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Found 9 papers, 2 papers with code
Advancing Humanoid Locomotion: Mastering Challenging Terrains with Denoising World Model Learning
In this work, we introduce Denoising World Model Learning (DWL), an end-to-end reinforcement learning framework for humanoid locomotion control, which demonstrates the world's first humanoid robot to master real-world challenging terrains such as snowy and inclined land in the wild, up and down stairs, and extremely uneven terrains.
RCDM: Enabling Robustness for Conditional Diffusion Model
The conditional diffusion model (CDM) enhances the standard diffusion model by providing more control, improving the quality and relevance of the outputs, and making the model adaptable to a wider range of complex tasks.
Multi-Objective Sizing Optimization Method of Microgrid Considering Cost and Carbon Emissions
Microgrid serves as a promising solution to integrate and manage distributed renewable energy resources.
Dynamic Virtual Power Plants With Frequency Regulation Capacity
For integrating heterogeneous distributed energy resources to provide fast frequency regulation, this paper proposes a dynamic virtual power plant~(DVPP) with frequency regulation capacity.
Feature Re-Embedding: Towards Foundation Model-Level Performance in Computational Pathology
Unlike existing works that focus on pre-training powerful feature extractor or designing sophisticated instance aggregator, R$^2$T is tailored to re-embed instance features online.
Incorporating Domain Knowledge through Task Augmentation for Front-End JavaScript Code Generation
However, in some domain-specific scenarios, building such a large paired corpus for code generation is difficult because there is no directly available pairing data, and a lot of effort is required to manually write the code descriptions to construct a high-quality training dataset.
A Contact-Safe Reinforcement Learning Framework for Contact-Rich Robot Manipulation
In this paper, we propose a contact-safe reinforcement learning framework for contact-rich robot manipulation, which maintains safety in both the task space and joint space.
Super-Resolving Commercial Satellite Imagery Using Realistic Training Data
We propose a realistic training data generation model for commercial satellite imagery products, which includes not only the imaging process on satellites but also the post-process on the ground.
Automatic Parameter Selection for Denoising Algorithms Using a No-Reference Measure of Image Content
Across the field of inverse problems in image and video processing, nearly all algorithms have various parameters which need to be set in order to yield good results.
