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Resource control in heterogeneous computers built with subsystems from different vendors is challenging. There is a tension between the need to quickly generate local decisions in each subsystem and the desire to coordinate the different subsystems for global optimization. In practice, global coordination among subsystems is considered hard, and current commercial systems use centralized controllers. The result is high response time and high design cost due to lack of modularity. To control emerging heterogeneous computers effectively, we propose a new control framework called Tangram that is fast, glob- ally coordinated, and modular. Tangram introduces a new formal controller that combines multiple engines for optimization and safety, and has a standard interface. Building the controller for a subsystem requires knowing only about that subsystem. As a het- erogeneous computer is assembled, the controllers in the different subsystems are connected hierarchically, exchanging standard co- ordination signals. To demonstrate Tangram, we prototype it in a heterogeneous server that we assemble using components from multiple vendors. Compared to state-of-the-art control, Tangram re- duces, on average, the execution time of heterogeneous applications by 31% and their energy-delay product by 39%.
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MeMo: Meaningful, Modular Controllers via Noise Injection
Robots are often built from standardized assemblies, (e.g. arms, legs, or fingers), but each robot must be trained from scratch to control all the actuators of all the parts together. In this paper we demonstrate a new approach that takes a single robot and its controller as input and produces a set of modular controllers for each of these assemblies such that when a new robot is built from the same parts, its control can be quickly learned by reusing the modular controllers. We achieve this with a framework called MeMo which learns (Me)aningful, (Mo)dular controllers. Specifically, we propose a novel modularity objective to learn an appropriate division of labor among the modules. We demonstrate that this objective can be optimized simultaneously with standard behavior cloning loss via noise injection. We benchmark our framework in locomotion and grasping environments on simple to complex robot morphology transfer. We also show that the modules help in task transfer. On both structure and task transfer, MeMo achieves improved training efficiency to graph neural network and Transformer baselines.
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- Award ID(s):
- 1918839
- PAR ID:
- 10585262
- Publisher / Repository:
- ICML 2024
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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