8 accepted papers
The sparse unordered structure of point clouds causes unnecessary computation and energy consumption in deep models. Conventionally, the Transformer architecture is leveraged to model global relationships in point clouds, however, its quadratic complexity restricts scalability. Although the Mamba a…
In goal-oriented dialogue tasks, the main challenge is to steer the interaction towards a given goal within a limited number of turns. Existing approaches either rely on elaborate prompt engineering, whose effectiveness is heavily dependent on human experience, or integrate policy networks and pre-t
Vision-language-action models (VLAs) use an end-to-end learning architecture, which can realize the integration of visual perception, semantic understanding and motion control. However, when tackling with the dynamic or long-horizon tasks, VLAs have poor robustness and real-time adjustment ability a
The inherent sparsity, lack of structure, and rotation sensitivity of point clouds often lead to high computational and parameter cost in robust feature learning. To address these problems, we present QPoint, a lightweight framework that leverages robust quaternion feature learning. QPoint incorpora…
Mobile Manipulation (MM) involves long-horizon decision-making over multi-stage compositions of heterogeneous skills, such as navigation and picking up objects. Despite recent progress, existing MM methods still face two key limitations: (i) low sample efficiency, due to ineffective use of redundant
The unordered nature of point cloud data poses significant challenges to conventional architectures primarily designed for structured data. Spiking neural networks (SNN), by virtue of their inherent sparsity and dynamics, are particularly well-suited for processing point clouds to effectively extrac…
In this letter, we propose a new method for visual locomotion controller in quadruped robots, aimed at enhancing their capability to traverse challenging terrain. Our approach integrates computer vision techniques with robust locomotion control to improve terrain traversal. To facilitate terrain per
Disturbances widely exist in all control systems. Disturbance observers are commonly employed to estimate the disturbances which are often fully compensated in the control signal. However, one rarely recognized fact is that disturbances may be beneficial to the control performances and thus can be e