Learning to Control Emulated Muscles in Real Robots: Towards Exploiting Bio-Inspired Actuator Morphology

Recent studies have demonstrated the immense potential of exploiting muscle actuator morphology for natural and robust movement -- in simulation. A validation on real robotic hardware is yet missing. In this study, we emulate muscle actuator properties on hardware in real-time, taking advantage of modern and affordable electric motors. We demonstrate that our setup can emulate a simplified muscle model on a real robot while being controlled by a learned policy. We improve upon an existing muscle model by deriving a damping rule that ensures that the model is not only performant and stable but also tuneable for the real hardware. Our policies are trained by reinforcement learning entirely in simulation, where we show that previously reported benefits of muscles extend to the case of quadruped locomotion and hopping: the learned policies are more robust and exhibit more regular gaits. Finally, we confirm that the learned policies can be executed on real hardware and show that sim-to-real transfer with real-time emulated muscles on a quadruped robot is possible. These results show that artificial muscles can be highly beneficial actuators for future generations of robust legged robots.

PyDCM: Custom Data Center Models with Reinforcement Learning for Sustainability

The increasing global emphasis on sustainability and reducing carbon emissions is pushing governments and corporations to rethink their approach to data center design and operation. Given their high energy consumption and exponentially large computational workloads, data centers are prime candidates for optimizing power consumption, especially in areas such as cooling and IT energy usage. A significant challenge in this pursuit is the lack of a configurable and scalable thermal data center model that offers an end-to-end pipeline. Data centers consist of multiple IT components whose geometric configuration and heat dissipation make thermal modeling difficult. This paper presents PyDCM, a customizable Data Center Model implemented in Python, that allows users to create unique configurations of IT equipment with custom server specifications and geometric arrangements of IT cabinets. The use of vectorized thermal calculations makes PyDCM orders of magnitude faster (30 times) than current Energy Plus modeling implementations and scales sublinearly with the number of CPUs. Also, PyDCM enables the use of Deep Reinforcement Learning via the Gymnasium wrapper to optimize data center cooling and offers a user-friendly platform for testing various data center design prototypes.