6 papers
3D CAVLA: Leveraging Depth and 3D Context to Generalize Vision Language Action Models for Unseen Tasks
Vineet Bhat, Yu-Hsiang Lan, Prashanth Krishnamurthy +2
Robotic manipulation in 3D requires effective computation of N degree-of-freedom joint-space trajectories that enable precise and robust control. To achieve this, robots must integ…
LoongFlow: Directed Evolutionary Search via a Cognitive Plan-Execute-Summarize Paradigm
Chunhui Wan, Xunan Dai, Zhuo Wang +5
The transition from static Large Language Models (LLMs) to self-improving agents is hindered by the lack of structured reasoning in traditional evolutionary approaches. Existing me…
VFMF: World Modeling by Forecasting Vision Foundation Model Features
Gabrijel Boduljak, Yushi Lan, Christian Rupprecht +1
Forecasting from partial observations is central to world modeling. Many recent methods represent the world through images, and reduce forecasting to stochastic video generation. A…
OmniVGGT: Omni-Modality Driven Visual Geometry Grounded Transformer
Haosong Peng, Hao Li, Yalun Dai +8
General 3D foundation models have started to lead the trend of unifying diverse vision tasks, yet most assume RGB-only inputs and ignore readily available geometric cues (e.g., cam…
IGGT: Instance-Grounded Geometry Transformer for Semantic 3D Reconstruction
Hao Li, Zhengyu Zou, Fangfu Liu +8
Humans naturally perceive the geometric structure and semantic content of a 3D world as intertwined dimensions, enabling coherent and accurate understanding of complex scenes. Howe…
Gateformer: Advancing Multivariate Time Series Forecasting through Temporal and Variate-Wise Attention with Gated Representations
Yu-Hsiang Lan, Eric K. Oermann
There has been a recent surge of interest in time series modeling using the Transformer architecture. However, forecasting multivariate time series with Transformer presents a uniq…