11 accepted papers
Vision-Language-Action (VLA) models have achieved significant breakthroughs by leveraging Large Vision Language Models (VLMs) to jointly interpret instructions and visual inputs. However, the substantial increase in visual tokens, particularly from multi-view inputs, poses serious challenges to real
In real-world scenarios, multi-view cameras are typically employed for fine-grained manipulation tasks. Existing approaches (e.g., ACT ) tend to treat multi-view features equally and directly concatenate them for policy learning. How ever, it will introduce redundant visual information and bring hig…
Object-Goal Navigation (OGN) in complex domestic environments remains challenging due to spatial memory and semantic uncertainties. To address this, we introduce EMKG, an embodied multimodal memory knowledge graph framework that enables open-world navigation. In contrast to conventional vision-langu
Video data provides a rich source beyond expensive action-labeled data for advancing robot learning. Recent approaches have demonstrated promising potential in leveraging video data by learning latent actions for policy training. The latent action tokenizer encodes latent actions between successive
In real-world scenarios, multi-view cameras are typically employed for fine-grained manipulation tasks. Existing approaches (e.g., ACT [1]) tend to treat multi-view features equally and directly concatenate them for policy learning. However, it will introduce redundant visual information and bring h
Autonomous driving progress relies on large-scale annotated datasets. In this work, we explore the potential of generative models to produce vast quantities of freely-labeled data for autonomous driving applications and present SubjectDrive, the first model proven to scale generative data production…
Long-term temporal fusion is a crucial but often overlooked technique in camera-based Bird's-Eye-View (BEV) 3D perception. Existing methods are mostly in a parallel manner. While parallel fusion can benefit from long-term information, it suffers from increasing computational and memory overheads as
"This paper introduces the Stream Query Denoising (SQD) strategy, a novel and general approach for high-definition map (HD-map) construction. SQD is designed to improve the modeling capability of map elements by learning temporal consistency. Specifically, SQD involves the process of denoising the q…
Many point-based 3D detectors adopt point-feature sampling strategies to drop some points for efficient inference. These strategies are typically based on fixed and handcrafted rules, making it difficult to handle complicated scenes. Different from them, we propose a Dynamic Ball Query (DBQ) network…
"To date, the most powerful semi-supervised object detectors (SS-OD) are based on pseudo-boxes, which need a sequence of post-processing with fine-tuned hyper-parameters. In this work, we propose replacing the sparse pseudo-boxes with the dense prediction as a united and straightforward form of pseu…