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

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.

SeanNet: Semantic Understanding Network for Localization Under Object Dynamics

We aim for domestic robots to operate indoor for long-term service. Under the object-level scene dynamics induced by human daily activities, a robot needs to robustly localize itself in the environment subject to scene uncertainties. Previous works have addressed visual-based localization in static environments, yet the object-level scene dynamics challenge existing methods on long-term deployment of the robot. This paper proposes SEmantic understANding Network (SeanNet) that enables robots to measure the similarity between two scenes on both visual and semantic aspects. We further develop a similarity-based localization method based on SeanNet for monitoring the progress of visual navigation tasks. In our experiments, we benchmarked SeanNet against baselines methods on scene similarity measures, as well as visual navigation performance once integrated with a visual navigator. We demonstrate that SeanNet outperforms all baseline methods, by robustly localizing the robot under object dynamics, thus reliably informing visual navigation about the task status.