6 accepted papers
In this paper, we propose a training-free framework for vision-and-language navigation (VLN). Existing zero-shot VLN methods are mainly designed for discrete environments or involve unsupervised training in continuous simulator environments, which makes it challenging to generalize and deploy them i…
In complex multi-robot application scenarios, particularly in dynamically adversarial, hazardous, or disaster environments, traditional cooperation paradigms face significant challenges due to unreliable or absent communication links. Achieving efficient cooperation in the absence of communication h
Wind disturbance is a critical challenge for uninhabited aerial vehicles (UAVs), particularly for hybrid vertical takeoff and landing (VTOL) UAVs such as lifting-wing quadcopters, which are prone to aerodynamic disturbances like gusts and turbulence due to their wing structure. Existing control stra
Many real-world bandit applications are characterized by sparse rewards, which can significantly hinder learning efficiency. Leveraging problem-specific structures for careful distribution modeling is recognized as essential for improving estimation efficiency in statistics. However, this approach r…
Automatic landing of tethered unmanned aerial vehicles (UAVs) is an important issue. Typically, UAVs rely on location sensors such as global navigation satellite system (GNSS) and external cameras to obtain location data. However, harsh environments such as denial GNSS or strong winds make it diffic…
Despite the great interest in the bandit problem, designing efficient algorithms for complex models remains challenging, as there is typically no analytical way to quantify uncertainty. In this paper, we propose Multiplier Bootstrap-based Exploration (MBE), a novel exploration strategy that is appli…