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10 papers · 2 filters
Fly Out The Window: Exploiting Discrete-Time Flatness for Fast Vision-Based Multirotor Flight
Melissa Greeff, Siqi Zhou, Angela P. Schoellig
Current control design for fast vision-based flight tends to rely on high-rate, high-dimensional and perfect state estimation. This is challenging in real-world environments due to…
Observability-Aware Trajectory Optimization: Theory, Viability, and State of the Art
Christopher Grebe, Emmett Wise, Jonathan Kelly
Ideally, robots should move in ways that maximize the knowledge gained about the state of both their internal system and the external operating environment. Trajectory design is a…
safe-control-gym: a Unified Benchmark Suite for Safe Learning-based Control and Reinforcement Learning in Robotics
Zhaocong Yuan, Adam W. Hall, Siqi Zhou +4
In recent years, both reinforcement learning and learning-based control -- as well as the study of their safety, which is crucial for deployment in real-world robots -- have gained…
PlaTe: Visually-Grounded Planning with Transformers in Procedural Tasks
Jiankai Sun, De-An Huang, Bo Lu +3
In this work, we study the problem of how to leverage instructional videos to facilitate the understanding of human decision-making processes, focusing on training a model with the…
Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World TriFinger
Arthur Allshire, Mayank Mittal, Varun Lodaya +7
We present a system for learning a challenging dexterous manipulation task involving moving a cube to an arbitrary 6-DoF pose with only 3-fingers trained with NVIDIA's IsaacGym sim…
Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning
Lukas Brunke, Melissa Greeff, Adam W. Hall +4
The last half-decade has seen a steep rise in the number of contributions on safe learning methods for real-world robotic deployments from both the control and reinforcement learni…