Student Projects
To apply, please send your CV, your Ms and Bs transcripts by email to all the contacts indicated below the project description. Do not apply on SiROP . Since Prof. Davide Scaramuzza is affiliated with ETH, there is no organizational overhead for ETH students. Custom projects are occasionally available. If you would like to do a project with us but could not find an advertized project that suits you, please contact Prof. Davide Scaramuzza directly to ask for a tailored project (sdavide at ifi.uzh.ch).
Upon successful completion of a project in our lab, students may also have the opportunity to get an internship at one of our numerous industrial and academic partners worldwide (e.g., NASA/JPL, University of Pennsylvania, UCLA, MIT, Stanford, ...).
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Evolutionary Optimization Meets Differentiable Simulation
The goal is to investigate how the latest differentiable simulation strategies push the limits of learning real-world tasks such as agile flight.
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Codesign of shape and control: A study in autonomous perching
We aim to co-design a controller and the shape of a glider for a perching maneuver, involving deployment on the real system.
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Observability-and-Perception-aware Planning and Control for Event-Based Object Reconstruction
Design a model-based / learning-based controller that is aware of state observability and sensor perception objectives for object reconstruction using a quadrotor with an egocentric camera.
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Vision-Based Drone Racing from Raw Pixels with Deep Reinforcement Learning
Explore the possibility of high-speed drone racing using raw RGB camera images only.
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High-Speed Object Pickup During Quadrotor Flight with Reinforcement Learning
Explore the possibility of catching/picking up an object during high-speed agile flight, with potential application in fast turnaround delivery.
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Event Representation Learning for Control with Visual Distractors
This project develops event-based representation learning methods for control tasks in environments with visual distractors, leveraging sparse, high-temporal-resolution event data to improve robustness and efficiency over traditional frame-based approaches.
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Rethinking RNNs for Neuromorphic Computing and Event-based Vision
This thesis develops hardware-optimized recurrent neural network architectures with novel parallelization and kernel-level strategies to efficiently process event-based vision data for real-time neuromorphic and GPU-based applications.
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Spiking Architectures for Advanced Event-Based Temporal Reasoning
This thesis explores novel spiking neural network architectures that leverage event-based vision data and emergent neural synchronization to enable efficient and robust temporal reasoning for dynamic scene understanding and sequential tasks.
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Reinforcement Learning with World Models
Explore and develop model-based RL algorithms.
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Event‑based Temporal Segmentation & Tracking
Event cameras are revolutionary sensors that capture pixel-level illumination changes with microsecond latency, providing significant advantages in high-speed and high-dynamic-range scenarios where traditional cameras suffer from motion blur. Recently, large-scale foundational segmentation models have been successfully adapted to the event domain. However, these current approaches remain constrained to per-frame analysis, treating continuous event streams as isolated, static snapshots and ignoring temporal consistency. At the same time, existing event-based methods for moving object segmentation can isolate motion but fail to maintain instance identity over time—they can segment moving pixels, but they cannot "track" specific objects. This project aims to bridge the gap between static foundational segmentation and dynamic motion analysis by developing the first comprehensive tracker for event cameras. The objective is to design a system capable of not only segmenting arbitrary objects but also maintaining their identity consistently across long, high-speed sequences. The student will extend current spatial feature adaptation strategies to support temporal identity, effectively transforming a frame-by-frame instance segmenter into a robust Video Object Segmentation (VOS) tracker. Furthermore, to handle severe object occlusions and rapid, erratic motion, the project will explore sparse temporal memory mechanisms that prevent identity-switching. Finally, to rigorously test the system's reliability, the student will establish a novel benchmark for dense segmentation in extreme edge cases, such as night driving with severe glare and rapid evasive maneuvers.
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Learning Rapid UAV Exploration with Foundation Models
Recent research has demonstrated significant success in integrating foundational models with robotic systems. In this project, we aim to investigate how these foundational models can enhance the vision-based navigation of UAVs. The drone will utilize learned semantic relationships from extensive world-scale data to actively explore and navigate through unfamiliar environments. While previous research primarily focused on ground-based robots, our project seeks to explore the potential of integrating foundational models with aerial robots to enhance agility and flexibility.
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Event Cameras for Agile Drone 3D Perception
Fast drones often move too quickly for conventional cameras, resulting in motion blur and unreliable 3D perception. This project investigates how event cameras, which capture microsecond-level brightness changes, can help drones “see clearly” during aggressive flight. The student will develop learning-based methods that combine standard images, event data, and motion cues to recover sharp visual information and reconstruct the 3D environment.