Fully autonomous racing demands not only high-speed driving but also fair and courteous maneuvers. In this paper, we propose an autonomous racing framework that learns complex racing behaviors from expert demonstrations using hierarchical policy abstractions. At the trajectory level, our policy model predicts a dense distribution map indicating the likelihood of trajectories learned from offline demonstrations. The maximum likelihood trajectory is then passed to the control-level policy, which generates control inputs in a residual fashion, considering vehicle dynamics at the limits of performance. We evaluate our framework in a high-fidelity racing simulator and compare it against competing baselines in challenging multi-agent adversarial scenarios. Quantitative and qualitative results show that our trajectory planning policy significantly outperforms the baselines, and the residual control policy improves lap time and tracking accuracy. Moreover, challenging closed-loop experiments with ten opponents show that our framework can overtake other vehicles by understanding nuanced interactions, effectively balancing performance and courtesy like professional drivers.

Abstract-- We present an integrated UAV-hexapod robotic system designed for GNSS-denied maritime operations, capable of autonomous deployment and retrieval of a hexapod robot via a winch mechanism installed on a UAV. This system is intended to address the challenges of localization, control, and mobility in dynamic maritime environments. Experimental results demonstrate the effectiveness of this system in real-world scenarios, validating its performance during field tests in both controlled and operational conditions in the MBZIRC 2023 Maritime Challenge. I. INTRODUCTION Unmanned Aerial Vehicles (UAVs) have become an essential component of modern robotics, widely used in various applications, including surveillance, inspection, search and Figure 1: UAV-Hexapod system executing its mission in a rescue, and transportation. Their ability to fly over challenging GNSS-denied maritime environment. Team KAIST won 2nd terrains and access remote areas has expanded the place in the MBZIRC 2023 Maritime Challenge.

In real-world field operations, aerial grasping systems face significant challenges in dynamic environments due to strong winds, shifting surfaces, and the need to handle heavy loads. Particularly when dealing with heavy objects, the powerful propellers of the drone can inadvertently blow the target object away as it approaches, making the task even more difficult. To address these challenges, we introduce SPIBOT, a novel drone-tethered mobile gripper system designed for robust and stable autonomous target retrieval. SPIBOT operates via a tether, much like a spider, allowing the drone to maintain a safe distance from the target. To ensure both stable mobility and secure grasping capabilities, SPIBOT is equipped with six legs and sensors to estimate the robot's and mission's states. It is designed with a reduced volume and weight compared to other hexapod robots, allowing it to be easily stowed under the drone and reeled in as needed. Designed for the 2024 MBZIRC Maritime Grand Challenge, SPIBOT is built to retrieve a 1kg target object in the highly dynamic conditions of the moving deck of a ship. This system integrates a real-time action selection algorithm that dynamically adjusts the robot's actions based on proximity to the mission goal and environmental conditions, enabling rapid and robust mission execution. Experimental results across various terrains, including a pontoon on a lake, a grass field, and rubber mats on coastal sand, demonstrate SPIBOT's ability to efficiently and reliably retrieve targets. SPIBOT swiftly converges on the target and completes its mission, even when dealing with irregular initial states and noisy information introduced by the drone.

This paper focuses on the acquisition of mapless navigation skills within unknown environments. We introduce the Skill Q-Network (SQN), a novel reinforcement learning method featuring an adaptive skill ensemble mechanism. Unlike existing methods, our model concurrently learns a high-level skill decision process alongside multiple low-level navigation skills, all without the need for prior knowledge. Leveraging a tailored reward function for mapless navigation, the SQN is capable of learning adaptive maneuvers that incorporate both exploration and goal-directed skills, enabling effective navigation in new environments. Our experiments demonstrate that our SQN can effectively navigate complex environments, exhibiting a 40% higher performance compared to baseline models. Without explicit guidance, SQN discovers how to combine low-level skill policies, showcasing both goal-directed navigations to reach destinations and exploration maneuvers to escape from local minimum regions in challenging scenarios. Remarkably, our adaptive skill ensemble method enables zero-shot transfer to out-of-distribution domains, characterized by unseen observations from non-convex obstacles or uneven, subterranean-like environments.

