8 accepted papers
In real-world applications, a reinforcement learning (RL) agent should consider multiple objectives and adhere to safety guidelines. To address these considerations, we propose a constrained multi-objective RL algorithm named constrained multi-objective gradient aggregator (CoMOGA). In the field of…
Transformer-based sequence models have proven effective in offline reinforcement learning for modeling agent trajectories using large-scale datasets. However, applying these models directly to multi-agent offline reinforcement learning introduces additional challenges, especially in managing complex
We propose a new calibration method for survival models based on the Kolmogorov–Smirnov (KS) metric. Existing approaches—including conformal prediction, D-calibration, and Kaplan–Meier (KM)-based methods—often rely on heuristic binning or additional nonparametric estimators, which undermine their ad…
Object goal navigation (ObjectNav) is the task of finding a target object in an unseen environment. It is one of the fundamental challenges in visual navigation as it requires both structural and semantic understanding. In this paper, we present OVG-Nav, a novel ObjectNav framework that leverages a
We propose a novel type of map for visual navigation, a renderable neural radiance map (RNR-Map), which is designed to contain the overall visual information of a 3D environment. The RNR-Map has a grid form and consists of latent codes at each pixel. These latent codes are embedded from image observ…
Designing a socially-aware navigation method for crowded environments has become a critical issue in robotics. In order to perform navigation in a crowded environment without causing discomfort to nearby pedestrians, it is necessary to design a global planner that is able to consider both human-robo…
A novel framework is proposed to incrementally collect landmark-based graph memory and use the collected memory for image goal navigation. Given a target image to search, an embodied robot utilizes semantic memory to find the target in an unknown environment. In this paper, we present a topological…
We present a novel graph-structured memory for visual navigation, called visual graph memory (VGM), which consists of unsupervised image representations obtained from navigation history. The proposed VGM is constructed incrementally based on the similarities among the unsupervised representations of…