7 citations · 7 across the 4 of their papers we have counts for
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cs.RO2026
Online World Modeling Enables Real-World Inverse Reinforcement Learning from Observation
Tyler Han, Bat Nemekhbold, Siyang Shen +6
Current methods in robot learning are fundamentally bottlenecked by one or more of: hand-designed rewards, simulation modeling, or action supervision (e.g. teleoperation) each requ…
cs.RO2023
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…
cs.RO2023★ 7 cited
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…