Robotic Systems Lab

Open Opportunities

ANYmal - Navigation using self-supervised depth image representation ETH Zurich Robotic Systems Lab Project Objective: This project aims to develop a new method to integrate depth perception into robots. Specifically we are tackling the task of navigation and locomotion for our legged robot ANYmal. Instead of training RL policies from scratch, we would like to understand the potential performance impacts by using self-supervised training, e.g. using DINO, for monocular depth images. The training may consist of leveraging existing datasets and focuses on learning a useful representation that includes not only geometric hints but potentially also semantic information from depth analog to the recent advantages achieved using RGB images. Computer Hardware, Computer Perception, Memory and Attention, Computer Software, Computer Vision, Electrical and Electronic Engineering Master Thesis, Semester Project

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

RL-based Navigation with Multi-Resolution Semantic Volumetric Maps ETH Zurich Robotic Systems Lab Other organizations: Autonomous Systems Lab Wavemap is a multi-resolution volumetric mapping framework. By integrating its 3D maps with RL pipelines, we want to enable robots to navigate in complex environments. Future semantic support will enable advanced applications like urban navigation and safe traversal of hazardous terrains. Intelligent Robotics Master Thesis

Learning GPU Accelerated Multi-Robot Pick-And-Place ETH Zurich Robotic Systems Lab Improve collision-free path planning for robotic arms used for waste sorting by leveraging the CUDA-accelerated parallel planning library cuRobo. On top of that (scope: Master Thesis) we want to train a reinforcement learning policy that performs object sorting based on the state of the tracked items on the conveyor belt, outputting the end-effector poses to be tracked by cuRobo. Engineering and Technology 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

Sample Efficienct Reinforcement Learning for Particle-Based Excavation 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. Information, Computing and Communication Sciences Master Thesis, Semester Project

Multiagent RL in Terra ETH Zurich Robotic Systems Lab We want to train multiple agents in the Terra environment, a fully end-to-end GPU-accelerated environment for RL training. Information, Computing and Communication Sciences Master Thesis, Semester Project

Bridging Imitation- and Reinforcement learning to interact with Articulated Objects ETH Zurich Robotic Systems Lab Other organizations: ETH Competence Center - ETH AI Center To integrate robots into daily life, they must learn to manipulate diverse environments and objects. Recent advances in imitation learning show promise for teaching visual-motor skills, but require extensive robot-specific data. Reinforcement learning in simulation can learn robust policies in varied settings but struggles with the sim-to-real gap, especially with complex systems and camera observations. This work combines both approaches: using imitation learning to control a five-fingered hand from RGB images and reinforcement learning to control a quadruped's base and arm. Intelligent Robotics, Knowledge Representation and Machine Learning, Robotics and Mechatronics Master Thesis