We present SIERRA, a novel framework for accelerating development and improving reproducibility of results in robotics research. SIERRA accelerates research by automating the process of generating experiments from queries over independent variables, executing experiments, and processing the results to generate deliverables such as graphs and videos. It shifts the paradigm for testing hypotheses from procedural (“Do these steps to answer the query”) to declarative (“Here is the query to test—GO!”), reducing the burden on researchers. It employs a modular architecture enabling easy customization and extension for the needs of individual researchers, thereby eliminating manual configuration and processing via throw-away scripts. SIERRA improves reproducibility of research by providing automation independent of the execution environment (HPC hardware, real robots, etc.) and targeted platform (simulator, real robots, etc.). This enables exact experiment replication, up to the limit of the execution environment and platform, as well as making it easy for researchers to test hypotheses in different computational environments. Though SIERRA is targeted at robotics research, its design makes it extendable to other fields.

Automation in construction is possible with systems designed using the swarm robotic principles of scalability, flexibility, robustness, and emergence. We derive quantitative measurements of these principles in 10,000 robot swarms as a first step in achieving this goal. We summarize our recent task allocation work in the context of an object gathering task and demonstrate its feasibility in the context of automated construction tasks. We present a trajectory to extend our current task allocation methodology using stochastic processes in order to present a unified approach to task allocation in swarm-robotic construction

When designing swarm-robotic systems, system- atic comparison of algorithms from different do- mains is necessary to determine which is capa- ble of scaling up to handle the target problem size and target operating conditions. We propose a set of quantitative metrics for scalability, flexibility, and emergence which are capable of addressing these needs during the system design process. We demonstrate the applicability of our proposed met- rics as a design tool by solving a large object gath- ering problem in temporally varying operating con- ditions using iterative hypothesis evaluation. We provide experimental results obtained in simulation for swarms of over 10,000 robots.