poses to the robot’s morphology, training a robot to recreate these kinematic states, and then developing a method to receive text input to smoothly access this built motion vocabulary. You will then design a pipeline that can receive audio input from an interlocutor and feed these to the policy in real-time to effectively interpret the speech into sign language. You will be tackling challenges such as ensuring that words are being clearly formed by the robot and enabling smooth transitions between words.
Keywords
Humanoid robotics, learning from demonstration, reinforcement learning
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Semester Project , Master Thesis
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Published since: 2025-12-10
Organization Robotic Systems Lab
Hosts Wang Shengzhi , Mittal Mayank , Heyrman Matthias
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: 2025-12-09 , 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
Walking Through Fog with Radar Splatting and RL
Help push the boundaries of robotic perception in foggy and visually degraded environments. This project focuses on enhancing the navigation capabilities of the quadruped robot Anymal using radar to perceive through fog. You'll simulate challenging conditions in NVIDIA IsaacLab and IsaacSim, generate clear 3D maps using 3D Gaussian Splatting, and train reinforcement learning-based navigation policies for real-world deployment. Combining advanced sensor fusion with cutting-edge RL, this project offers a hands-on opportunity to explore next-gen robotic vision and autonomy.
Keywords
Gaussian Splatting, Robotics, RL,
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Master Thesis
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Published since: 2025-12-09 , Earliest start: 2025-11-01 , Latest end: 2026-07-31
Applications limited to ETH Zurich
Organization Robotic Systems Lab
Hosts Wilder-Smith Max , Patil Vaishakh , Roth Pascal
Topics Information, Computing and Communication Sciences
Propose Your Own Robotics Project
This project invites you to step into the role of an innovator, encouraging you to identify challenges you are passionate about within the field of robotics. Rather than working on predefined problems, you will have the freedom to propose your own project ideas, address real-world issues, or explore cutting-edge topics. This project allows you to define your own research journey.
Keywords
Robotics, Research
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Semester Project , Bachelor Thesis , Master Thesis
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Published since: 2025-12-09 , Earliest start: 2025-01-27
Organization Robotic Systems Lab
Hosts Schwarke Clemens , Bjelonic Filip , Klemm Victor
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
Generative Environment Design: Using LLMs to bridge Sim-to-Real Gap for Locomotion
This project proposes an LLM-driven framework to automate the design of complex simulation environments for robust legged locomotion. By translating natural language descriptions directly into low-level physics parameters, we replace tedious manual tuning with semantic reasoning. The study focuses on training adaptive policies for quadrupedal or wheeled-legged robots, aiming to achieve robust performance across diverse, language-generated terrains.
Keywords
Large Language Models, Locomotion, Sim-to-Real, Domain Randomization, Physics Simulation, Reinforcement Learning.
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Semester Project , Bachelor Thesis , Master Thesis
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Published since: 2025-12-03
Organization Robotic Systems Lab
Hosts Palma Emilio , Roth Pascal
Topics Mathematical Sciences , Information, Computing and Communication Sciences , Engineering and Technology , Physics
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.
Keywords
Keywords: Reinforcement learning, task planning
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Semester Project , Master Thesis
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Published since: 2025-12-01 , Earliest start: 2026-02-01 , Latest end: 2026-09-30
Organization Robotic Systems Lab
Hosts Terenzi Lorenzo
Topics Information, Computing and Communication Sciences
Foundation models for generalizable construction machine automation
Autonomous operation of heavy construction machinery promises major gains in safety, efficiency, and scalability for the multi-trillion-dollar construction industry. Excavators present a unique challenge, combining the navigation demands of autonomous driving with the precision of robotic manipulation in unstructured, dynamic environments. This project explores Vision-Language-Action (VLA) and diffusion-based policy learning to develop generalizable excavation policies. Leveraging large-scale demonstration data naturally collected during human operation, we aim to train multimodal control models capable of adapting across sites, lighting conditions, and machine types. The approach will be evaluated through simulation benchmarks and, if possible, real-world trials, advancing the path toward scalable, generalist autonomy for robotic construction.
