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Found 7 papers, 4 papers with code
Hindsight States: Blending Sim and Real Task Elements for Efficient Reinforcement Learning
Reinforcement learning has shown great potential in solving complex tasks when large amounts of data can be generated with little effort.
normflows: A PyTorch Package for Normalizing Flows
It allows to build normalizing flow models from a suite of base distributions, flow layers, and neural networks.
Learning Event-triggered Control from Data through Joint Optimization
We present a framework for model-free learning of event-triggered control strategies.
TriFinger: An Open-Source Robot for Learning Dexterity
Dexterous object manipulation remains an open problem in robotics, despite the rapid progress in machine learning during the past decade.
Learning to Play Table Tennis From Scratch using Muscular Robots
This work is the first to (a) fail-safe learn of a safety-critical dynamic task using anthropomorphic robot arms, (b) learn a precision-demanding problem with a PAM-driven system despite the control challenges and (c) train robots to play table tennis without real balls.
Safe and Fast Tracking on a Robot Manipulator: Robust MPC and Neural Network Control
Fast feedback control and safety guarantees are essential in modern robotics.
An Open Torque-Controlled Modular Robot Architecture for Legged Locomotion Research
Finally, to demonstrate the capabilities of the quadruped, we present a novel controller which combines feedforward contact forces computed from a kino-dynamic optimizer with impedance control of the center of mass and base orientation.
Robotics