We introduce MoNet, a novel functionally modular network for self-supervised and interpretable end-to-end learning. By leveraging its functional modularity with a latent-guided contrastive loss function, MoNet efficiently learns task-specific decision-making processes in latent space without requiring task-level supervision. Moreover, our method incorporates an online, post-hoc explainability approach that enhances the interpretability of end-to-end inferences without compromising sensorimotor control performance. In real-world indoor environments, MoNet demonstrates effective visual autonomous navigation, outperforming baseline models by 7% to 28% in task specificity analysis. We further explore the interpretability of our network through post-hoc analysis of perceptual saliency maps and latent decision vectors. This provides valuable insights into the incorporation of explainable artificial intelligence into robotic learning, encompassing both perceptual and behavioral perspectives. Supplementary materials are available at https://sites.google.com/view/monet-lgc.

In aerial combat, dogfighting poses intricate challenges that demand an understanding of both strategic maneuvers and the aerodynamics of agile fighter aircraft. In this paper, we introduce TempFuser, a novel long short-term temporal fusion transformer designed to learn tactical and agile flight maneuvers in aerial dogfights. Our approach employs two distinct LSTM-based input embeddings to encode long-term sparse and short-term dense state representations. By integrating these embeddings through a transformer encoder, our model captures the tactics and agility of fighter jets, enabling it to generate end-to-end flight commands that secure dominant positions and outmaneuver the opponent. After extensive training against various types of opponent aircraft in a high-fidelity flight simulator, our model successfully learns to perform complex fighter maneuvers, consistently outperforming several baseline models. Notably, our model exhibits human-like strategic maneuvers even when facing adversaries with superior specifications, all without relying on explicit prior knowledge. Moreover, it demonstrates robust pursuit performance in challenging supersonic and low-altitude environments. Demo videos are available at https://sites.google.com/view/tempfuser.

In this letter, we propose a model parameter identification method via a hyperparameter optimization scheme (MI-HPO). Our method adopts an efficient explore-exploit strategy to identify the parameters of dynamic models in a data-driven optimization manner. We utilize our method for model parameter identification of the AV-21, a full-scaled autonomous race vehicle. We then incorporate the optimized parameters for the design of model-based planning and control systems of our platform. In experiments, MI-HPO exhibits more than 13 times faster convergence than traditional parameter identification methods. Furthermore, the parametric models learned via MI-HPO demonstrate good fitness to the given datasets and show generalization ability in unseen dynamic scenarios. We further conduct extensive field tests to validate our model-based system, demonstrating stable obstacle avoidance and high-speed driving up to 217 km/h at the Indianapolis Motor Speedway and Las Vegas Motor Speedway. The source code for our work and videos of the tests are available at https://github.com/hynkis/MI-HPO.

While the majority of autonomous driving research has concentrated on everyday driving scenarios, further safety and performance improvements of autonomous vehicles require a focus on extreme driving conditions. In this context, autonomous racing is a new area of research that has been attracting considerable interest recently. Due to the fact that a vehicle is driven by its perception, planning, and control limits during racing, numerous research and development issues arise. This paper provides a comprehensive overview of the autonomous racing system built by team KAIST for the Indy Autonomous Challenge (IAC). Our autonomy stack consists primarily of a multi-modal perception module, a high-speed overtaking planner, a resilient control stack, and a system status manager. We present the details of all components of our autonomy solution, including algorithms, implementation, and unit test results. In addition, this paper outlines the design principles and the results of a systematical analysis. Even though our design principles are derived from the unique application domain of autonomous racing, they can also be applied to a variety of safety-critical, high-cost-of-failure robotics applications. The proposed system was integrated into a full-scale autonomous race car (Dallara AV-21) and field-tested extensively. As a result, team KAIST was one of three teams who qualified and participated in the official IAC race events without any accidents. Our proposed autonomous system successfully completed all missions, including overtaking at speeds of around $220 km/h$ in the IAC@CES2022, the world's first autonomous 1:1 head-to-head race.

Collision-free path planning is an essential requirement for autonomous exploration in unknown environments, especially when operating in confined spaces or near obstacles. This study presents an autonomous exploration technique using a small drone. A local end-point selection method is designed using LiDAR range measurement and then generates the path from the current position to the selected end-point. The generated path shows the consistent collision-free path in real-time by adopting the Euclidean signed distance field-based grid-search method. The simulation results consistently showed the safety, and reliability of the proposed path-planning method. Real-world experiments are conducted in three different mines, demonstrating successful autonomous exploration flight in environments with various structural conditions. The results showed the high capability of the proposed flight autonomy framework for lightweight aerial-robot systems. Besides, our drone performs an autonomous mission during our entry at the Tunnel Circuit competition (Phase 1) of the DARPA Subterranean Challenge.