6 accepted papers
GUI grounding, which maps natural-language instructions to actionable UI elements, is a core capability of GUI agents. Prior work largely treats instructions as a static proxy for user intent, overlooking the impact of instruction diversity on grounding performance. Through a careful investigation o…
In motion planning with partial observability, addressing uncertainty is crucial for preventing collisions and congestion, especially in the vicinity of constrained narrow areas connecting wider spaces, called hallways. In this work, we propose a cooperative motion planning algorithm that leverages
Powered by extensive curated training data, the Segment Anything Model (SAM) demonstrates impressive generalization capabilities in open-world scenarios, effectively guided by user-provided prompts. However, the class-agnostic characteristic of SAM renders its segmentation accuracy highly dependent…
We propose a continuous gathering scheme based on the swept volume to address the challenges involved in planning a tethered robot duo to efficiently collect marine debris. Specifically, we model the tethered robot duo by constructing a double-layer U-shape, and then apply an object-aware optimizati
Reasoning over the Temporal Knowledge Graph (TKG) that predicts facts in the future has received much attention. Most previous works attempt to model temporal dynamics with knowledge graphs and graph convolution networks. However, these methods lack the consideration of high-order interactions betwe…
State-of-the-art multi-agent collision avoidance algorithms face limitations when applied to cluttered public environments, where obstacles may have a variety of shapes and structures. The issue arises because most of these algorithms are agent-level methods. They concentrate solely on preventing co…