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Fleet2D: A fast and light simulator for home robotics

By Apaar Sadhwani, Hamid Badiozamani, Tushar Agarwal, Saraswathi Marthandam, Amin Atrash, Jing Zhu, Aarthi Raveendran, William D. Smart
2023
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Home robots operate in diverse and dynamic environments, delivering a range of functions that enhance utility. Many of these functions span extended periods, from weeks to months, typically improving through observations and interactions. Efficient development and validation of these functions necessitate simulations that can run faster than real time. However, many current robot simulators focus on high-fidelity physics simulation that limits their speed to a small multiple of real time. While these are tailored for critical low-level functions, they aren’t optimized for simulating higher-level functions such as learning human behaviors and interacting with them. In this work, we introduce Fleet2D, a fast, lightweight simulator designed for long-term human-robot interactions in the home. By abstracting away low-level physics, aggressively caching compute-intensive operations, and operating in a simplified two-dimensional world, we are able to perform realistic simulations of robot behavior at more than 10,000 times real time. We present the design, development, and validation of Fleet2D, showcase its effectiveness on a number of use cases in home robotics, and discuss how it has accelerated the development cycle for a home robot. Finally, we make Fleet2D open source for the research community at github.com/amazon-science/fleet2d.
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