5 accepted papers
Coordinated navigation of an arbitrary number of robots to an arbitrary number of goals is a big challenge in robotics, often hindered by scalability limitations of existing strategies. This letter introduces a decentralized multi-agent control system using neural network policies trained in simulat
In this work, we extend the model predictive control methods developed in the legged robotics literature to collaborative loco-manipulation settings. The systems we study entail a payload collectively carried by multiple quadruped robots equipped with a mechanical arm. We use a direct multiple shoot…
We present Ungar, an open-source library to aid the implementation of high-dimensional optimal control problems (OCPs). We adopt modern template metaprogramming techniques to enable the compile-time modeling of complex systems while retaining maximum runtime efficiency. Our framework provides syntac…
This work presents a motion capture-driven locomotion controller for quadrupedal robots that replicates the non-periodic footsteps and subtle body movement of animal motions. We adopt a nonlinear model predictive control (NMPC) formulation that generates optimal base trajectories and stepping locati…
We present a control-aware design optimization method for quadrupedal robots. In particular, we show that it is possible to analytically differentiate typical, inverse dynamics-based whole body controllers with respect to design parameters, and that gradient-based methods can be used to efficiently…