12 accepted papers
Due to the heterogeneity of faces and edges in B-rep, conventional graph-based representations is incapable of establishing a unified formulation for faces and edges, thereby constraining the capabilities of B-rep generative models. We propose a B-rep Variational Graph Auto Encoding (BrepVGAE), the
In industrial settings, classification of 3D CAD models are critical for efficient manufacturing. However, the limited availability of annotated CAD models presents an obstacle to achieving rapid adaptation in few-shot part classification scenarios. In this paper, we propose a hybrid graph represent
Tele-homecare has become a promising approach to meet the growing demand for elderly and disability care. In such a context, ensuring human-robot interaction safety during teleoperation poses a critical challenge. Existing teleoperation control approaches focus solely on the robot’s end-effector tra
Multimodal large language models (MLLMs) are transforming the capabilities of graphical user interface (GUI) agents, facilitating their transition from controlled simulations to complex, real-world applications across various platforms. However, the effectiveness of these agents hinges on the robust…
This study presents a safety-aware shared control strategy that combines proximity sensing and force guidance to achieve precise and stable teleoperation under dynamic interference. Based on the sensing information of the proximity sensor, a safety-aware controller is designed to enable the manipula
With the rapid technology development pushing the introduction of the fifth industrial revolution, Industry 5.0, robots are getting rid of fences and sharing the workspace with humans. In such a context, ensuring the safety of humans and robots is a critical demand. One of the effective methods for
Few-shot learning is a central problem in meta-learning, where learners must quickly adapt to new tasks given limited training data. Recently, feature pre-training has become a ubiquitous component in state-of-the-art meta-learning methods and is shown to provide significant performance improvement.…
The goal of optimization-based meta-learning is to find a single initialization shared across a distribution of tasks to speed up the process of learning new tasks. Conditional meta-learning seeks task-specific initialization to better capture complex task distributions and improve performance. Howe…
We consider the problem of imitation learning from a finite set of expert trajectories, without access to reinforcement signals. The classical approach of extracting the expert’s reward function via inverse reinforcement learning, followed by reinforcement learning is indirect and may be computation…
Robot learning by demonstration gives robots the ability to learn tasks which they have not been programmed to do before. The paradigm allows robots to work in a greater range of real-world applications in our daily life. However, this paradigm has traditionally been applied to learn tasks from a si…
Classifying human cognitive states from behavioral and physiological signals is a challenging problem with important applications in robotics. The problem is challenging due to the data variability among individual users, and sensor artefacts. In this work, we propose an end-to-end framework for rea…
Dynamic Movement Primitives (DMPs) are a generic approach for trajectory modeling in an attractor land-scape based on differential dynamical systems. DMPs guarantee stability and convergence properties of learned trajectories, and scale well to high dimensional data. In this paper, we propose DMP+,…