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Found 14 papers, 2 papers with code
A-Scan2BIM: Assistive Scan to Building Information Modeling
This paper proposes an assistive system for architects that converts a large-scale point cloud into a standardized digital representation of a building for Building Information Modeling (BIM) applications.
Hybrid Reinforcement Learning for Optimizing Pump Sustainability in Real-World Water Distribution Networks
By leveraging operational data as a foundation for the agent's actions, we enhance the explainability of the agent's actions, foster more robust recommendations, and minimize error.
ASAP: Automated Sequence Planning for Complex Robotic Assembly with Physical Feasibility
The automated assembly of complex products requires a system that can automatically plan a physically feasible sequence of actions for assembling many parts together.
PlotMap: Automated Layout Design for Building Game Worlds
Concretely, we present a system that leverages Reinforcement Learning (RL) to automatically assign concrete locations on a game map to abstract locations mentioned in a given story (plot facilities), following spatial constraints derived from the story.
Representation Learning for Sequential Volumetric Design Tasks
We develop the preference model by estimating the density of the learned representations whereas we train an autoregressive transformer model for sequential design generation.
Learning Dense Reward with Temporal Variant Self-Supervision
Rewards play an essential role in reinforcement learning.
Building-GAN: Graph-Conditioned Architectural Volumetric Design Generation
Volumetric design is the first and critical step for professional building design, where architects not only depict the rough 3D geometry of the building but also specify the programs to form a 2D layout on each floor.
RobustPointSet: A Dataset for Benchmarking Robustness of Point Cloud Classifiers
We also find that robustness to unseen transformations cannot be brought about merely by extensive data augmentation.
A Learning Approach to Robot-Agnostic Force-Guided High Precision Assembly
In this work we propose a learning approach to high-precision robotic assembly problems.
Fusion 360 Gallery: A Dataset and Environment for Programmatic CAD Construction from Human Design Sequences
Parametric computer-aided design (CAD) is a standard paradigm used to design manufactured objects, where a 3D shape is represented as a program supported by the CAD software.
Fusion 360 Gallery: A Dataset and Environment for Programmatic CAD Reconstruction
We provide a dataset of 8, 625 designs, comprising sequential sketch and extrude modeling operations, together with a complementary environment called the Fusion 360 Gym, to assist with performing CAD reconstruction.
Dynamic Experience Replay
Our ablation studies show that Dynamic Experience Replay is a crucial ingredient that either largely shortens the training time in these challenging environments or solves the tasks that the vanilla Ape-X DDPG cannot solve.
A Learning Framework for High Precision Industrial Assembly
The framework combines both the supervised learning and the reinforcement learning.
Visual Diagnostics for Deep Reinforcement Learning Policy Development
In this paper, we present our extensions of CNN visualization algorithms to the domain of vision-based reinforcement learning.
