7 citations · 7 across the 6 of their papers we have counts for
6 papers
Dynamic Non-Prehensile Object Transport via Model-Predictive Reinforcement Learning
Neel Jawale, Byron Boots, Balakumar Sundaralingam +1
We investigate the problem of teaching a robot manipulator to perform dynamic non-prehensile object transport, also known as the `robot waiter' task, from a limited set of real-wor…
Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL
Andrew Wagenmaker, Kevin Huang, Liyiming Ke +3
In order to mitigate the sample complexity of real-world reinforcement learning, common practice is to first train a policy in a simulator where samples are cheap, and then deploy…
Dynamics Models in the Aggressive Off-Road Driving Regime
Tyler Han, Sidharth Talia, Rohan Panicker +3
Current developments in autonomous off-road driving are steadily increasing performance through higher speeds and more challenging, unstructured environments. However, this operati…
Model Predictive Control for Aggressive Driving Over Uneven Terrain
Tyler Han, Alex Liu, Anqi Li +3
Terrain traversability in unstructured off-road autonomy has traditionally relied on semantic classification, resource-intensive dynamics models, or purely geometry-based methods t…
Deep Model Predictive Optimization
Jacob Sacks, Rwik Rana, Kevin Huang +3
A major challenge in robotics is to design robust policies which enable complex and agile behaviors in the real world. On one end of the spectrum, we have model-free reinforcement…
DATT: Deep Adaptive Trajectory Tracking for Quadrotor Control
Kevin Huang, Rwik Rana, Alexander Spitzer +2
Precise arbitrary trajectory tracking for quadrotors is challenging due to unknown nonlinear dynamics, trajectory infeasibility, and actuation limits. To tackle these challenges, w…