206 citations · 340 across the 26 of their papers we have counts for
4 papers · 1 filter
Bayesian Optimization Using Domain Knowledge on the ATRIAS Biped
Akshara Rai, Rika Antonova, Seungmoon Song +3
Controllers in robotics often consist of expert-designed heuristics, which can be hard to tune in higher dimensions. It is typical to use simulation to learn these parameters, but…
Deep Kernels for Optimizing Locomotion Controllers
Rika Antonova, Akshara Rai, Christopher G. Atkeson
Sample efficiency is important when optimizing parameters of locomotion controllers, since hardware experiments are time consuming and expensive. Bayesian Optimization, a sample-ef…
Unlocking the Potential of Simulators: Design with RL in Mind
Rika Antonova, Silvia Cruciani
Using Reinforcement Learning (RL) in simulation to construct policies useful in real life is challenging. This is often attributed to the sequential decision making aspect: inaccur…
Reinforcement Learning for Pivoting Task
Rika Antonova, Silvia Cruciani, Christian Smith +1
In this work we propose an approach to learn a robust policy for solving the pivoting task. Recently, several model-free continuous control algorithms were shown to learn successfu…