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Found 8 papers, 2 papers with code
Symbolic Regression on Sparse and Noisy Data with Gaussian Processes
To overcome this, we combine Gaussian process regression with a sparse identification of nonlinear dynamics (SINDy) method to denoise the data and identify nonlinear dynamical equations.
Learning Contact-based Navigation in Crowds
This paradigm poses two problems: 1) freezing while navigating a crowd may cause people to trip and fall over the robot, resulting in more harm than the collision itself, and 2) in very dense social environments where collisions are unavoidable, such a control scheme would render the robot unable to move and preclude the opportunity to study how humans incorporate robots into these environments.
Sample Efficient Dynamics Learning for Symmetrical Legged Robots:Leveraging Physics Invariance and Geometric Symmetries
Model generalization of the underlying dynamics is critical for achieving data efficiency when learning for robot control.
Learning to Walk by Steering: Perceptive Quadrupedal Locomotion in Dynamic Environments
We present a hierarchical learning framework, named PRELUDE, which decomposes the problem of perceptive locomotion into high-level decision-making to predict navigation commands and low-level gait generation to realize the target commands.
Nested Mixture of Experts: Cooperative and Competitive Learning of Hybrid Dynamical System
As an alternative, gray-box modeling leverages prior knowledge in neural network training but only for simple systems.
Gaussian-Process-based Robot Learning from Demonstration
Endowed with higher levels of autonomy, robots are required to perform increasingly complex manipulation tasks.
Robotics
Solving Service Robot Tasks: UT Austin Villa@Home 2019 Team Report
RoboCup@Home is an international robotics competition based on domestic tasks requiring autonomous capabilities pertaining to a large variety of AI technologies.
Robust and Adaptive Door Operation with a Mobile Robot
In addition, we propose a versatile Bayesian framework that endows the robot with the ability to infer the door kinematic model from observations of its motion and learn from previous experiences or human demonstrations.
Robotics
