DynCur-Geo introduces a dynamic curiosity framework for active geo‑localization, adjusting the intrinsic reward based on the remaining distance to a target. A distance‑aware gate promotes early exploration and transitions the policy toward goal‑directed behavior as the UAV approaches the target, while potential‑based reward shaping provides dense progress guidance. Experiments in multimodal, cross‑scene, disaster‑affected, and long‑range scenarios demonstrate consistent performance gains over existing active geo‑localization baselines.
arXiv:2606. 14879v1 Announce Type: cross Abstract: Mobile agents require efficient exploration strategies to map unseen environments and autonomously plan tasks.
arXiv:2608. 09564v1 Announce Type: cross Abstract: UAV vision-language navigation (UAV-VLN) focuses on enabling an aerial agent to follow natural-language instructions in open 3D environments from egocentric visual observations.
arXiv:2609.08442v1 Announce Type: cross Abstract: Aerial Vision-and-Language Navigation requires drones to follow natural-language instructions and navigate through complex urban environments. Accura...
The paper introduces Active Cross-View Object Geo-Localization (ActiveGeo), enabling mobile agents to actively select new viewpoints and decide when to stop to improve localization with fewer observations. It proposes the ActiveMoPT framework, which uses a three-stage training process: Multi-View Prompt-Preserving Adaptation, Trajectory-Guided Policy Initialization, and Cost-Aware Policy Refinement with GRPO. The authors also create a zero-shot test set, ActiveGeo-858, and demonstrate that ActiveMoPT outperforms prior methods on MoP-UAV and ActiveGeo-858.
The paper proposes a reward-based policy that relies only on rewards and actions, enabling zero‑shot transfer between source and target environments with entirely different observation spaces. Experiments on Pointmass, Cartpole, 2D Car Racing, and the Stretch robot in Habitat‑Sim show that the policy can adapt to new visual styles or 3D renderings without additional samples. Additionally, the reward policy can guide the training of an observation‑based policy in the target environment.
arXiv:2609.06623v1 Announce Type: cross Abstract: Can exploratory UAV waypoint sequences be generated from multimodal onboard observations and a fixed-dimensional recurrent internal state without mai...
arXiv:2607. 29419v1 Announce Type: cross Abstract: In reinforcement learning, exploration with sparse and delayed rewards presents a significant challenge due to the limited feedback available for guiding the learning process.
Aerial image-goal navigation requires an unmanned aerial vehicle (UAV) to reach a target location specified by a goal image. Existing world-model-based methods rank candidate trajectories using predicted futures, but typically rely on only one or a few point predictions, which is inadequate for large-scale outdoor environments with substantial future-state uncertainty.
arXiv:2606. 03252v1 Announce Type: cross Abstract: Navigating a drone in unseen and cluttered environments requires reliable generalization to unseen scene layouts and understanding of environmental structure relative to the robot's capabilities.
arXiv:2608.29315v1 Announce Type: cross Abstract: This work introduces Semantically-Guided Exploration (SGE), a modular exploration framework for ground vehicles that integrates pixel-level semantic...
The paper proposes Novelty and Surprise Prioritized Experience Replay (NSPER) for image-based reinforcement learning, combining novelty to highlight underrepresented states and surprise to reveal gaps in the agent’s knowledge. An extended version, NSPER+R, also uses these signals as intrinsic rewards to enhance both replay quality and exploration. Experiments on DeepMind Control Suite tasks demonstrate that NSPER and NSPER+R accelerate training and improve convergence compared to existing methods.
The paper tackles sample efficiency in image-based reinforcement learning by combining novelty and surprise signals to prioritize experiences. It proposes Novelty and Surprise Prioritized Experience Replay (NSPER) and an extended version, NSPER+R, which also uses these signals as intrinsic rewards. Experiments on DeepMind Control Suite tasks demonstrate that both methods accelerate training and improve convergence compared to existing techniques.