223 citations · 273 across the 42 of their papers we have counts for
46 papers
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…
Temporal Cascading of Planning and Control for Quadrotor MPC
Rudolf Reiter, Chao Qin, Leonard Bauersfeld +1
Many aerial tasks involving quadrotors demand both instant reactivity and long-horizon planning for obstacle avoidance, energy efficiency, or trajectory tracking. High-fidelity mod…
The Reality Gap in Robotics: Challenges, Solutions, and Best Practices
Elie Aljalbout, Jiaxu Xing, Angel Romero +9
Machine learning has facilitated significant advancements across various robotics domains, including navigation, locomotion, and manipulation. Many such achievements have been driv…
What Matters in RL-Based Methods for Object-Goal Navigation? An Empirical Study and A Unified Framework
Hongze Wang, Boyang Sun, Jiaxu Xing +5
Object-Goal Navigation (ObjectNav) is a key capability for deploying mobile robots in everyday environments such as homes, schools, and workplaces. In this task, an agent must loca…
Sight Over Site: Perception-Aware Reinforcement Learning for Efficient Robotic Inspection
Richard Kuhlmann, Jakob Wolfram, Boyang Sun +4
Autonomous inspection is a central problem in robotics, with applications ranging from industrial monitoring to search-and-rescue. Traditionally, inspection has often been reduced…
PA-MPPI: Perception-Aware Model Predictive Path Integral Control for Quadrotor Navigation in Unknown Environments
Yifan Zhai, Rudolf Reiter, Davide Scaramuzza
Quadrotor navigation in unknown environments is critical for practical missions such as search-and-rescue. Solving this problem requires addressing three key challenges: path plann…
Learning on the Fly: Rapid Policy Adaptation via Differentiable Simulation
Jiahe Pan, Jiaxu Xing, Rudolf Reiter +3
Learning control policies in simulation enables rapid, safe, and cost-effective development of advanced robotic capabilities. However, transferring these policies to the real world…
Learning Acrobatic Flight from Preferences
Colin Merk, Ismail Geles, Jiaxu Xing +3
Preference-based reinforcement learning (PbRL) enables agents to learn control policies without requiring manually designed reward functions, making it well-suited for tasks where…
Low-Latency Event-Based Velocimetry for Quadrotor Control in a Narrow Pipe
Leonard Bauersfeld, Davide Scaramuzza
Autonomous quadrotor flight in confined spaces such as pipes and tunnels presents significant challenges due to unsteady, self-induced aerodynamic disturbances. Very recent advance…
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…
HDVIO2.0: Wind and Disturbance Estimation with Hybrid Dynamics VIO
Giovanni Cioffi, Leonard Bauersfeld, Davide Scaramuzza
Visual-inertial odometry (VIO) is widely used for state estimation in autonomous micro aerial vehicles using onboard sensors. Current methods improve VIO by incorporating a model o…
Accelerating Model-Based Reinforcement Learning with State-Space World Models
Maria Krinner, Elie Aljalbout, Angel Romero +1
Reinforcement learning (RL) is a powerful approach for robot learning. However, model-free RL (MFRL) requires a large number of environment interactions to learn successful control…