CALOS is a runtime safety layer for quadrotor control that enforces attitude constraints without altering the underlying deep reinforcement learning algorithm. It formulates tilt-angle inequalities and a Lyapunov descent condition into a single quadratic program, solved exactly via active-set enumeration over a three-dimensional torque space. In NVIDIA Isaac Lab trajectory-tracking tasks, CALOS reduces lateral tracking error by 55‑60% compared to an unconstrained Proximal Policy Optimization baseline and eliminates attitude-constraint violations during training.
By Fabrizio Cesareo, Sebastiano Mengozzi, Nicola Mimmo, Andrea Acquaviva
arXiv:2607. 15003v1 Announce Type: new Abstract: The deployment of autonomous cyber-physical systems in safety-critical environments requires closed-loop control strategies (i.
By Riccardo Curcio, Toni Mancini, Enrico Tronci
arXiv:2606. 23444v2 Announce Type: replace-cross Abstract: Accurate dynamics models are critical for informed decision-making in robotic systems, particularly for agile aerial vehicles operating under uncertainty.
By Pratyaksh Rao, Wancong Zhang, Randall Balestriero, Yann LeCun, Giuseppe Loianno
Reinforcement learning (RL) enables the synthesis of control policies directly from data, making it highly appealing for complex cyber-physical systems (CPSs) and robotics. A persistent challenge, however, is ensuring strict, hard safety constraints during the active learning phase.
arXiv:2607. 07252v1 Announce Type: new Abstract: Reinforcement learning (RL) enables the synthesis of control policies directly from data, making it highly appealing for complex cyber-physical systems (CPSs) and robotics.
By Georg Sch\"afer, Jakob Rehrl, Stefan Huber, Simon Hirlaender
arXiv:2608. 19425v1 Announce Type: cross Abstract: Reliable performance evaluation is a central bottleneck for deploying robot-learning policies in real-world conditions.
By Dijie Zhu, Seunghun Oh, Ruopeng Huang, Zhiyu Huang, Jiaqi Ma, Chen Tang