6 citations · 10 across the 18 of their papers we have counts for
20 papers
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…
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…
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…
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…
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…
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…
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…
Multi-Task Reinforcement Learning for Quadrotors
Jiaxu Xing, Ismail Geles, Yunlong Song +2
Reinforcement learning (RL) has shown great effectiveness in quadrotor control, enabling specialized policies to develop even human-champion-level performance in single-task scenar…
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…
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…
Demonstrating Agile Flight from Pixels without State Estimation
Ismail Geles, Leonard Bauersfeld, Angel Romero +2
Quadrotors are among the most agile flying robots. Despite recent advances in learning-based control and computer vision, autonomous drones still rely on explicit state estimation.…
Bootstrapping Reinforcement Learning with Imitation for Vision-Based Agile Flight
Jiaxu Xing, Angel Romero, Leonard Bauersfeld +1
Learning visuomotor policies for agile quadrotor flight presents significant difficulties, primarily from inefficient policy exploration caused by high-dimensional visual inputs an…
Contrastive Learning for Enhancing Robust Scene Transfer in Vision-based Agile Flight
Jiaxu Xing, Leonard Bauersfeld, Yunlong Song +2
Scene transfer for vision-based mobile robotics applications is a highly relevant and challenging problem. The utility of a robot greatly depends on its ability to perform a task i…
Autonomous Power Line Inspection with Drones via Perception-Aware MPC
Jiaxu Xing, Giovanni Cioffi, Javier Hidalgo-Carrió +1
Drones have the potential to revolutionize power line inspection by increasing productivity, reducing inspection time, improving data quality, and eliminating the risks for human o…