We introduce Any6D, a model-free framework for 6D object pose estimation that requires only a single RGB-D anchor image to estimate both the 6D pose and size of unknown objects in novel scenes. Unlike existing methods that rely on textured 3D models or multiple viewpoints, Any6D leverages a joint object alignment process to enhance 2D-3D alignment and metric scale estimation for improved pose accuracy. Our approach integrates a render-and-compare strategy to generate and refine pose hypotheses, enabling robust performance in scenarios with occlusions, non-overlapping views, diverse lighting conditions, and large cross-environment variations. We evaluate our method on five challenging datasets: REAL275, ToyotaLight, HO3D, YCBINEOAT, and LM-O, demonstrating its effectiveness in significantly outperforming state-of-the-art methods for novel object pose estimation. Project page: https://taeyeop.com/any6d

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 SPI- BOT, 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.

A pilot needs to manipulate various gadgets in the cockpit based on vast knowledge of rules and procedures while verbally communicating with air traffic controllers. While precision manipulation in the cockpit during the flight is already a difficult task, a far more difficult thing is how to make a robot learn all the knowledge needed to fly an airplane in accordance with all the rules and regulations. As a pioneering effort, this paper introduces LLM-PIBOT, which leverages the latest advances in Large Language Models (LLMs) to empower a humanoid pilot robot (PIBOT) to take the full authority of an airplane by understanding and executing complex procedures outlined in Pilot’s Operating Handbooks (POHs). Unlike traditional rule-based methods, LLM-PIBOT system infers suitable flight procedures, employs an embedding process to accurately identify relevant procedures within documents, and structures the text-extracted flight tasks into tuples using our carefully crafted prompts. This approach enables PIBOT to adapt to the given POHs, generating and executing task plans in real-time in response to commands and situations. Experimental results show that LLM-PIBOT can comprehend and follow the complex procedures specified in the manuals and to fly the airplane on a full-scale simulator using the generated flight plans.

Recently, numerous studies have investigated cooperative traffic systems using the communication among vehicle-to-everything (V2X). Unfortunately, when multiple autonomous vehicles are deployed while exposed to communication failure, there might be a conflict of ideal conditions between various autonomous vehicles leading to adversarial situation on the roads. In South Korea, virtual and real-world urban autonomous multi-vehicle races were held in March and November of 2021, respectively. During the competition, multiple vehicles were involved simultaneously, which required maneuvers such as overtaking low-speed vehicles, negotiating intersections, and obeying traffic laws. In this study, we introduce a fully autonomous driving software stack to deploy a competitive driving model, which enabled us to win the urban autonomous multi-vehicle races. We evaluate module-based systems such as navigation, perception, and planning in real and virtual environments. Additionally, an analysis of traffic is performed after collecting multiple vehicle position data over communication to gain additional insight into a multi-agent autonomous driving scenario. Finally, we propose a method for analyzing traffic in order to compare the spatial distribution of multiple autonomous vehicles. We study the similarity distribution between each team’s driving log data to determine the impact of competitive autonomous driving on the traffic environment. Our fully autonomous software architecture, proven successful in winning urban autonomous multi-vehicle races in South Korea, is ready for deployment on urban robot taxis. Our traffic analysis addresses multi-agent scenarios and resolves competitive conflicts among robot taxi companies, crucial for smart city integration and optimizing autonomous vehicle performance in complex urban settings.