TLDR: This research introduces a continual learning framework for robots to autonomously disassemble power batteries in dynamic environments. It employs a Neuro-Symbolic Task and Motion Planning (TAMP) approach, integrating multimodal perception (vision and force) for cross-validation and a bidirectional reasoning flow. This enables robots to dynamically correct perception errors, learn from task failures, and continuously optimize their performance, significantly improving task success rates and reducing perception misjudgments in complex industrial settings.
The rapid growth of the new energy vehicle industry has brought a significant challenge: the efficient and safe recycling of power batteries. Traditional disassembly methods rely heavily on manual labor, which is slow, costly, and often unsafe. This has spurred the development of autonomous robotic systems capable of handling these complex tasks.
However, unstructured disassembly environments, where conditions can change dynamically, pose a major hurdle for robots. Factors like shifting layouts, camera displacements, or sudden lighting changes can lead to perception errors, causing robots to misjudge situations and fail tasks. Existing robotic systems often struggle to adapt to these dynamic changes and can experience performance degradation over time due to issues like tool wear.
A New Framework for Adaptive Disassembly
Researchers have proposed a novel continual learning framework designed to enhance the adaptability and robustness of robots in these challenging environments. This framework integrates a multimodal perception cross-validation mechanism into a Neuro-Symbolic Task and Motion Planning (TAMP) system. TAMP is a core technology for embodied intelligence, combining high-level symbolic task planning with low-level motion optimization.
The proposed system operates with a bidirectional reasoning flow:
- Forward Working Flow: This is where the robot plans and executes its actions. It uses multimodal perception data (vision and force feedback) to understand its environment and map it to symbolic states. Based on these states and a defined goal, a planner generates a sequence of actions. If an action fails to achieve its expected outcome, the system triggers a replanning mechanism, dynamically adjusting its strategy.
- Backward Learning Flow: This flow focuses on learning from past experiences. After a task is completed, especially if it involved replanning or initial failures, the system retrospectively analyzes the execution. It identifies and corrects perception errors, using successful outcomes (often from a more reliable modality like force perception) as ‘ground truth’ to retrain and optimize its perception models. It also corrects misjudgments in symbolic states by identifying and flipping the least confident neural predicates. This process autonomously collects effective data, reducing the need for manual annotations and enabling continuous self-optimization.
Multimodal Perception: Vision and Force
A key innovation is the multimodal perception cross-validation mechanism. Robots often rely on a single perception modality, which can be vulnerable to environmental noise. By combining vision (using RGB-D images) and force feedback (detecting variations during contact), the system gains a more robust understanding of its environment. For instance, if visual perception initially misjudges a screw’s pose, force feedback during an insertion attempt can detect the failure, trigger replanning, and then provide a more accurate pose estimation, which is then used to correct the visual model.
Experimental Validation and Results
The framework was tested in a real-world scenario involving the disassembly of electric vehicle battery screws using a UR10e collaborative robotic arm. The experiments simulated realistic operational conditions, including lighting variations that typically affect visual perception. The system’s performance was measured by its task success rate and the average number of replans required per task, with a replanning threshold set to prevent excessive recovery attempts.
The results were significant: the task success rate in dynamic disassembly scenarios improved from 81.68% to a perfect 100% after just two continual learning updates. Concurrently, the average number of perception misjudgments (indicated by replans) decreased substantially, from 3.389 to 1.128. These findings underscore the framework’s ability to enhance robustness and practicality in unstructured industrial environments.
Also Read:
- Advancing Robot Generalization Through Preserved Vision-Language Representations
- Tenma: A New Approach to Versatile Robot Manipulation
Future Directions
While highly effective, the current system still relies on carefully designed predicates and action primitives. Future work aims to incorporate action success rates and state transition probabilities for more robust probabilistic reasoning. Researchers also plan to explore integrating continual learning with adaptive action primitive generation using large language models (LLMs). LLMs could help the system dynamically generate and refine actions, define new symbolic operations, and optimize parameters through multimodal feedback, leading to even more autonomous skill acquisition and improved generalization in complex scenarios. You can read more about this research in the full paper available here.
