Robotic Systems Lab

Open Opportunities

VR in Habitat 3.0 ETH Zurich Computer Vision and Geometry Group Other organizations: Robotic Systems Lab Motivation: Explore the newly improved Habitat 3.0 simulator with a special focus on the Virtual Reality Features. This project is meant to be an exploration task on the Habitat 3.0 simulator, exploring all the newly introduced features focusing specifically on the implementation of virtual reality tools for scene navigation. The idea is to extend these features to self created environments in Unreal Engine that build uppon Habitat Artificial Intelligence and Signal and Image Processing Semester Project

Diffusion Navigation for ANYmal - Semester Project, Master Thesis ETH Zurich Robotic Systems Lab Navigation of ANYmal using Diffusion Policy including real-world experiments and deployment. References [1] Reuss, Moritz, et al. "Goal-conditioned imitation learning using score-based diffusion policies." RSS 2023 [2] Shah, Dhruv, et al. "ViNT: A foundation model for visual navigation." CoRL 2023 [3] Sridhar, Ajay, et al. "Nomad: Goal masked diffusion policies for navigation and exploration." 2024 (ICRA). IEEE, 2024. Computer Communications Networks, Computer Graphics, Computer Hardware, Computer Perception, Memory and Attention, Computer Vision, Computer-Human Interaction, Engineering/Technology Instrumentation, Intelligent Robotics, Robotics and Mechatronics, Software Engineering Master Thesis, Semester Project

Live Limited View Dynamic Gaussian Splatting ETH Zurich Robotic Systems Lab In this project we seek to reconstruct 3D Gaussian Splatting scenes and capture motion as it happens. Computer Graphics, Computer Vision, Intelligent Robotics Master Thesis

Large Scale 3D Gaussian Splatting SLAM ETH Zurich Robotic Systems Lab This project aims to create large scale 3D Gaussian Splatting scenes using online robotic data. Computer Graphics, Computer Vision, Intelligent Robotics Master Thesis

Learning Diverse Behaviors: Diffusion Meets RL ETH Zurich Robotic Systems Lab This thesis will tackle the challenge of multi-modality in robot learning by integrating diffusion models into reinforcement learning (RL). The project is in collaboration with Prof. Gerhard Neumann from KIT (https://alr.iar.kit.edu/21_65.php) Intelligent Robotics, Knowledge Representation and Machine Learning Master Thesis, Semester Project

Applying Large Language Models (LLMs) to enhance skill acquisition for ANYmal robot ETH Zurich Robotic Systems Lab The goal of this project is to apply LLMs to teach the ANYmal robot new low-level skills via Reinforcement Learning (RL) that the task planner identifies to be missing. Intelligent Robotics Master Thesis

Reinforcement Learning for Control of Shape-Shifting Robot Legs ETH Zurich Robotic Systems Lab We are working toward robots that transform their shape adapt to new tasks and environments. This project will entail developing control policies in simulation and deploying them on hardware, with the goal of controlling a quadruped that can change the shape of its legs to accomplish new and useful tasks (see attached image a). Intelligent Robotics, Robotics and Mechatronics, Simulation and Modelling Master Thesis, Semester Project

Reinforcement Learning for Excavation Planning ETH Zurich Robotic Systems Lab 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. Intelligent Robotics Master Thesis, Semester Project

Reinforcement Learning for Particle-Based Excavation in Isaac Sim ETH Zurich Robotic Systems Lab We want to train RL agents on our new particle simulator, accelerated on the GPU via warp in Isaac sim. Intelligent Robotics Master Thesis, Semester Project

Terrain Neural Scene Reconstruction for RL ETH Zurich Robotic Systems Lab In this project, our goal is to build a practical solution for reconstructing 3D earthworks scenes using incomplete point cloud data. We plan to train an encoder-decoder neural network that can accurately recreate the missing parts of the scene. However, our main emphasis lies in creating powerful latent representations that will enable us to train reinforcement learning agents for digging tasks. Intelligent Robotics Master Thesis, Semester Project