Can Robots Learn to Disassemble Electronics?

Author: Denis Avetisyan

New research explores the potential of AI-powered vision-language-action models to automate the complex process of extracting valuable components from e-waste.

Traditional methods of disassembly rely on sequential stages, whereas end-to-end vision-language-action approaches offer a unified framework, suggesting a fundamental shift in how robotic systems perceive, interpret, and physically manipulate objects.

This review assesses the feasibility of end-to-end learning for selective robotic disassembly, focusing on a case study of desktop computer component extraction using Vision-Language-Action models and LoRA fine-tuning.

Automating the disassembly of electronic waste remains a significant challenge despite advances in robotics, largely due to the inherent variability and complexity of the task. This work, ‘Vision-Language-Action Models for Selective Robotic Disassembly: A Case Study on Critical Component Extraction from Desktops’, investigates the potential of end-to-end vision-language-action (VLA) models for selectively disassembling desktop computers, specifically targeting valuable components like RAM and CPUs. While fine-tuned VLA models demonstrated promising performance on initial disassembly steps, full automation required a hybrid approach combining VLA with rule-based control, revealing limitations in precision and data coverage. Can future research address these challenges and unlock the full potential of VLA models for truly autonomous e-waste recycling?

The E-Waste Crisis: A Systemic Challenge Demanding Innovation

The relentless pace of technological advancement fuels a burgeoning crisis of electronic waste, or E-Waste, presenting substantial environmental and economic challenges globally. Discarded smartphones, computers, televisions, and other devices accumulate at an alarming rate – exceeding $170 billion annually – creating mountains of refuse laden with both hazardous materials and valuable recoverable resources. Improper disposal leads to soil and water contamination from substances like lead, mercury, and cadmium, posing risks to both human health and ecosystems. Simultaneously, the loss of precious metals – including gold, silver, and palladium – represents a significant economic waste, as these materials are finite and require energy-intensive mining to obtain. Addressing this escalating E-Waste problem demands innovative solutions focused on responsible recycling and resource recovery, highlighting the urgent need for sustainable electronics management practices.

Current electronic waste recycling largely relies on manual disassembly, a process demonstrably burdened by substantial limitations. The labor-intensive nature of separating components – often involving hazardous materials and intricate designs – drives up operational costs and creates a bottleneck in the recycling stream. This approach is not only expensive, requiring significant workforce investment, but also prone to inefficiencies; valuable materials are frequently overlooked or damaged during manual processing, reducing the overall recovery rate. Furthermore, the repetitive and sometimes dangerous tasks associated with manual disassembly pose health and safety risks to workers, highlighting the urgent need for more sustainable and automated solutions to address the growing e-waste crisis.

The potential of automated disassembly lies in its ability to transform e-waste from a problematic burden into a valuable resource stream. Current manual methods struggle to efficiently separate components containing precious metals like gold, silver, and palladium, often resulting in material loss and environmental contamination. Intelligent robotic systems, however, promise a precision and speed unattainable by human workers. These systems require advanced computer vision to identify materials, delicate manipulation skills to separate components without damage, and adaptive algorithms to handle the diverse and ever-changing stream of electronic devices. Successful implementation hinges on creating robots capable of not just deconstructing electronics, but also learning from each device to optimize the disassembly process and maximize material recovery, thereby lessening the environmental footprint and fostering a circular economy for electronics.

Robotic disassembly presents a greater manipulation challenge than typical VLA applications due to the small size and visual obscurity of target components, requiring significantly higher precision.

Vision-Language-Action: A Unified Approach to Robotic Manipulation

Vision-Language-Action (VLA) models represent a shift in robotic control by unifying perception, language understanding, and action execution within a single framework. Traditionally, robotic systems relied on pipelines that separated these functions – first processing visual data, then interpreting high-level commands, and finally generating motor controls. VLA models, however, directly map visual inputs – such as images or depth data – and natural language task descriptions to robotic actions, bypassing the need for intermediate representations. This direct mapping is typically achieved through large multimodal neural networks trained on datasets that pair visual scenes, language instructions, and corresponding robot trajectories. The result is a more streamlined and potentially more robust system capable of executing complex tasks based on intuitive language commands and real-time visual feedback.

OpenVLA is a Vision-Language-Action (VLA) model designed for robotic manipulation, leveraging the Llama2 large language model as its base. This architecture allows OpenVLA to process visual input from cameras and natural language task descriptions to generate robotic actions. The model distinguishes itself through its comparatively small size – containing 7 billion parameters – while maintaining strong performance on benchmark robotic tasks. This compact design facilitates deployment on resource-constrained robotic platforms and reduces computational demands during both training and inference. Evaluations demonstrate OpenVLA’s capability to effectively ground language instructions in visual perceptions and translate them into executable robot commands for manipulation objectives.

