4 citations · 4 across the 6 of their papers we have counts for
7 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…
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…
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…
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…
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…