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Semester Project , Master Thesis
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Published since: 2025-12-01 , Earliest start: 2025-08-31 , Latest end: 2026-03-31
Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne
Organization Robotic Systems Lab
Hosts Nan Fang , Zhang Weixuan
Topics Engineering and Technology
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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Semester Project , Master Thesis
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Published since: 2025-12-01 , Earliest start: 2026-02-01 , 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: 2025-06-01 , Latest end: 2025-12-31
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: 2026-02-01 , Latest end: 2026-08-31
Organization Robotic Systems Lab
Hosts Terenzi Lorenzo
Topics Engineering and Technology
RL Finetuning for Generalized Quadruped Locomotion
This project investigates the potential of reinforcement learning (RL) fine-tuning to develop a single, universal locomotion policy for quadruped 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, Quadruped Locomotion
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Master Thesis
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Published since: 2025-11-28 , Earliest start: 2025-06-15
Organization Robotic Systems Lab
Hosts Schwarke Clemens , He Junzhe
Topics Information, Computing and Communication Sciences
Hardware Support HiWi
Mechanical Design, Integration, Robot repairs
Keywords
Hardware, Design, Mechanics, CAD
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Student Assistant / HiWi
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Published since: 2025-11-28 , Earliest start: 2025-12-01
Organization Robotic Systems Lab
Hosts Krasnova Elena
Topics Engineering and Technology
Close-Proximity Human-Aware Locomotion
In order to safely navigate a quadrupedal robot through a dense crowd, two conditions need to be satisfied: (1) The robot body does not collide with any of the people in the scene, and (2) the robot does not step on anyone’s feet. The goal of this thesis is to train a locomotion policy that can safely traverse environments with dynamically moving bodies. The problem is treated as an inverse stepping-stone problem, using binary segmentation of the human (foot) areas to create forbidden zones within the elevation map.
Keywords
Robotics, Reinforcement Learning, Crowd Navigation, Human-Robot Interaction
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Master Thesis
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Published since: 2025-11-27 , Earliest start: 2026-02-01 , Latest end: 2026-10-31
Organization Robotic Systems Lab
Hosts Scheidemann Carmen
Topics Information, Computing and Communication Sciences
Free-Viewpoint Teleoperation via Dynamic Gaussian Splatting
Immersive teleoperation should feel natural and precise—like reaching in with your own hands. Most prior work shows this only in static scenes, where the operator guides a robot to an object without truly changing the world [1]. Real tasks aren’t that simple: the moment you grasp, move, or reorient something, the scene changes. If the 3D view doesn’t update, depth cues fade, confidence drops, and mistakes creep in. We propose a single-arm teleoperation system that keeps the 3D world live. Using 3D Gaussian Splatting, the scene is continuously refreshed as objects move, so what you see in VR matches what the robot sees right now. With just a wrist camera and small, smart arm adjustments to avoid occlusions (no extra camera arm), the operator keeps a clear view of the action. In VR, you can pick helpful viewpoints on the fly, working with a photorealistic, always-current reconstruction that boosts precision, speed, and confidence. Moreover, you will get hands-on time with the real robot (you really do get to play with it), with proper safety and supervision. Strong results may also lead to a conference or journal paper submission at the end of the project.
Keywords
Dynamic Gaussian Splatting, Teleoperation, Robotics
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Semester Project , Master Thesis
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Published since: 2025-11-25 , Earliest start: 2025-12-01 , Latest end: 2026-06-01
Applications limited to ETH Zurich
Organization Robotic Systems Lab
Hosts Wilder-Smith Max , Wang Shengzhi , Patil Vaishakh
Topics Information, Computing and Communication Sciences , Engineering and Technology
Design and control of a bio-inspired tensegrity robot leg
This hardware-focused thesis will build a robotic leg from tensegrity structures, taking the first steps to realizing a new class of dynamic legged robot.
