9 accepted papers
Collision detection is critical for safe physical human-robot interaction (pHRI). The generalized momentum observer (GMO) is a prevalent sensorless method, estimating momentum deviations to detect collisions. However, its reliance on iterative solutions and static thresholds severely limits operatio
While 3D Gaussian Splatting has recently demonstrated impressive results in scene-level reconstruction, it lacks the ability to reconstruct and track individual objects with accurate poses. This limitation hinders its application in downstream tasks such as augmented reality and embodied AI, where o
Visual servoing techniques guide robotic motion using visual information to accomplish manipulation tasks, requiring high precision and robustness against noise. Traditional methods often require prior knowledge and are susceptible to external disturbances. Learning-driven alternatives, while promis
Text-to-image (T2I) models can be maliciously used to generate harmful content such as sexually explicit, unfaithful, and misleading or Not-Safe-for-Work (NSFW) images. Previous attacks largely depend on the availability of the diffusion model or involve a lengthy optimization process. In this work,…
We introduce LongTableBench , a benchmark for evaluating long-context reasoning over semi-structured tables across diverse formats, tasks, and domains. It comprises 5,950 QA instances spanning 7 table formats (e.g., Markdown, HTML, SQL), 18 domains, and input lengths up to 128K tokens, including mul
With the rapid advancement of Large Language Models (LLMs), there is an increasing need for challenging benchmarks to evaluate their capabilities in handling complex tabular data. However, existing benchmarks are either based on outdated data setups or focus solely on simple, flat table structures.…
To migrate the remarkable successes of Large Language Models (LLMs), the community has made numerous efforts to generalize them to the table reasoning tasks for the widely deployed tabular data. Despite that, in this work, by showing a probing experiment on our proposed StructQA benchmark, we postul…
Traditional imitation learning methods typically rely on high-quality expert demonstrations and exhibit poor generalization when deployed in unfamiliar environments. A key limitation is their inability to effectively quantify and utilize epistemic uncertainty in the decision-making process. To addre
Deep neural networks have been shown to be very powerful tools for object detection in various scenes. Their remarkable performance, however, heavily depends on the availability of a large number of high quality labeled data, which are time-consuming and costly to acquire for scenes with densely pac…