11 accepted papers
Active recognition, a fundamental task in embodied vision, aims to improve recognition performance by dynamically adjusting viewpoints and poses to mitigate the negative impacts of occlusion and blind spots. Although existing active recognition methods possess basic viewpoint adaptation capabilities
Meta-reinforcement learning offers a mechanism for zero-shot adaptation, enabling agents to handle new tasks with parametric variation in real-world environments. However, existing methods still struggle with task-level adaptation, which demands generalization beyond simple variations within tasks,
Zero-shot adaptation is crucial for agents facing new tasks. Offline Meta-Reinforcement Learning (OMRL), utilizing offline multi-task datasets to train policies, offers a way to attain this ability. Although most OMRL methods construct task representations via contrastive learning and merge them wit…
For a general-purpose robot, it is desirable to imitate human demonstration videos that can effectively solve long-horizon tasks and perform novel ones. Recent advances in skill-based imitation learning have shown that extracting skill embedding from raw human videos is a promising paradigm to enabl…
For autonomous robots, it is desirable to learn coordination of primitive skills that can effectively solve long-horizon tasks and perform novel ones. Recent advances in hierarchical policy learning have shown that decomposing complex tasks into sequences of primitive skills which are called sketche
Domain adaptive object detection (DAOD) assumes that both labeled source data and unlabeled target data are available for training, but this assumption does not always hold in real-world scenarios. Thus, source-free DAOD is proposed to adapt the source-trained detectors to target domains with only u…
The task of zero-shot object goal visual navigation (ZSON) aims to enable robots to locate previously “unseen” objects by visual observations. This task presents a significant challenge since the robot must transfer the navigation policy learned from “seen” objects to “unseen” objects through auxili
Segmenting unseen objects is a crucial ability for the robot since it may encounter new environments during the operation. Recently, a popular solution is leveraging RGB-D features of large-scale synthetic data and directly applying the model to unseen real-world scenarios. However, the domain shift…
Object goal visual navigation is a challenging task that aims to guide a robot to find the target object based on its visual observation, and the target is limited to the classes pre-defined in the training stage. However, in real households, there may exist numerous target classes that the robot ne…
Active interaction with environments is one of the striking characteristics of robotic active vision, which allows robots to move to facilitate visual tasks. Recently, several embodied AI platforms have been proposed as the synthetic environments to study robotic active vision, without needing to in
Multispectral pedestrian detection has shown great advantages under poor illumination conditions, since the thermal modality provides complementary information for the color image. However, real multispectral data suffers from the position shift problem, i.e. the color-thermal image pairs are not st…