This paper presents a novel framework for modeling and conditional generation of 3D articulated objects. Troubled by flexibility-quality tradeoffs, existing methods are often limited to using predefined structures or retrieving shapes from static datasets. To address these challenges, we parameterize an articulated object as a tree of tokens and employ a transformer to generate both the object’s high-level geometry code and its kinematic relations. Subsequently, each sub-part’s geometry is further decoded using a signed-distance-function (SDF) shape prior, facilitating the synthesis of high-quality 3D shapes. Our approach enables the generation of diverse objects with high-quality geometry and varying number of parts. Comprehensive experiments on conditional generation from text descriptions demonstrate the effectiveness and flexibility of our method.

Research in visual perception has shown that the human visual system utilizes high-level feedback information to guide lower-level processing, enabling adaptation to signals of varying characteristics. Inspired by this, we propose the Feedback multi-Level feature Extractor (Flex) to dynamically adjust feature selection in object detection based on image-wise and instance-level feedback information. This is particularly beneficial for applications such as aerial object detection, UAV-based target recognition and autonomous vehicle navigation, where global image quality issues like sensor degradation, foggy, or rainy conditions can impact detection performance. Flex adapts to variations in image quality, refining the feature extraction process to improve robustness against these challenges. Experimental results demonstrate that Flex consistently enhances a range of state-of-the-art methods on challenging aerial object detection datasets, including DOTA-v1.0, DOTA-v1.5, and HRSC2016. Furthermore, additional experiments on MS COCO confirm the module's effectiveness in general object detection tasks. Our quantitative and qualitative analyses reveal that the improvements are strongly correlated with image quality, aligning with our original motivation to address global image quality issues in real-world scenarios.

This paper tackles the challenging task of maintaining formation among multiple unmanned aerial vehicles (UAVs) while avoiding both static and dynamic obstacles during directed flight. The complexity of the task arises from its multi-objective nature, the large exploration space, and the sim-to-real gap. To address these challenges, we propose a two-stage reinforcement learning (RL) pipeline. In the first stage, we randomly search for a reward function that balances key objectives: directed flight, obstacle avoidance, formation maintenance, and zero-shot policy deployment. The second stage applies this reward function to more complex scenarios and utilizes curriculum learning to accelerate policy training. Additionally, we incorporate an attention-based observation encoder to improve formation maintenance and adaptability to varying obstacle densities. Experimental results in both simulation and real-world environments demonstrate that our method outperforms both planning-based and RL-based baselines in terms of collision-free rates and formation maintenance across static, dynamic, and mixed obstacle scenarios. Ablation studies further confirm the effectiveness of our curriculum learning strategy and attention-based encoder. Animated demonstrations are available at: https://sites.google.com/view/uav-formation-with-avoidance/.

Despite the emerging progress of integrating evolutionary computation into reinforcement learning, the absence of a high-performance platform endowing composability and massive parallelism causes nontrivial difficulties for research and applications related to asynchronous commercial games. Here, we introduce Lamarckian1—an open-source platform featuring support for evolutionary reinforcement learning scalable to distributed computing resources. To improve the training speed and data efficiency, Lamarckian adopts optimized communication methods and an asynchronous evolutionary reinforcement learning workflow. To meet the demand for an asynchronous interface by commercial games and various methods, Lamarckian tailors an asynchronous Markov Decision Process interface and designs an object-oriented software architecture with decoupled modules. In comparison with the state-of-the-art RLlib, we empirically demonstrate the unique advantages of Lamarckian on benchmark tests with up to 6000 CPU cores: 1) both the sampling efficiency and training speed are doubled when running proximal policy optimization (PPO) on Google football game; 2) the training speed is 13 times faster when running PBT+PPO onPonggame. Moreover, we also present two use cases: 1) how Lamarckian is applied to generating behavior-diverse game AI; 2) how Lamarckian is applied to game balancing tests for an asynchronous commercial game.