Design and Architecture

Building upon Project Malmo

ToMCAT extends Project Malmo to provide a platform to study human behavior in a virtual environment, with a focus on studying human-human and human-machine teaming.

The core of Project Malmo consisted of two components: a static C++ library and a Java mod for Minecraft. Together, these components exposed an API for communicating with Minecraft over a TCP socket, thus enabling researchers to programmatically control avatars in the Minecraft world and declaratively specify ‘missions’ that provided suitable environments for reinforcement learning research.

ToMCAT inverts the original research direction of Project Malmo somewhat - rather than serving as a reinforcement learning research platform to teach AI agents to perform complex tasks in Minecraft, we focus on learning about how humans think and behave. We have vendorized the source of Project Malmo (external/malmo) and modified it to suit our purposes:

  1. The original Java mod (external/malmo/Minecraft) has been extended to

    • allow human control by default,

    • add software instrumentation to capture human actions in the Minecraft environment, and

    • add ‘missions’ (and documentation on how to implement new ones) to support developing machine social intelligence.

  2. The original C++ code for the Malmo static library has not been changed significantly (besides a bit of modernization and code formatting). We use this library to create an executable, runMission that serves as the driver for our experiments.

There are a couple of other high-level differences in the technical approaches taken by Malmo and ToMCAT.

  • In Malmo, a mission environment can be declaratively specified using low (e.g. DrawBlock, DrawEntity elements) and high (e.g. BuildBattleDecorator) level XML elements, and corresponding ‘decorator’ implementations on the Java side. In contrast, the equivalent functionality in ToMCAT has been almost completely extracted out into a separate C++ module (src/cpp/pro_gen) that simultaneously creates low-level (block and entity positions, types, etc) and high-level (specifications of bounding volumes and topological structure) machine-readable representations of the mission environment that can be then parsed relatively straightforward on the Java side to create the mission environment. These representations can also be used downstream by other applications, including AI agents in the ASIST program.

  • Malmo focuses on state observations at regular time intervals, but when studying humans, it is desirable to record events that do not necessary happen at regular time intervals (e.g. opening a door, flipping a lever, attacking a mob). These kinds of events are accessible through the Forge API, and ToMCAT exposes a subset of these (see the documentation on events and data models and instructions on how to implement new missions and events for more details).


We place a heavy premium on automation, and thus eschew support for Windows and non-LAN multiplayer in favor of maintaining a set of robust scripts (in the tools directory) and a procedural generation module (src/cpp/pro_gen) to ease the lives of developers and end-users by minimizing the amount of complex documentation they need to read to get set up. Our software is currently tested on macOS and Ubuntu with a continuous integration pipeline.


Audio and Video

We implement sensors to record video and audio of the human player and the game itself. The video and audio of the player and the video of the game are captured using ffmpeg, and the audio of the game is captured using pacat (Linux) or BlackHole (macOS).

In-game observations and events

We implement capturing in-game observations and events related to the human players and the missions they are conducting. These are published to an MQTT message bus on various topics.

Facial Landmark, Gaze, Pose, and AU detection

We leverage OpenFace to create an executable called faceAnalyzer that outputs JSON messages containing information about a player’s gaze, pose, action units, and face landmarks, either from a webcam feed or from a video file. These messages are output to standard output, from where they can be either redirected to a file, piped to an MQTT client (e.g. mosquitto_sub) for publication to a message bus, or used for other downstream applications.