The BenchBot software stack is a collection of software packages that allow end users to control robots in real or simulated environments with a simple python API. It leverages the simple "observe, act, repeat" approach to robot problems prevalent in reinforcement learning communities (OpenAI Gym users will find the BenchBot API interface very similar).
BenchBot was created as a tool to assist in the research challenges faced by the semantic scene understanding community; challenges including understanding a scene in simulation, transferring algorithms to real world systems, and meaningfully evaluating algorithm performance. We've since realised, these challenges don't just exist for semantic scene understanding, they're prevalent in a wide range of robotic problems.
This led us to create version 2 of BenchBot with a focus on allowing users to define their own functionality for BenchBot through add-ons. Want to integrate your own environments? Plug-in new robot platforms? Define new tasks? Share examples with others? Add evaluation measures? This all now possible with add-ons, and you don't have to do anything more than add some YAML and Python files defining your new content!
The "bench" in "BenchBot" refers to benchmarking, with our goal to provide a system that greatly simplifies the benchmarking of novel algorithms in both realistic 3D simulation and on real robot platforms. If there is something else you would like to use BenchBot for (like integrating different simulators), please let us know. We're very interested in BenchBot being the glue between your novel robotics research and whatever your robot platform may be.
This repository contains the software stack needed to develop solutions for BenchBot tasks on your local machine. It installs and configures a significant amount of software for you, wraps software in stable Docker images (~120GB), and provides simple interaction with the stack through 4 basic scripts:
We've used BenchBot in a number of places already in our research, including our Semantic Scene Understanding challenges. See below for an overview of how the challenges work.
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