# Using Human-Guided Causal Knowledge for More Generalized Robot Task Planning

arXiv: https://arxiv.org/abs/2110.04664 · HTML: https://mlsanity.com/paper/2110.04664/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 9 Oct 2021.

## Builds on (most cited first, 9 of 9)

- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [Model-Ensemble Trust-Region Policy Optimization](https://mlsanity.com/paper/1802.10592/) (2018)
- [Towards Causal Representation Learning](https://mlsanity.com/paper/2102.11107/) (2021)
- [Near Optimal Behavior via Approximate State Abstraction](https://mlsanity.com/paper/1701.04113/) (2017)
- [Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense](https://mlsanity.com/paper/2004.09044/) (2020)
- [Causal Induction from Visual Observations for Goal Directed Tasks](https://mlsanity.com/paper/1910.01751/) (2019)
- [Theory-based Causal Transfer: Integrating Instance-level Induction and Abstract-level Structure Learning](https://mlsanity.com/paper/1911.11185/) (2019)
- [Learning State Abstractions for Transfer in Continuous Control](https://mlsanity.com/paper/2002.05518/) (2020)

## Abstract (arXiv)

A major challenge in research involving artificial intelligence (AI) is the development of algorithms that can find solutions to problems that can generalize to different environments and tasks. Unlike AI, humans are adept at finding solutions that can transfer. We hypothesize this is because their solutions are informed by causal models. We propose to use human-guided causal knowledge to help robots find solutions that can generalize to a new environment. We develop and test the feasibility of a language interface that na"ive participants can use to communicate these causal models to a planner. We find preliminary evidence that participants are able to use our interface and generate causal models that achieve near-generalization. We outline an experiment aimed at testing far-generalization using our interface and describe our longer terms goals for these causal models.