223 citations · 273 across the 42 of their papers we have counts for
13 papers · 1 filter
Adaptive Expert Guidance for Efficient On-Policy Reinforcement Learning
Daniele Affinita, Ming Xu, Rudolf Reiter +2
With massively parallel simulation, on-policy Reinforcement Learning methods such as PPO have become standard in many domains. However, learning from scratch is sample-inefficient…
Accelerating Reinforcement Learning via MPC Solver-Gradient Guidance for Weights-varying MPC
Baha Zarrouki, Arslan Thobani, Jasper Hoffmann +6
In Model Predictive Control (MPC), cost-function weights shape closed-loop behavior, yet changing conditions often make fixed parametrizations suboptimal and motivate context-depen…
Learning to Throw: Agile and Accurate Cable-Suspended Payload Delivery with a Quadrotor
Yifan Zhai, Elia Raimondi, Yunfan Ren +4
Quadrotors offer the agility needed to rapidly transport suspended payloads during time-critical applications, including search-and-rescue and medical delivery. While suspended-pay…
Continual Robot Policy Learning via Variational Neural Dynamics
Jiaxu Xing, Zhiyuan Zhu, Yunfan Ren +4
Robots deployed in the real world rarely operate under a single fixed dynamics model: wind changes, payloads vary, batteries drain, contacts shift, and hardware wears. Yet most lea…
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…
Neural Events: Discrete Asynchronous Autoencoders for Event-Based Vision
Roberto Pellerito, Daniel Gehrig, Shintaro Shiba +1
Event cameras capture dynamic scenes with exceptional temporal fidelity by representing them as a continuous stream of microsecond resolution \textit{events}. Each individual event…
Superhuman Safe and Agile Racing through Multi-Agent Reinforcement Learning
Ismail Geles, Leonard Bauersfeld, Markus Wulfmeier +1
Autonomous systems have achieved superhuman performance in isolation or simulation, yet they remain brittle in shared, dynamic real-world spaces. This failure stems from the domina…
Vision-Based Agile Landing on Turbulent Waters
Dimosthenis Angelis, Leonard Bauersfeld, Davide Scaramuzza +1
Autonomous landing of Unmanned Aerial Vehicles on maritime vessels is challenging due to the coupled motion of the vehicle and landing platform in open-sea conditions. This paper p…
Online Adaptation of Visual Odometry Frontends with Image-Conditioned Reinforcement Learning
Simone Nascivera, Leonard Bauersfeld, Jeff Delaune +1
Visual odometry (VO) frontends are typically tuned offline by domain experts on pre-recorded datasets and then deployed with fixed hyperparameters. Yet a configuration that perform…
Perception-Aware Time-Optimal Planning for Quadrotor Waypoint Flight
Chao Qin, Jiaxu Xing, Rudolf Reiter +4
Agile quadrotor flight pushes the limits of control, actuation, and onboard perception. While time-optimal trajectory planning has been extensively studied, existing approaches typ…
EAAE: Energy-Aware Autonomous Exploration for UAVs in Unknown 3D Environments
Jacob Elskamp, Moji Shi, Leonard Bauersfeld +2
Battery-powered multirotor unmanned aerial vehicles (UAVs) can rapidly map unknown environments, but mission performance is often limited by energy rather than geometry alone. Stan…
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…
Learning Agile Quadrotor Flight in the Real World
Yunfan Ren, Zhiyuan Zhu, Jiaxu Xing +1
Learning-based controllers have achieved impressive performance in agile quadrotor flight but typically rely on massive training in simulation, necessitating accurate system identi…