MuJoCo (Multi-Joint dynamics with Contact), Google DeepMind, London, United Kingdom

Data-driven methods have lead to advances in multiple fields including robotics. These methods however have had limited impact on dexterous hand manipulation, partly due to lack of rich and physically-consistent dataset as well as technology able to collect them. To fill this gap, we developed a virtual reality system combining real-time motion capture, physics simulation and stereoscopic visualization. The system enables a user wearing a CyberGlove to “reach-in” the simulation, and manipulate virtual objects through contacts with a tele-operated virtual hand. The system is evaluated on a subset of tasks in the Southampton Hand Assessment Procedure – which is a clinically validated test of hand function. The system is also being used by performer teams in the DARPA Hand Proprioception & Touch Interfaces program to develop neural control interfaces in simulation. The software is freely available at www.mujoco.org

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This video shows the first application of whole-body nonlinear model predictive control (MPC) on a humanoid robot. It is the first time that MPC is applied in real time to the whole body of a humanoid robot, controlling the balance, handling different constraints (e.g. collisions, joint limits) and performing the specified reaching task at the same time. In particular, we focus on the problems raised by the delays due to computation and by the differences between the real robot and the simulated model. The implementation of a complete model-predictive controller and its application to the physical HRP-2 robot is based on the optimal-control solver MuJoCo

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