254 citations · 612 across the 14 of their papers we have counts for
3 papers · 1 filter
Joint Space Control via Deep Reinforcement Learning
Visak Kumar, David Hoeller, Balakumar Sundaralingam +2
The dominant way to control a robot manipulator uses hand-crafted differential equations leveraging some form of inverse kinematics / dynamics. We propose a simple, versatile joint…
Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion
Xingye Da, Zhaoming Xie, David Hoeller +5
We present a hierarchical framework that combines model-based control and reinforcement learning (RL) to synthesize robust controllers for a quadruped (the Unitree Laikago). The sy…
Practical Reinforcement Learning For MPC: Learning from sparse objectives in under an hour on a real robot
Napat Karnchanachari, Miguel I. Valls, David Hoeller +1
Model Predictive Control (MPC) is a powerful control technique that handles constraints, takes the system's dynamics into account, and optimizes for a given cost function. In pract…