works on

From the 1 of 5 linked papers with an AI index.

most citedPA-MPPI: Perception-Aware Model Predictive Path Integral Control for Quadrotor Navigation in Unknown Environments

3 citations · 3 across the 4 of their papers we have counts for

collaborators

5 papers

cs.RO2026

Temporal Cascading of Planning and Control for Quadrotor MPC

Rudolf Reiter, Chao Qin, Leonard Bauersfeld +1

The paper introduces UNIQUE, a model predictive control framework that temporally cascades planning and control for quadrotors, integrating high‑ and low‑fidelity models within a s…

cs.RO2026

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…

cs.RO2026

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…

cs.RO20263 cited

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…

cs.RO2026

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…