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

• URL: http://arxiv.org/abs/2206.03027v1
• Date: Tue, 7 Jun 2022 06:00:39 GMT
• Title: Learning Symbolic Operators: A Neurosymbolic Solution for Autonomous Disassembly of Electric Vehicle Battery
• Abstract summary: Currently, battery disassembly is still primarily done by humans, probably assisted by robots, due to the unstructured environment and high uncertainties. This paper proposes a novel neurosymbolic method, which augments the traditional Variational Autoencoder (VAE) model to learn symbolic operators. The method's feasibility is verified through test results.
• Score: 16.963523032239202
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• A Real-Time Neuro-Symbolic Ethical Governor for Safe Decision Control in Autonomous Robotic Manipulation [0.0]
  This paper presents a real-time neuro-symbolic ethical governor to enable risk-aware supervisory control in autonomous robotic manipulation tasks.<n>The proposed framework integrates transformer-based ethical reasoning with a probabilistic ethical risk field formulation.<n>The effectiveness of the proposed approach is validated through simulated autonomous robot-arm task scenarios.
  arXiv  Detail & Related papers  (2026-03-15T04:50:45Z)
• EmboCoach-Bench: Benchmarking AI Agents on Developing Embodied Robots [68.29056647487519]
  Embodied AI is fueled by high-fidelity simulation and large-scale data collection.<n>However, this scaling capability remains bottlenecked by a reliance on labor-intensive manual oversight.<n>We introduce textscEmboCoach-Bench, a benchmark evaluating the capacity of LLM agents to autonomously engineer embodied policies.
  arXiv  Detail & Related papers  (2026-01-29T11:33:49Z)
• Embodied Intelligence in Disassembly: Multimodal Perception Cross-validation and Continual Learning in Neuro-Symbolic TAMP [12.081833179751724]
  This paper proposes a continual learning framework based on Neuro-Symbolic task and motion planning (TAMP) to enhance the adaptability of embodied intelligence systems in dynamic environments.<n> Experimental results show that the proposed framework improves the task success rate in dynamic disassembly scenarios from 81.68% to 100%, while reducing the average number of perception misjudgments from 3.389 to 1.128.
  arXiv  Detail & Related papers  (2025-09-14T13:47:07Z)
• Mitigating Covariate Shift in Imitation Learning for Autonomous Vehicles Using Latent Space Generative World Models [60.87795376541144]
  A world model is a neural network capable of predicting an agent's next state given past states and actions. During end-to-end training, our policy learns how to recover from errors by aligning with states observed in human demonstrations. We present qualitative and quantitative results, demonstrating significant improvements upon prior state of the art in closed-loop testing.
  arXiv  Detail & Related papers  (2024-09-25T06:48:25Z)
• Revolutionizing Battery Disassembly: The Design and Implementation of a Battery Disassembly Autonomous Mobile Manipulator Robot(BEAM-1) [5.6751825724842035]
  We propose a Battery Disassembly AMMR(BEAM-1) system based on NeuralSymbolic AI. It detects the environmental state by leveraging a combination of multi-sensors and neural predicates and then translates this information into a quasi-symbolic space. In real-time, it identifies the optimal sequence of action primitives through LLM-heuristic tree search, ensuring high-precision execution of these primitives.
  arXiv  Detail & Related papers  (2024-07-09T06:44:20Z)
• Deriving Rewards for Reinforcement Learning from Symbolic Behaviour Descriptions of Bipedal Walking [5.737287537823071]
  In this paper, a novel approach to finding a reward function from a symbolic description is proposed. The intended system behaviour is modelled as a hybrid automaton, which reduces the system state space to allow more efficient reinforcement learning. The approach is applied to bipedal walking, by modelling the walking robot as a hybrid automaton over state space orthants, and used with the compass walker to derive a reward that incentivizes following the hybrid automaton cycle.
  arXiv  Detail & Related papers  (2023-12-16T05:37:57Z)
• RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation [68.70755196744533]
  RoboGen is a generative robotic agent that automatically learns diverse robotic skills at scale via generative simulation. Our work attempts to extract the extensive and versatile knowledge embedded in large-scale models and transfer them to the field of robotics.
  arXiv  Detail & Related papers  (2023-11-02T17:59:21Z)
• Robot Learning with Sensorimotor Pre-training [98.7755895548928]
  We present a self-supervised sensorimotor pre-training approach for robotics. Our model, called RPT, is a Transformer that operates on sequences of sensorimotor tokens. We find that sensorimotor pre-training consistently outperforms training from scratch, has favorable scaling properties, and enables transfer across different tasks, environments, and robots.
  arXiv  Detail & Related papers  (2023-06-16T17:58:10Z)
• DiAReL: Reinforcement Learning with Disturbance Awareness for Robust Sim2Real Policy Transfer in Robot Control [0.0]
  Delayed Markov decision processes fulfill the Markov property by augmenting the state space of agents with a finite time window of recently committed actions. We introduce a disturbance-augmented Markov decision process in delayed settings as a novel representation to incorporate disturbance estimation in training on-policy reinforcement learning algorithms.
  arXiv  Detail & Related papers  (2023-06-15T10:11:38Z)
• Active Predicting Coding: Brain-Inspired Reinforcement Learning for Sparse Reward Robotic Control Problems [79.07468367923619]
  We propose a backpropagation-free approach to robotic control through the neuro-cognitive computational framework of neural generative coding (NGC) We design an agent built completely from powerful predictive coding/processing circuits that facilitate dynamic, online learning from sparse rewards. We show that our proposed ActPC agent performs well in the face of sparse (extrinsic) reward signals and is competitive with or outperforms several powerful backprop-based RL approaches.
  arXiv  Detail & Related papers  (2022-09-19T16:49:32Z)
• Your Autoregressive Generative Model Can be Better If You Treat It as an Energy-Based One [83.5162421521224]
  We propose a unique method termed E-ARM for training autoregressive generative models. E-ARM takes advantage of a well-designed energy-based learning objective. We show that E-ARM can be trained efficiently and is capable of alleviating the exposure bias problem.
  arXiv  Detail & Related papers  (2022-06-26T10:58:41Z)
• EVGen: Adversarial Networks for Learning Electric Vehicle Charging Loads and Hidden Representations [4.273017002805776]
  We develop generative adversarial networks (GANs) to learn of electric vehicle (EV) charging sessions and disentangled representations. We show that this model structure successfully parameterizes unlabeled temporal and power patterns without supervision and is able to generate synthetic data conditioned on these parameters.
  arXiv  Detail & Related papers  (2021-08-09T00:23:47Z)
• Towards self-organized control: Using neural cellular automata to robustly control a cart-pole agent [62.997667081978825]
  We use neural cellular automata to control a cart-pole agent. We trained the model using deep-Q learning, where the states of the output cells were used as the Q-value estimates to be optimized.
  arXiv  Detail & Related papers  (2021-06-29T10:49:42Z)
• Conv1D Energy-Aware Path Planner for Mobile Robots in Unstructured Environments [9.230959086746736]
  Energy consumption plays a major role in the navigation of mobile robots in challenging environments. This paper reports on first results of an energy-aware path planner, which can provide estimates of the driving energy consumption and energy recovery of a robot traversing complex uneven terrains. A novel feature of the method is the use of 1D convolutional neural network to analyse the terrain sequentially in the same temporal order as it would be experienced by the robot when moving.
  arXiv  Detail & Related papers  (2021-04-04T08:13:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.