 Robotic Systems LabOpen OpportunitiesRobots need to manipulate a wide range of unknown objects, from transparent to shiny surfaces. The goal of this project is to investigate learning techniques to bridge the visual domain gap between high-fidelity rendered scenes and real-world images for scene understanding.
- Computer Vision, Intelligent Robotics, Knowledge Representation and Machine Learning
- Master Thesis
| Diffusion models have a huge potential in motion planning and navigation. In this project, we focus on generating spline-based trajectories using diffusion models able to make ANYmal navigate in extremely challenging dynamic environments - Intelligent Robotics
- Master Thesis, Semester Project
| 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
| We want to develop a generalist digging agent that is able to do multiple tasks, such as digging and moving loose soil, using our legged excavator. We plan to use decision transformers, trained on offline data, to accomplish these tasks - Intelligent Robotics
- Master Thesis, Semester Project
| In this work we would utilize reinforcement learning, neural network actuator modeling, and perception for the control and arm motion planning of a 75ton excavator with a free-swinging joint.
The project will be in collaboration with LIEBHERR, a german company building excavators and other construction machines. - Computer Vision, Intelligent Robotics, Mechanical Engineering, Neural Networks, Genetic Alogrithms and Fuzzy Logic, Systems Theory and Control
- CLS Student Project (MPG ETH CLS), Collaboration, ETH Zurich (ETHZ), Internship, Master Thesis, Other specific labels, Semester Project
| This research addresses the limitations of conventional simulation environments in capturing the complexities of forest terrains for robotics and automation applications in forest management. Utilizing real-world LiDAR and RGBD data, the project aims to develop a high-fidelity meshing pipeline. These advanced simulation environments will significantly improve perception algorithms and navigation strategies, thereby enhancing resource allocation and emergency response capabilities in forest settings. - Intelligent Robotics
- Master Thesis, Semester Project
| Recently, there has been significant progress in learning object manipulation from human videos. One of the key limitations of these methods is the absence of tactile feedback, making it hard to identify whether or not a contact has been made. Thus, in this project, we would like to investigate how we can use demonstrations including tactile measurements to learn object manipulation. - Intelligent Robotics, Mechanical Engineering
- Master Thesis
| Soft robots, characterized by highly flexible components, hold the potential to attain remarkable locomotion capabilities that surpass those of traditional rigid robots. However, effectively modeling, simulating, and controlling these advanced designs present significant challenges. The nonlinear dynamics inherent in these systems elude accurate representation by model-based controllers, while model-free algorithms face limitations in accessing efficient simulation environments necessary for agent training. Thus, developing robust and effective reinforcement learning (RL) approaches tailored to the unique characteristics of soft robots is imperative. In this student project, we aim to explore and apply RL techniques to enhance the locomotion performance of soft robots by addressing the aforementioned challenges. Through innovative modeling strategies, sophisticated simulation environments, and state-of-the-art model-free RL algorithms, we seek to unlock the full potential of legged robots with flexible components, enabling them to achieve exceptional locomotion capabilities and pave the way for advancements in this field. - Engineering and Technology
- Master Thesis
| In this project you will work with the Gravis team to develop 1.) a segmentation algorithm using point clouds to detect the shovel pose, 2.) a calibration procedure to calibrate the lidar to the machine using the detected shovel pose and the existing arm pose state estimation, and 3.) a learning algorithm that outputs the shovel pose given images. - Engineering and Technology
- Master Thesis, Semester Project
| Minimal is a mostly 3D-printed, highly reconfigurable robot. Using state-of-the-art reinforcement learning, we will explore novel and highly advanced hardware design possibilities that will be coupled with design optimization through learning. This will enable the next generation of robots to be a lot faster, stronger and agile. - Engineering and Technology
- Master Thesis, Semester Project
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