Search Results

Sampling-based Model Predictive Control (MPC) is a flexible control framework that can reason about non-smooth dynamics and cost functions. Recently, significant work has focused on the use of machine learning to improve the performance of MPC, often ...

We present foundations for using Model Predictive Control (MPC) as a differen-tiable policy class for reinforcement learning. This provides one way of leveraging and combining the advantages of model-free and model-based approaches. Specifically, we ...

GPUs are bottlenecked by the off-chip communication bandwidth and its energy cost; hence near-data acceleration is particularly attractive for GPUs. Integrating the accelerators within DRAM can mitigate these bottlenecks and additionally expose them to ...

The data revolution is fueled by advances in machine learning, databases, and hardware design. Programmable accelerators are making their way into each of these areas independently. As such, there is a void of solutions that enables hardware ...

Novel algorithmic advances have paved the way for robotics to transform the dynamics of many social and enterprise applications. To achieve true autonomy, robots need to continuously process and interact with their environment through computationally-...