Search-and-rescue (SAR) operations in disaster environments require drone swarms to coordinate efficiently despite incomplete information and potential communication failures. Existing stigmergy-based approaches provide low-bandwidth coordination but rely on fixed rules, while multi-agent reinforcement learning (MARL) offers adaptive behavior but struggles with scalability and coordination. This paper proposes a novel approach combining directional pheromone gradient observations with decentralized MARL for swarm drone SAR missions.

Abstract As industries increasingly move towards automation, the ability to visually localize objects and accurately estimate their pose, including rigid and non-rigid transformations, becomes critical. Conventional pose estimation methods are...

The design of robotic binocular camera systems has been inspired by human vision, as has their use in humanoid robots. One aspect of this inspiration has yet to play a role in robotics: convergent binocular stereo, where the cameras are angled inward to overlap their fields of view for improved depth perception.

Introduces a full-stack co-designed tactile-reactive policy with asynchronous mixture-of-transformers (slow visuomotor planning + fast 4× tactile corrections), trained on the largest open 50-hour / 5,500-episode synchronized robot tactile play dataset collected on 22-DoF hands; bridges egocentric video pretraining to contact-rich manipulation via EgoScale mid-training.

Senna: Bridging Large Vision-Language Models and End-to-End Autonomous Driving

This study develops a binocular stereo vision system to enhance the operational safety of agricultural unmanned aerial vehicles (UAVs) by detecting and localizing thin cables—one of the most prevalent hazards in low-altitude flight.

Deformable linear objects (DLOs) exhibit highly nonlinear dynamic behavior, complicating their control during high-speed maneuvers. Furthermore, the lack of a generic spatial representation and the difficulty of real-time state estimation...

Abstract Embodied agents must continuously adapt to the physical world using interaction data collected across varying timescales, controllers, and environmental conditions. However, standard reinforcement learning assumes stationary dynamics and on-policy data collection, which breaks down when agents must learn from heterogeneous data sources with non-stationary dynamics and off-policy behavior.

Objective: In the field of mobile robotics, autonomous navigation in dynamic environments is one of the most challenging tasks in these environments: traditional methods based on pre-mapping and geometric planning...

Generalist robots need to perform diverse tasks while operating in dynamic, uncertain, and unstructured environments, often around human beings. Vision-language-action (VLA) models have recently emerged as a promising and flexible approach to enabling generalist robot behavior. This is a systematic literature review of VLA models for generalist robots.