Keywords
Robotics, Quadrupeds, Control, Tensegrity, Mechanics, Design
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Semester Project , Bachelor Thesis , Master Thesis
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Published since: 2025-11-25 , Earliest start: 2026-01-01 , Latest end: 2026-11-01
Applications limited to ETH Zurich
Organization Robotic Systems Lab
Hosts Baines Robert
Topics Information, Computing and Communication Sciences , Engineering and Technology
Perceptive object pushing with a quadrupedal manipulator
Legged manipulators combine the mobility of legged robots with the dexterous interaction capabilities of robotic arms. Recent research has demonstrated a variety of skills on such systems using reinforcement learning (RL) controllers trained in simulation. In our previous work [1], we showcased contact-rich, non-prehensile mobile manipulation, where a legged robot was able to move and reorient unknown objects through pushing actions. However, this approach depends on an external motion-capture system to track the object’s 6D pose, a major limitation for real-world and field deployment. This project aims to overcome that challenge by developing a perceptive control policy that performs nonprehensile object manipulation using only onboard sensing. A particularly promising direction is to leverage onboard RGB cameras to provide rich visual feedback regarding both the position and orientation of the object in real time [2]. End-to-end RL (perception to action) and/or teacher-student distillation [3] approaches will be investigated. Through this project, the student will gain extensive hands-on experience in RL, sim-to-real transfer, perception for learning-based control and mobile manipulation. References: 1. Dadiotis et al, “Dynamic object goal pushing with mobile manipulators through model-free constrained reinforcement learning”, ICRA 2025 2. Qureshi et al, “SplatSim: Zero-Shot Sim2Real Transfer of RGB Manipulation Policies Using Gaussian Splatting”, ICRA 2025. 3. Singh et al, “DextrAH-RGB: Visuomotor Policies to Grasp Anything with Dexterous Hands”. arXiv 2024.
Keywords
End-to-end RL, Perceptive Manipulation, Mobile Manipulation
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Master Thesis
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Published since: 2025-11-16 , Earliest start: 2026-01-15 , Latest end: 2026-10-31
Organization Robotic Systems Lab
Hosts Bhardwaj Arjun , Dadiotis Ioannis
Topics Information, Computing and Communication Sciences
Push or pull? 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: 2025-11-14 , Earliest start: 2026-01-07
Organization Robotic Systems Lab
Hosts An Tianxu , Dadiotis Ioannis
Topics Information, Computing and Communication Sciences , Engineering and Technology
Spatially-Enhanced Linear-Attention Transformer for End-to-End Navigation
Transformer architectures have proven highly effective in sequence modelling, but their standard self-attention mechanism suffers from quadratic complexity in sequence length. The work by Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention (Katharopoulos et al., 2020) shows how self-attention can be reformulated as kernel feature maps with a linear complexity implementation. Separately, in navigation and robotics, end-to-end learning of navigation policies (e.g., via reinforcement learning) has shown strong promise; however, many architectures (such as standard RNNs) struggle to build spatial memory — that is, integrating observations from different viewpoints into a coherent spatial representation. For example, the recent work Spatially‑Enhanced Recurrent Memory for Long‑Range Mapless Navigation via End‑to‑End Reinforcement Learning (Yang et al., 2025) proposes Spatially-Enhanced Recurrent Units (SRUs) to address this. This motivates the question: can we combine the efficiency and scalability of linear-attention transformers with a spatial-enhancement mechanism tailored for navigation, thereby achieving end-to-end navigation with long-term spatial memory and efficient inference?
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Semester Project , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-10-28 , Earliest start: 2025-11-15 , Latest end: 2025-12-15
Organization Robotic Systems Lab
Hosts Yang Fan
Topics Information, Computing and Communication Sciences
Learning-based object orientation prediction for handovers
Humans are exceptional at handovers. Besides timing and spatial precision, they also have a high-level understanding of how the other person wants to use the object that is handed over. This information is needed to hand over an object, such that it can be used directly for a specific task. While robots can reason about grasp affordances, the integration of this information with perception and control is missing.
