13 accepted papers
Open-vocabulary mobile manipulation (OVMM) requires long-horizon navigation in unseen environments and object-centric manipulation. Most existing methods treat navigation and manipulation as separate stages, which can yield navigation endpoints that are poor for manipulation or manipulation-friendly…
Understanding the geometric and semantic structure of environments is essential for embodied agents. Existing semantic mapping methods trade off between explicit geometry and multi-scale semantics,and lack a native interface for large models, thus requiring additional training of feature projection
Existing zero-shot Object Goal Navigation (ObjectNav) methods often exploit commonsense knowledge from large language or vision-language models to guide navigation. However, such knowledge arises from internet-scale text rather than embodied 3D experience, and episodic observations collected during
Object goal navigation requires an agent to navigate to a specified target in unseen environments without an explicit map, which demands an understanding of object-scene context to infer the target's location based on partial observations. The function of an object plays a crucial role in its catego…
Object navigation tasks require an agent to locate a target object using visual observations in unseen environments, where unfamiliar layouts and novel object appearances can hinder navigation. Most existing methods lack the adaptability needed to handle these uncertainties, as their navigation mode…
Visual navigation is to let the agent reach the target according to the continuous visual input. In most previous works visual navigation is usually assumed to be done in a static and ideal environment: the target is always reachable with no need to alter the environment. However the "messy" environ…
The Object Goal navigation (ObjectNav) task requires the agent to navigate to a specified target in an unseen environment. Since the environment layout is unknown the agent needs to infer the unknown contextual objects from partially observations thereby deducing the likely location of the target. P…
Current metrics for text-to-image models typically rely on statistical metrics which inadequately represent the real preference of humans. Although recent work attempts to learn these preferences via human annotated images they reduce the rich tapestry of human preference to a single overall score.…
Object goal navigation requires an agent to navigate to a specified object in an unseen environment based on visual observations and user-specified goals. Human decision-making in navigation is sequential, planning a most likely sequence of actions toward the goal. However, existing ObjectNav meth…
Previous works for ObjectNav task attempt to learn the association (e.g. relation graph) between the visual inputs and the goal during training. Such association contains the prior knowledge of navigating in training environments, which is denoted as the experience. The experience performs a positiv…
The goal of visual object navigation for an agent is to find the target objects accurately. Recent works mainly focus on the feature of embedding, attempting to learn better features with different variants, such as object distribution and graph representations. However, some typical navigation prob…
"Object navigation is a task to let the agent navigate to a target object. Prevailing works attempt to expand navigation ability in new environments and achieve reasonable performance on the seen object categories that have been observed in training environments. However, this setting is somewhat li…
The goal of object navigation is to reach the expected objects according to visual information in the unseen environments. Previous works usually implement deep models to train an agent to predict actions in real-time. However, in the unseen environment, when the target object is not in egocentric v…