53 citations · 65 across the 17 of their papers we have counts for
19 papers
Bridging Performance and Generalization in Reinforcement Learning for Agile Flight
Jonathan Green, Jiaxu Xing, Nico Messikommer +2
Autonomous drone racing is a fundamentally challenging regime for autonomous aerial robots, requiring time-optimal control while operating under persistent actuation saturation. Wh…
Generative Event Pretraining with Foundation Model Alignment
Jianwen Cao, Jiaxu Xing, Nico Messikommer +1
Event cameras provide robust visual signals under fast motion and challenging illumination conditions thanks to their microsecond latency and high dynamic range. However, their uni…
Approximate Imitation Learning for Event-based Quadrotor Flight in Cluttered Environments
Nico Messikommer, Jiaxu Xing, Leonard Bauersfeld +3
Event cameras offer high temporal resolution and low latency, making them ideal sensors for high-speed robotic applications where conventional cameras suffer from motion blur. Howe…
Motion-aware Event Suppression for Event Cameras
Roberto Pellerito, Nico Messikommer, Giovanni Cioffi +2
Event cameras report asynchronously per-pixel brightness changes with microsecond latency, encoding dynamic visual information as a sparse stream of events. However, their extreme…
Hybrid Event Frame Sensors: Modeling, Calibration, and Simulation
Yunfan Lu, Nico Messikommer, Xiaogang Xu +5
Hybrid event-frame sensors integrate an Event Vision Sensor (EVS) and an Active Pixel Sensor (APS) within a single chip, combining the high dynamic range and low latency of the EVS…
Event-Based De-Snowing for Autonomous Driving
Manasi Muglikar, Nico Messikommer, Marco Cannici +1
Adverse weather conditions, particularly heavy snowfall, pose significant challenges to both human drivers and autonomous vehicles. Traditional image-based de-snowing methods often…
Reading in the Dark with Foveated Event Vision
Carl Brander, Giovanni Cioffi, Nico Messikommer +1
Current smart glasses equipped with RGB cameras struggle to perceive the environment in low-light and high-speed motion scenarios due to motion blur and the limited dynamic range o…
ForesightNav: Learning Scene Imagination for Efficient Exploration
Hardik Shah, Jiaxu Xing, Nico Messikommer +3
Understanding how humans leverage prior knowledge to navigate unseen environments while making exploratory decisions is essential for developing autonomous robots with similar abil…
Student-Informed Teacher Training
Nico Messikommer, Jiaxu Xing, Elie Aljalbout +1
Imitation learning with a privileged teacher has proven effective for learning complex control behaviors from high-dimensional inputs, such as images. In this framework, a teacher…
S7: Selective and Simplified State Space Layers for Sequence Modeling
Taylan Soydan, Nikola Zubić, Nico Messikommer +2
A central challenge in sequence modeling is efficiently handling tasks with extended contexts. While recent state-space models (SSMs) have made significant progress in this area, t…
Environment as Policy: Learning to Race in Unseen Tracks
Hongze Wang, Jiaxu Xing, Nico Messikommer +1
Reinforcement learning (RL) has achieved outstanding success in complex robot control tasks, such as drone racing, where the RL agents have outperformed human champions in a known…
Reinforcement Learning Meets Visual Odometry
Nico Messikommer, Giovanni Cioffi, Mathias Gehrig +1
Visual Odometry (VO) is essential to downstream mobile robotics and augmented/virtual reality tasks. Despite recent advances, existing VO methods still rely on heuristic design cho…
Contrastive Initial State Buffer for Reinforcement Learning
Nico Messikommer, Yunlong Song, Davide Scaramuzza
In Reinforcement Learning, the trade-off between exploration and exploitation poses a complex challenge for achieving efficient learning from limited samples. While recent works ha…
Revisiting Token Pruning for Object Detection and Instance Segmentation
Yifei Liu, Mathias Gehrig, Nico Messikommer +2
Vision Transformers (ViTs) have shown impressive performance in computer vision, but their high computational cost, quadratic in the number of tokens, limits their adoption in comp…
Data-Driven Feature Tracking for Event Cameras With and Without Frames
Nico Messikommer, Carter Fang, Mathias Gehrig +2
Because of their high temporal resolution, increased resilience to motion blur, and very sparse output, event cameras have been shown to be ideal for low-latency and low-bandwidth…
Multi-Bracket High Dynamic Range Imaging with Event Cameras
Nico Messikommer, Stamatios Georgoulis, Daniel Gehrig +5
Modern high dynamic range (HDR) imaging pipelines align and fuse multiple low dynamic range (LDR) images captured at different exposure times. While these methods work well in stat…
ESS: Learning Event-based Semantic Segmentation from Still Images
Zhaoning Sun, Nico Messikommer, Daniel Gehrig +1
Retrieving accurate semantic information in challenging high dynamic range (HDR) and high-speed conditions remains an open challenge for image-based algorithms due to severe image…
Bridging the Gap between Events and Frames through Unsupervised Domain Adaptation
Nico Messikommer, Daniel Gehrig, Mathias Gehrig +1
Reliable perception during fast motion maneuvers or in high dynamic range environments is crucial for robotic systems. Since event cameras are robust to these challenging condition…
Event-based Asynchronous Sparse Convolutional Networks
Nico Messikommer, Daniel Gehrig, Antonio Loquercio +1
Event cameras are bio-inspired sensors that respond to per-pixel brightness changes in the form of asynchronous and sparse "events". Recently, pattern recognition algorithms, such…