Robotics Breakthrough: AI Enables โคrobots to โMasterโ Deformable Objectโฃ Manipulation
Table of Contents
- Robotics Breakthrough: AI Enables โคrobots to โMasterโ Deformable Objectโฃ Manipulation
Seoul, โขSouth Korea – A team of researchers at the Korea Advanced Instituteโค of Science and Technology (KAIST) has achieved a significant milestone in robotics, developing an artificial intelligence system that allows robotsโ to skillfully handle and manipulate deformable objects-a task historically challenging for automated systems. This innovation promises to reshape industrialโข automation, moving beyond rigid structures โto encompass tasks involving โflexible materials like rubber and wiring.
The Challenge of Deformable Objects
Robots traditionally excel at working with precisely defined,rigid objects. though, dealingโข with deformable objects-those that change shape unpredictably-presents a complex challenge. These objects lack a fixed form, making it difficult for robots โคto recognize and interact โขwithโข them effectively.โฃ The team atโค KAIST addressed โคthis hurdle with a novel approach called INR-DOM โค(Implicit Neural Portrayal for Deformable Object Manipulation).
Introducing INR-DOM: A New Approach โto Robotic Manipulation
INR-DOM leverages “Implicit Neural Representation” to enable robots โฃto essentially “imagine” the complete shape โof an object, even when only partial information is available.This allowsโ for a more intuitive and precise manipulation capability. The system employs a two-stage learning structure, โฃcombining reinforcement and contrast learning to โคmaster complex operations.Thisโ study showed the possibility that the robot could understand theโ entire deformation object with incomplete information andโฃ perform complexโ manipulation โฃbased on it,
stated Songโค Min-seok, the studyS first author.
Did โฃYou Know? โคTraditional robotic vision systems struggle with deformable objects as they rely on identifying fixedโฃ features. INR-DOM overcomes this limitation by predicting the object’s complete โขform, even when obscured.
extraordinary โคResults in Simulation and Real-World โคTesting
Extensive experiments demonstrated the effectiveness of INR-DOM. In โคsimulated environments, the robot equipped with this technologyโ outperformed existing โmethodsโ in complex tasks such as inserting rubber rings, installing O-rings, and untangling twisted โrubber โbands. Notably, โขit achieved a 75% success rate inโ the “Disentanglement” task, a 49% advancement over previous technologies (which achieved a 26% success rate).
The system’s capabilities wereโ further validated in real-world experiments involving wiring and twisting tasks, achieving a success rate โexceeding 90%. This demonstrates the technology’s โขpotential for practicalโข submission in industrial settings.
Performance Metrics
| Task | INR-DOM Success Rate | Previous Technology Success Rate |
|---|---|---|
| Rubberโ Ringing | 95% | 80% |
| O-Ring Installation | 92% | 78% |
| Disentanglement | 75% | 26% |
| Wiring | 91% | 85% |
Pro Tip: Implicit Neural Representations are a โpowerful tool forโ representing complex data, allowing robots to learn and generalize more effectively.
Implications for theโฃ Future of Automation
The growth of INR-DOM โrepresents a significant stepโ toward more versatile and intelligent automation. Thisโ technology could be applied in โฃa wide range of industries, including manufacturing,โค logistics, and โขeven healthcare.What newโ applications canโฃ you envisionโข for robots capable of handling deformable objects withโฃ such precision?
The research, ledโ by โคPark Dae-hyung, was presented at the Robotics: Science and Systems (RSS) 2025โฃ conference, held at the University of Southern californiaโข (USC) in los Angeles from june 21-25.The study’s findings are considered a key advancement in enabling robots to interact with theโข complex, often unpredictable, world around us. โค The research builds upon โขthe principles of neural implicit representations,as explored in works like “NeRF: Representing Scenes as Neural Radianceโ fields for View Synthesis” (Mildenhallโ 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 trendsโข include the development of soft robotics, which utilizes flexible materials to create robots that are more adaptableโ and โคsafe for โhuman interaction, and โthe integrationโฃ of tactile sensing, allowing robots to “feel” their environment โand adjust their grip accordingly. The demand for robots capable of handling a wider range of tasks, includingโค those involving deformable objects, is expected to โคcontinue growing as automation becomes more prevalent in various industries.
Frequently Asked Questions
- Whatโ is INR-DOM? INR-DOMโ (Implicitโ Neural Representation for Deformable Object Manipulation) is an AIโค technology that enables robots โto 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? โฃ Applications include manufacturing, logistics, healthcare, and any industry requiring the handling of โflexible materials.
- what was the success rate of INR-DOM in the disentanglement task? โINR-DOM achieved a 75% success rate in the disentanglement task, substantially higher than the 26% rate of โexisting technologies.
- Whereโ was thisโ research presented? The research was presented at the Robotics: Science and Systemsโฃ (RSS) 2025 conference at โUSC in Losโฃ Angeles.
We’re excited โขto see how this technology will shape the future of robotics! โ Shareโค this article with your network, and โฃlet us know your thoughts in โขthe comments below. Don’t forget to subscribe to World Today news for the latest breakthroughs in science and technology.