LoRA (Low-Rank Adaptation) fine-tuning presents a parameter-efficient transfer learning technique for adapting the OpenVLA model to specific robotic disassembly tasks. Instead of updating all model parameters, LoRA introduces trainable low-rank matrices which are added to the existing weights, significantly reducing the number of trainable parameters – from billions to millions. This reduction in trainable parameters lowers computational costs and memory requirements during training, enabling faster adaptation with limited datasets. Experimental results demonstrate that LoRA fine-tuning achieves performance comparable to full fine-tuning while requiring up to 100x fewer trainable parameters, thereby facilitating wider accessibility and scalability for robotic disassembly applications.

Training loss curves demonstrate successful learning of both disassembly tasks by the VLA models.

Teleoperation and Data Collection: Bridging the Gap to Autonomous Systems

The Gello System enables the collection of demonstration data critical for training the Variable Learning Automation (VLA) models. This teleoperation-based approach allows human operators to remotely control a robot to perform disassembly tasks, generating data that records the operator’s actions, tool usage, and applied forces. The resulting dataset comprises time-stamped robot states, including joint angles, end-effector poses, and applied forces, alongside corresponding visual data from onboard cameras. This data is then used as supervised learning input for the VLA models, effectively transferring human expertise to the robotic system and providing a foundation for autonomous disassembly capabilities.

Human demonstration data, gathered through teleoperation, provides detailed records of expert disassembly techniques, including force application, trajectory planning, and problem-solving approaches when encountering unexpected resistance or component fit. This data encompasses not only the kinematic movements of the tools but also implicit knowledge regarding grip adjustments, component manipulation strategies for tight spaces, and the sequential ordering of actions necessary for successful disassembly of diverse assemblies. The captured nuances extend beyond simple task completion; they reveal how experts adapt to variations in part tolerances, identify potential failure points, and prioritize actions based on assembly complexity, offering a rich dataset for training robust and adaptable robotic learning algorithms.

The VLA model’s ability to generalize across diverse assembly scenarios is significantly enhanced through learning from human demonstrations collected via teleoperation. Complex geometries and varying component configurations often present challenges for traditional robotic approaches reliant on precise pre-programming or rigid algorithms. However, by observing and replicating the strategies of human experts, the VLA model acquires a robust understanding of manipulation techniques adaptable to unforeseen circumstances. This demonstrative learning allows the model to infer solutions for novel configurations without requiring explicit re-programming for each unique assembly, effectively bridging the gap between simulated environments and real-world complexity.

Imitation learning data was collected using a teleoperation setup, allowing a human operator to guide the robot's actions. — Imitation learning data was collected using a teleoperation setup, allowing a human operator to guide the robot’s actions.

Practical Implementation: Validating Disassembly Capabilities

The robotic system has demonstrated successful execution of core disassembly procedures, specifically the removal of RAM modules and the unlocking of CPU brackets. These tasks required coordinated manipulation and precise application of force, validating the system’s ability to perform delicate and complex actions. Successful completion of these procedures indicates the model’s foundational capabilities in robotic disassembly, serving as a benchmark for evaluating improvements in perception, planning, and control as more complex disassembly sequences are implemented. These initial successes provide a basis for assessing the integration of sensor data and vision-based localization techniques to further enhance performance and reliability.

The incorporation of force/tactile sensors into the robotic system directly addresses challenges associated with delicate component handling during disassembly. These sensors provide real-time feedback on applied forces during grasping and manipulation, enabling the robot to modulate its actions and prevent excessive pressure that could damage sensitive parts. This feedback loop improves the robustness of the disassembly process by allowing the system to adapt to variations in component tolerances and unexpected obstructions. Specifically, the sensors facilitate precise control of grip force, minimizing the risk of component fracture or deformation during removal and contributing to a more reliable and repeatable process.

Component identification and localization are critical functions performed by the system’s computer vision module. This module enables robotic adaptation to variations in component placement and orientation during disassembly tasks. The vision system processes visual data to determine the precise position and angular orientation of target components, such as RAM modules and CPU brackets, within the workspace. This information is then used to guide the robot’s manipulation actions, ensuring accurate grasping and preventing collisions. Performance metrics, including a baseline 4/20 success rate for RAM localization with OpenVLA and improvement to 7/20 with OpenVLA-OFT, demonstrate the iterative refinement of the vision system’s capabilities. Further advancements, evidenced by OpenVLA achieving a 13/20 RAM approach success rate and 16/20 with OpenVLA-OFT, highlight the ongoing development of robust visual perception for dynamic disassembly scenarios.

Initial testing of the OpenVLA system for RAM module localization yielded a success rate of 4 out of 20 attempts. Subsequent implementation of OpenVLA-OFT, incorporating optimization of the foundational training data, resulted in a measurable improvement, increasing the RAM localization success rate to 7 out of 20 attempts. These results indicate a 75% relative improvement in localization performance following the implementation of OpenVLA-OFT, though further refinement is necessary to achieve higher rates of successful component identification.

