Drones are increasingly used in applications such as last-mile delivery and infrastructure inspection, but their safe operation, especially in high-speed scenarios, remains a critical challenge. Existing vision- and LiDAR-based obstacle localization methods suffer from motion blur, latency, and low spatio-temporal resolution, making them inadequate for detecting and tracking fast-moving objects. In this work, we present HawkEye, a drone obstacle avoidance system that fuses event cameras and LiDAR to achieve high-frequency, accurate 3D tracking of dynamic objects. By leveraging the complementary strengths of both sensors, Hawkeye enables robust real-time sensing and safe evasive maneuvers, addressing a key requirement for the large-scale deployment of autonomous drones. Demo: https://wenhua00.github.io/HawkEye/.

Robot sports, characterized by well-defined objectives, explicit rules, and dynamic interactions, present ideal scenarios for demonstrating embodied intelligence. In this paper, we present VolleyBots, a novel robot sports testbed where multiple drones cooperate and compete in the sport of volleyball under physical dynamics. VolleyBots integrates three features within a unified platform: competitive and cooperative gameplay, turn-based interaction structure, and agile 3D maneuvering.These intertwined features yield a complex problem combining motion control and strategic play, with no available expert demonstrations.We provide a comprehensive suite of tasks ranging from single-drone drills to multi-drone cooperative and competitive tasks, accompanied by baseline evaluations of representative reinforcement learning (RL), multi-agent reinforcement learning (MARL) and game-theoretic algorithms. Simulation results show that on-policy RL methods outperform off-policy methods in single-agent tasks, but both approaches struggle in complex tasks that combine motion control and strategic play.We additionally design a hierarchical policy which achieves 69.5% win rate against the strongest baseline in the 3 vs 3 task, demonstrating its potential for tackling the complex interplay between low-level control and high-level strategy.To highlight VolleyBots’ sim-to-real potential, we further demonstrate the zero-shot deployment of a policy trained entirely in simulation on real-world drones.

Ambient backscatter communication (AmBC), whose passive tags transmit information to readers through radio frequency (RF) signals in the air, has attracted much attention due to its promising prospects in green internet of things (IoTs). However, AmBC detection has faced a new challenge at the reader because the received signal is mainly a hybrid signal from direct link and backscatter link, which makes it difficult to detect symbols of the backscatter tag. To address this problem, in this paper, frequency diverse array (FDA) is utilized to transmit time-variant ambient signal, which can separate the two link signals received by the reader. We propose an adaptive dual-threshold detector by employing the time-variant characteristic of FDA RF signal, which is based on the position of the boundary backscatter tag defined. Specifically, the closed form expressions for maximum likelihood (ML) threshold and adaptive thresholds are derived, respectively. Furthermore, the computational complexities are analyzed. Finally, numerical results are presented to show that the proposed adaptive dual-threshold detector achieves better bit error rate (BER) performance than the conventional ML detector.