Attentive support

A simulation-based implementation of the attentive support robot introduced in the paper To Help or Not to Help: LLM-based Attentive Support for Human-Robot Group Interactions.
See the project website for an overview.

Setup

Python 3.8 - 3.11
Prerequisites for building the simulator workspace: g++, cmake, Libxml2, Qt5, qwt, OpenSceneGraph, Bullet Physics

Ubuntu 20

libxml2-dev, qt5-default, libqwt-qt5-dev, libopenscenegraph-dev, libbullet-dev, libasio-dev, libzmq3-dev

Ubuntu 22

libxml2-dev, qtbase5-dev, qt5-qmake, libqwt-qt5-dev, libopenscenegraph-dev, libbullet-dev, libasio-dev, libzmq3-dev

Fedora

cmake, gcc-c++, OpenSceneGraph-devel, libxml2, qwt-qt5-devel, bullet-devel, asio-devel, cppzmq-devel, python3-devel, portaudio

Clone this repo and change into it: git clone https://github.com/HRI-EU/AttentiveSupport.git && cd AttentiveSupport
You can either run the setup script: bash build.sh or follow these steps:

1. Get the submodules: git submodule update --init --recursive
2. Create a build directory in the AttentiveSupport directory: mkdir -p build and change into it cd build
3. Install the smile workspace: cmake ../src/Smile/ -DCMAKE_INSTALL_PREFIX=../install; make -j; make install
   Note that if you have the Smile workspace installed somewhere else, you have to change the relative path in config.yaml accordingly. For details, check here
4. Install the Python dependencies: python -m venv .venv && source .venv/bin/activate && pip install -r requirements.txt
5. Make sure you have an OpenAI API key set up, see the official instructions
6. Enjoy 🕹️

Containerized Runtime

• Tested with podman and rootless docker
• Build the container: docker build -t localhost/attentive_support .
• Run the container with display support and don't forget to set the OPENAI_API_KEY as environment variable.

podman:

docker run \
-e OPENAI_API_KEY=replace_me \
-e WAYLAND_DISPLAY \
--net=host \
-it \
localhost/attentive_support

docker:

docker run \
-e OPENAI_API_KEY=replace_me \
-v /tmp/.X11-unix:/tmp/.X11-unix \
-it \
localhost/attentive_support

Containerized Runtime (remote, rootless with internal ssh server)

In certain scenarios it might not be possible to display the graphical window. For example when running docker rootless on a remote machine with X11. For these scenarios, the docker image can be built with the option docker build --build-arg WITH_SSH_SERVER=true -t localhost/attentive_support .. Then the image can be started with:

docker run \
  -it \
  -p 2022:22 \
  localhost/attentive_support

This starts an ssh server on port 2022 that can be accessed with username root and password hri. Then the example script can be started with:

export RCSVIEWER_SIMPLEGRAPHICS=True
export OPENAI_API_KEY=replace_me
/usr/bin/python -i /attentive_support/src/tool_agent.py

Usage

Running the agent

• Activate the virtual environment: source .venv/bin/activate
• Run the agent in interactive mode, from the AttentiveSupport directory: python -i src/tool_agent.py
• Provide commands: agent.plan_with_functions("Move the red glass to Felix")
• Reset
  • The simulation: SIM.reset()
  • The agent: agent.reset()

Customizing the agent

• Change the agent's character:
  • Either via the system_prompt variable in gpt_config.py
  • Or directly, note that this is not persistent: agent.character = "You are a whiny but helpful robot."
• Provide the agent with further tools:
  • Define tools as Python functions in tools.py
  • Make sure to use type hints and add docstrings in the Sphinx notation. This is important so that the function_analyzer.py can generate the function descriptions for openai automagically
  • For inspiration, check out some more examples in src/tool_variants/extended_tools.py
• Change generic settings such as the model used and its temperature via gpt_config.py
• Note: The gpt_config.py file can either be changed directly, or the filename of a custom config file can be passed to the agent when running in interactive mode: python -i src/tool_agent.py --config=custom_config

Additional features

• Stop the robot mid-action: activate the simulation window, then press Shift + S
• Setting an agent as busy: set_busy("Daniel", "iphone5")
• Enable text to speech: enable_tts()
• Speech input; start talking after executing the command and press any key (or a specified push_key) to stop the speech input: agent.execute_voice_command_once()

Example

Running the simulation with "Move the red glass to Felix":

For reproducing the situated interaction scenario run the following:

• agent.plan_with_functions("Felix -> Daniel: Hey Daniel, what do we have to drink?")
  Robot should do nothing because Daniel is available to answer.
• agent.plan_with_functions("Daniel -> Felix: We have two options, cola and fanta.")
  Robot should correct Daniel.
• agent.plan_with_functions("Felix -> Daniel: Daniel, please hand me the red glass.")
  Robot should help because the red glass is out of reach for Daniel
• Manually set Daniel to busy with the mobile: set_busy("Daniel", "iphone5")
• agent.plan_with_functions("Felix -> Daniel: Daniel, could you fill some coca cola into my glass?")
  Robot should help as Daniel is busy.
• agent.plan_with_functions("Daniel -> Felix: Felix, can you give me a full glass of the same, but without sugar?")
  Robot should help as Felix cannot see or reach the coke zero.
• agent.plan_with_functions("Felix -> Robot: What do you know about mixing coke and fanta?")
  Robot should answer.