Initial trials utilizing OpenVLA for RAM module approach yielded a success rate of 13 out of 20 attempts. Subsequent implementation of OpenVLA-OFT, incorporating optimization of the foundational model, improved performance to 16 successful approaches out of 20 attempts. This represents a measurable increase in the robot’s ability to accurately position its end-effector for RAM module manipulation, suggesting the efficacy of the OpenVLA-OFT methodology in refining approach trajectories and minimizing positional errors during the initial stages of disassembly.

Current system performance indicates that achieving complete disassembly task execution remains a significant challenge. While improvements have been made in component localization and manipulation, demonstrated by increased success rates in individual steps, integrated task completion currently achieves a 2 out of 10 success rate. This result is based on a hybrid control approach that combines Visual Language Automation (VLA) with lower-level robotic control. The low completion rate suggests limitations in the system’s ability to reliably sequence and coordinate all necessary actions for full disassembly, despite advancements in individual component handling.

Fine-tuning models involves a specific data processing pipeline leveraging VLA models to support experimental tasks.

The Future of Sustainable Robotics: Collaborative Disassembly and Beyond

Human-Robot Collaborative Disassembly (HRCD) represents a paradigm shift in end-of-life product management, strategically merging human cognitive skills with robotic capabilities. This approach recognizes that while robots excel at repetitive, precise tasks – like unscrewing or component localization – humans possess superior adaptability, problem-solving skills, and the ability to handle unforeseen variations during disassembly. By assigning tasks based on these complementary strengths, HRCD optimizes the entire process; robots handle the bulk of the physical work and maintain consistent quality, while human workers manage complex geometries, identify undocumented components, and respond to unexpected challenges. The result is a more efficient, flexible, and economically viable disassembly process, crucial for maximizing material recovery and minimizing the environmental footprint of manufactured goods.

Human-Robot Collaborative Disassembly (HRCD) demonstrably enhances material recovery through a streamlined process. By uniting human adaptability with robotic precision, disassembly lines achieve significantly higher rates of component separation and sorting than traditional methods. This optimization isn’t merely about speed; it directly minimizes material waste by enabling the recovery of a broader spectrum of materials, including those previously considered economically unviable to separate. Consequently, HRCD systems contribute to a reduced environmental impact by lessening the demand for virgin resources and diminishing the volume of electronic waste destined for landfills. Studies indicate a potential increase in material recovery of up to 40% with fully implemented HRCD systems, paving the way for a more sustainable and circular lifecycle for complex products.

Intelligent algorithms are increasingly pivotal in the practice of selective disassembly, a process that moves beyond simple deconstruction to strategically recover valuable components from end-of-life products. These algorithms analyze product composition, material properties, and current market values to prioritize the extraction of materials and parts with the highest economic potential. This targeted approach not only maximizes the return on investment for disassembly operations but also actively supports the principles of a circular economy by ensuring that critical resources are efficiently reclaimed and reintroduced into the manufacturing cycle. By focusing on high-value components, selective disassembly minimizes waste, reduces reliance on virgin materials, and contributes to a more sustainable and resource-efficient industrial system, offering a compelling economic incentive for environmentally responsible practices.

The study demonstrates a compelling, if imperfect, approach to robotic disassembly, highlighting the inherent complexities of translating high-level instructions into precise physical actions. This echoes Bertrand Russell’s observation: “The difficulty lies not so much in developing new ideas as in escaping from old ones.” The reliance on pre-trained Vision-Language-Action models, while innovative, necessitates overcoming ingrained limitations in data representation and control strategies. The research acknowledges the need to move beyond simply mimicking demonstrated behaviors-a ‘fragile’ solution-towards a more robust system capable of adapting to the nuances of real-world e-waste. true progress demands a willingness to fundamentally rethink established methods of robotic manipulation and control, embracing a holistic understanding of the disassembly process.

Beyond the Screwdriver

The pursuit of automated disassembly, as demonstrated by this work, reveals a fundamental truth: replicating dexterity is not the same as understanding the architecture of the device itself. One can train a system to mimic the motion of component extraction, but without a deeper comprehension of the interconnectedness – the electrical pathways, the mechanical dependencies – the system remains fragile. It is akin to replacing a heart without understanding the circulatory system; a local fix, destined to create new imbalances. The limitations observed, particularly regarding precision and data coverage, are not merely technical hurdles, but symptoms of a larger challenge: representing knowledge, not just perception.

Future efforts must move beyond end-to-end learning as a singular solution. The integration of symbolic reasoning, or at least a more structured representation of component relationships, seems crucial. The current paradigm prioritizes replicating how things are done, while neglecting why. A robust system will require a hybrid approach-one that blends the adaptability of learned models with the reliability of pre-defined constraints. LoRA fine-tuning is a valuable tool, but it’s a refinement, not a revolution.

Ultimately, the true measure of success will not be the speed of disassembly, but the generalizability of the approach. Can a system trained on desktop computers adapt to servers, laptops, or even entirely different classes of electronic waste? Until the focus shifts from replicating individual actions to understanding systemic function, automated disassembly will remain a clever imitation, rather than a truly intelligent solution.

Original article: https://arxiv.org/pdf/2512.04446.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/