Robotics Breakthrough: AI Enables robots to Master Deformable Object Manipulation
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Seoul, South Korea – A team of researchers at the Korea Advanced Institute of Science and Technology (KAIST) has achieved a significant milestone in robotics, developing an artificial intelligence system that allows robots to skillfully handle and manipulate deformable objects-a task historically challenging for automated systems. This innovation promises to reshape industrial automation, moving beyond rigid structures to encompass tasks involving flexible materials like rubber and wiring.
The Challenge of Deformable Objects
Robots traditionally excel at working with precisely defined,rigid objects. though, dealing with deformable objects-those that change shape unpredictably-presents a complex challenge. These objects lack a fixed form, making it difficult for robots to recognize and interact with them effectively. The team at KAIST addressed this hurdle with a novel approach called INR-DOM (Implicit Neural Portrayal for Deformable Object Manipulation).
Introducing INR-DOM: A New Approach to Robotic Manipulation
INR-DOM leverages “Implicit Neural Representation” to enable robots to essentially “imagine” the complete shape of an object, even when only partial information is available.This allows for a more intuitive and precise manipulation capability. The system employs a two-stage learning structure, combining reinforcement and contrast learning to master complex operations.This study showed the possibility that the robot could understand the entire deformation object with incomplete information and perform complex manipulation based on it,
stated Song Min-seok, the studyS first author.
Did You Know? Traditional robotic vision systems struggle with deformable objects as they rely on identifying fixed features. INR-DOM overcomes this limitation by predicting the object’s complete form, even when obscured.
extraordinary Results in Simulation and Real-World Testing
Extensive experiments demonstrated the effectiveness of INR-DOM. In simulated environments, the robot equipped with this technology outperformed existing methods in complex tasks such as inserting rubber rings, installing O-rings, and untangling twisted rubber bands. Notably, it achieved a 75% success rate in the “Disentanglement” task, a 49% advancement over previous technologies (which achieved a 26% success rate).
The system’s capabilities were further validated in real-world experiments involving wiring and twisting tasks, achieving a success rate exceeding 90%. This demonstrates the technology’s potential for practical submission in industrial settings.
Performance Metrics
| Task | INR-DOM Success Rate | Previous Technology Success Rate |
|---|---|---|
| Rubber Ringing | 95% | 80% |
| O-Ring Installation | 92% | 78% |
| Disentanglement | 75% | 26% |
| Wiring | 91% | 85% |
Pro Tip: Implicit Neural Representations are a powerful tool for representing complex data, allowing robots to learn and generalize more effectively.
Implications for the Future of Automation
The growth of INR-DOM represents a significant step toward more versatile and intelligent automation. This technology could be applied in a wide range of industries, including manufacturing, logistics, and even healthcare.What new applications can you envision for robots capable of handling deformable objects with such precision?
The research, led by Park Dae-hyung, was presented at the Robotics: Science and Systems (RSS) 2025 conference, held at the University of Southern california (USC) in los Angeles from june 21-25.The study’s findings are considered a key advancement in enabling robots to interact with the complex, often unpredictable, world around us. The research builds upon the principles of neural implicit representations,as explored in works like “NeRF: Representing Scenes as Neural Radiance fields for View Synthesis” (Mildenhall et al.,2020).
Looking Ahead: Trends in Robotic Manipulation
The field of robotic manipulation is rapidly evolving, driven by advancements in AI, sensor technology, and materials science. Key trends include the development of soft robotics, which utilizes flexible materials to create robots that are more adaptable and safe for human interaction, and the integration of tactile sensing, allowing robots to “feel” their environment and adjust their grip accordingly. The demand for robots capable of handling a wider range of tasks, including those involving deformable objects, is expected to continue growing as automation becomes more prevalent in various industries.
Frequently Asked Questions
- What is INR-DOM? INR-DOM (Implicit Neural Representation for Deformable Object Manipulation) is an AI technology that enables robots to understand and manipulate deformable objects by predicting their complete shape.
- How does INR-DOM improve robotic manipulation? It allows robots to work with objects that change shape, overcoming a major limitation of traditional robotic systems.
- What are some potential applications of this technology? Applications include manufacturing, logistics, healthcare, and any industry requiring the handling of flexible materials.
- what was the success rate of INR-DOM in the disentanglement task? INR-DOM achieved a 75% success rate in the disentanglement task, substantially higher than the 26% rate of existing technologies.
- Where was this research presented? The research was presented at the Robotics: Science and Systems (RSS) 2025 conference at USC in Los Angeles.
We’re excited to see how this technology will shape the future of robotics! Share this article with your network, and let us know your thoughts in the comments below. Don’t forget to subscribe to World Today news for the latest breakthroughs in science and technology.
