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Found 7 papers, 3 papers with code
ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skills
In the second stage, we deploy the policies in the real world and collect real-world data to train a delta (residual) action model that compensates for the dynamics mismatch.
CooHOI: Learning Cooperative Human-Object Interaction with Manipulated Object Dynamics
Given the scarcity of motion capture data on multi-humanoid collaboration and the efficiency challenges associated with multi-agent learning, these tasks cannot be straightforwardly addressed using training paradigms designed for single-agent scenarios.
Seamless Integration and Implementation of Distributed Contact and Contactless Vital Sign Monitoring
In contact vital sign monitoring, the chest wall movement due to respiration and heartbeat is translated into changes in the optical output intensity of a fiber Bragg grating (FBG).
Hybrid Internal Model: Learning Agile Legged Locomotion with Simulated Robot Response
Robust locomotion control depends on accurate state estimations.
Train Once, Get a Family: State-Adaptive Balances for Offline-to-Online Reinforcement Learning
Existing solutions tackle this problem by imposing a policy constraint on the policy improvement objective in both offline and online learning.
A new parsimonious method for classifying Cancer Tissue-of-Origin Based on DNA Methylation 450K data
The biological functions in cancer development of 50 selected genes is also explored through enrichment analysis and the results show that 12 out of 16 of our top features have already been identified to be specific with cancer and we also propose some more genes to be tested for future studies.
The Computational Complexity of Fire Emblem Series and similar Tactical Role-Playing Games
Fire Emblem (FE) is a popular turn-based tactical role-playing game (TRPG) series on the Nintendo gaming consoles.
