# Detecting and Accommodating Novel Types and Concepts in an Embodied Simulation Environment

arXiv: https://arxiv.org/abs/2211.04555 · HTML: https://mlsanity.com/paper/2211.04555/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 59 references that are in the map.
- First posted on arXiv 8 Nov 2022.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners](https://mlsanity.com/paper/2009.07118/) (2020)
- [Unity: A General Platform for Intelligent Agents](https://mlsanity.com/paper/1809.02627/) (2018)
- [Learning Physical Intuition of Block Towers by Example](https://mlsanity.com/paper/1603.01312/) (2016)
- [Towards Practical Multi-Object Manipulation using Relational Reinforcement Learning](https://mlsanity.com/paper/1912.11032/) (2019)
- [VoxML: A Visualization Modeling Language](https://mlsanity.com/paper/1610.01508/) (2016)
- [Acquiring Target Stacking Skills by Goal-Parameterized Deep Reinforcement Learning](https://mlsanity.com/paper/1711.00267/) (2017)
- [Exploiting Embodied Simulation to Detect Novel Object Classes Through Interaction](https://mlsanity.com/paper/2204.08107/) (2022)

## Built on by (most cited first, 3 of 3)

- [Grounding and Distinguishing Conceptual Vocabulary Through Similarity Learning in Embodied Simulations](https://mlsanity.com/paper/2305.13668/) (2023)
- [An Abstract Specification of VoxML as an Annotation Language](https://mlsanity.com/paper/2305.13076/) (2023)
- [Computational Thought Experiments for a More Rigorous Philosophy and Science of the Mind](https://mlsanity.com/paper/2405.08304/) (2024)

## Abstract (arXiv)

In this paper, we present methods for two types of metacognitive tasks in an AI system: rapidly expanding a neural classification model to accommodate a new category of object, and recognizing when a novel object type is observed instead of misclassifying the observation as a known class. Our methods take numerical data drawn from an embodied simulation environment, which describes the motion and properties of objects when interacted with, and we demonstrate that this type of representation is important for the success of novel type detection. We present a suite of experiments in rapidly accommodating the introduction of new categories and concepts and in novel type detection, and an architecture to integrate the two in an interactive system.