269 citations · 2k across the 61 of their papers we have counts for
4 papers · 1 filter
Simulator Predictive Control: Using Learned Task Representations and MPC for Zero-Shot Generalization and Sequencing
Zhanpeng He, Ryan Julian, Eric Heiden +5
Simulation-to-real transfer is an important strategy for making reinforcement learning practical with real robots. Successful sim-to-real transfer systems have difficulty producing…
Scaling simulation-to-real transfer by learning composable robot skills
Ryan Julian, Eric Heiden, Zhanpeng He +5
We present a novel solution to the problem of simulation-to-real transfer, which builds on recent advances in robot skill decomposition. Rather than focusing on minimizing the simu…
Learning Task-Specific Dynamics to Improve Whole-Body Control
Andrej Gams, Sean A. Mason, Aleš Ude +2
In task-based inverse dynamics control, reference accelerations used to follow a desired plan can be broken down into feedforward and feedback trajectories. The feedback term accou…
Probabilistic Recurrent State-Space Models
Andreas Doerr, Christian Daniel, Martin Schiegg +4
State-space models (SSMs) are a highly expressive model class for learning patterns in time series data and for system identification. Deterministic versions of SSMs (e.g. LSTMs) p…