The GOMA algorithm casts human-robot communication as a planning problem by selecting utterances that maximizally improves the efficiency of the joint plan in a partially observable environment.
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Reward of robot sharing information X to human:
$R$ (request X) = KL($\mathbb{E}$ [human plan | human mind + X] ||$\mathbb{E}$ [human plan | human mind ]) -$C$ -
Reward of robot requesting information X from human:
$R$ (request X) = KL($\mathbb{E}$ [robot plan | robot mind + X] ||$\mathbb{E}$ [robot plan | robot mind ]) -$C$
where C is the communication cost.
You can find a demonstration video on the website: lanceying.com/GOMA
The current implementation is tested in the virtual home domain. For installing virtual home, please clone this repo: https://github.com/xavierpuigf/virtualhome
To run the experiment use the following code:
python3 test_template_agent_structured.py --num-belief-particles 10 --num-proc 10 --model goma