2 citations · 2 across the 2 of their papers we have counts for
3 papers
A Differentiable Newton-Euler Algorithm for Real-World Robotics
Michael Lutter, Johannes Silberbauer, Joe Watson +1
Obtaining dynamics models is essential for robotics to achieve accurate model-based controllers and simulators for planning. The dynamics models are typically obtained using model…
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…
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…