6 accepted papers
Grasp pose detection (GPD) is a fundamental capability for robotic autonomy, but its reliance on large, diverse datasets creates significant data privacy and centralization challenges. Federated Learning (FL) offers a privacy-preserving solution, but its application to GPD is hindered by the substan
Grasp pose detection (GPD) is a fundamental capability for robotic autonomy, but its reliance on large, diverse datasets creates significant data privacy and centralization challenges. Federated Learning (FL) offers a privacy-preserving solution, but its application to GPD is hindered by the substan…
Autonomous vehicles employ semantic segmentation as a foundational component for perception and scene understanding, upon which driving decisions can be informed. Despite their performance, these deep learning models remain susceptible to subtle input perturbations that can cause severe deviation in…
In this paper, we present an adaptive walker system designed to address limitations in current intelligent walker technologies. While recent advancements have been made in this field, existing systems often struggle to seamlessly interpret user intent for speed control and lack adaptability across d
Autonomous vehicles employ semantic segmentation as a foundational component for perception and scene understanding, upon which driving decisions can be informed. Despite their performance, these deep learning models remain susceptible to subtle input perturbations that can cause severe deviation in
In the field of robot manipulation, learnable task planners are gaining attention, especially for long-horizon tasks such as cooking. However, existing methods that predominantly rely on symbolic representations suffer from limitations in generalization capabilities, particularly in handling unseen