2 citations · 8 across the 27 of their papers we have counts for
6 papers · 1 filter
Grasp Diffusion Network: Learning Grasp Generators from Partial Point Clouds with Diffusion Models in SO(3)xR3
Joao Carvalho, An T. Le, Philipp Jahr +4
Grasping objects successfully from a single-view camera is crucial in many robot manipulation tasks. An approach to solve this problem is to leverage simulation to create large dat…
Motion Planning Diffusion: Learning and Adapting Robot Motion Planning with Diffusion Models
J. Carvalho, A. Le, P. Kicki +2
The performance of optimization-based robot motion planning algorithms is highly dependent on the initial solutions, commonly obtained by running a sampling-based planner to obtain…
Global Tensor Motion Planning
An T. Le, Kay Hansel, João Carvalho +5
Batch planning is increasingly necessary to quickly produce diverse and quality motion plans for downstream learning applications, such as distillation and imitation learning. This…
Machine Learning with Physics Knowledge for Prediction: A Survey
Joe Watson, Chen Song, Oliver Weeger +12
This survey examines the broad suite of methods and models for combining machine learning with physics knowledge for prediction and forecast, with a focus on partial differential e…
Dude: Dual Distribution-Aware Context Prompt Learning For Large Vision-Language Model
Duy M. H. Nguyen, An T. Le, Trung Q. Nguyen +7
Prompt learning methods are gaining increasing attention due to their ability to customize large vision-language models to new domains using pre-trained contextual knowledge and mi…
Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks
Duy M. H. Nguyen, Nina Lukashina, Tai Nguyen +7
A molecule's 2D representation consists of its atoms, their attributes, and the molecule's covalent bonds. A 3D (geometric) representation of a molecule is called a conformer and c…