# Learning Symbolic Operators: A Neurosymbolic Solution for Autonomous Disassembly of Electric Vehicle Battery

arXiv: https://arxiv.org/abs/2206.03027 · HTML: https://mlsanity.com/paper/2206.03027/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 7 Jun 2022.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Neurosymbolic AI: The 3rd Wave](https://mlsanity.com/paper/2012.05876/) (2020)
- [PDDLStream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planning](https://mlsanity.com/paper/1802.08705/) (2018)
- [Classical Planning in Deep Latent Space: Bridging the Subsymbolic-Symbolic Boundary](https://mlsanity.com/paper/1705.00154/) (2017)
- [Grounding Predicates through Actions](https://mlsanity.com/paper/2109.14718/) (2021)
- [SPOTTER: Extending Symbolic Planning Operators through Targeted Reinforcement Learning](https://mlsanity.com/paper/2012.13037/) (2020)
- [Transferable Task Execution from Pixels through Deep Planning Domain Learning](https://mlsanity.com/paper/2003.03726/) (2020)
- [Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning](https://mlsanity.com/paper/1908.06769/) (2019)

## Built on by (most cited first, 1 of 1)

- [Using Memory-Based Learning to Solve Tasks with State-Action Constraints](https://mlsanity.com/paper/2303.04327/) (2023)

## Abstract (arXiv)

The booming of electric vehicles demands efficient battery disassembly for recycling to be environment-friendly. Currently, battery disassembly is still primarily done by humans, probably assisted by robots, due to the unstructured environment and high uncertainties. It is highly desirable to design autonomous solutions to improve work efficiency and lower human risks in high voltage and toxic environments. This paper proposes a novel neurosymbolic method, which augments the traditional Variational Autoencoder (VAE) model to learn symbolic operators based on raw sensory inputs and their relationships. The symbolic operators include a probabilistic state symbol grounding model and a state transition matrix for predicting states after each execution to enable autonomous task and motion planning. At last, the method's feasibility is verified through test results.