Keywords
Robot-Human Handover, Human-Robot-Interaction, Mobile Manipulation, Robotics
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Semester Project , Master Thesis
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Published since: 2025-10-13 , Earliest start: 2025-02-01 , Latest end: 2025-12-31
Organization Robotic Systems Lab
Hosts Scheidemann Carmen , Tulbure Andreea
Topics Information, Computing and Communication Sciences , Engineering and Technology
HandoverNarrate: Language-Guided Task-Aware Motion Planning for Handovers with Legged Manipulators
This project addresses the challenge of task-oriented human-robot handovers, where a robot must transfer objects in a manner that directly facilitates the human’s next action. In our prior work, we demonstrated that robots can present objects appropriately for immediate human use by leveraging large language models (LLMs) to reason about task context. However, integrating task-specific physical constraints—such as ensuring a full mug remains upright during transport—into the motion planning process remains unsolved. In this project, we aim to extend our existing motion planning framework for legged manipulators by incorporating such constraints. We propose using LLMs to dynamically generate task-aware constraint formulations based on high-level task descriptions and object states. These constraints will then be used to adjust the cost function of the model predictive controller in real time, enabling more context-sensitive and physically appropriate handovers.
Keywords
language-guided motion planning, legged robotics, human-robot collaboration
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Semester Project , Master Thesis
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Published since: 2025-10-13
Applications limited to ETH Zurich
Organization Robotic Systems Lab
Hosts Tulbure Andreea
Topics Information, Computing and Communication Sciences
How to Touch: Exploring Tactile Representations for Reinforcement Learning
Developing and benchmarking tactile representations for dexterous manipulation tasks using reinforcement learning.
Keywords
Reinforcement Learning, Dexterous Manipulation, Tactile Sensing
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Semester Project , Bachelor Thesis , Master Thesis
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Published since: 2025-10-05 , Earliest start: 2025-11-01 , Latest end: 2026-06-01
Applications limited to ETH Zurich
Organization Robotic Systems Lab
Hosts Bhardwaj Arjun , Zurbrügg René
Topics Information, Computing and Communication Sciences
Multi-Agent Local Collision Avoidance on Rough Terrain
Collaborative object transportation is a fundamental aspect of human interaction, particularly in scenarios where a single individual cannot manipulate an object due to its size or weight. Such tasks are common in practical domains, including construction sites and warehouse operations, where coordinated motion and spatial awareness are required to avoid collisions with environmental obstacles.
Keywords
Multi-Agent, Collision Avoidance, Rough Terrain
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Master Thesis
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Published since: 2025-09-25 , Earliest start: 2025-10-06
Applications limited to ETH Zurich
Organization Robotic Systems Lab
Hosts Bray Francesca
Topics Engineering and Technology
Reiforcement Learning of Pretrained Trasformer Models
We want to train RL agents on our new particle simulator, accelerated on the GPU via warp in Isaac sim.
Keywords
Keywords: particle simulation, omniverse, warp, reinforcement learning
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Semester Project , Master Thesis
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Published since: 2025-07-29 , Earliest start: 2026-02-01 , Latest end: 2026-10-31
Organization Robotic Systems Lab
Hosts Terenzi Lorenzo
Topics Information, Computing and Communication Sciences
Multiagent Reinforcement Learning in Terra
We want to train multiple agents in the Terra environment, a fully end-to-end GPU-accelerated environment for RL training.
Keywords
multiagent reinforcement learning, jax, deep learning, planning
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Semester Project , Master Thesis
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Published since: 2025-07-29 , Earliest start: 2026-02-01 , Latest end: 2026-09-30
Organization Robotic Systems Lab
Hosts Terenzi Lorenzo
Topics Information, Computing and Communication Sciences
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