82 citations · 105 across the 8 of their papers we have counts for
3 papers · 1 filter
Differentiable Physics Models for Real-world Offline Model-based Reinforcement Learning
Michael Lutter, Johannes Silberbauer, Joe Watson +1
A limitation of model-based reinforcement learning (MBRL) is the exploitation of errors in the learned models. Black-box models can fit complex dynamics with high fidelity, but the…
High Acceleration Reinforcement Learning for Real-World Juggling with Binary Rewards
Kai Ploeger, Michael Lutter, Jan Peters
Robots that can learn in the physical world will be important to en-able robots to escape their stiff and pre-programmed movements. For dynamic high-acceleration tasks, such as jug…
A Differentiable Newton Euler Algorithm for Multi-body Model Learning
Michael Lutter, Johannes Silberbauer, Joe Watson +1
In this work, we examine a spectrum of hybrid model for the domain of multi-body robot dynamics. We motivate a computation graph architecture that embodies the Newton Euler equatio…