Abstract of Self Healing Robots
When people or animals get hurt, they can usually compensate for minor injuries and keep limping along, but for robots, even slight damage can make them stumble and fall. Now a robot scarcely larger than a human hand has demonstrated a novel ability: It can recover from damage -- an innovation that could make robots more independent.
The new robot, which looks like a splay-legged, four-footed starfish, deduces the shape of its own body by performing a series of playful movements, swiveling its four limbs. By using sensors to record resulting changes in the angle of its body, it gradually generates a computerized image of itself. The robot then uses this to plan out how to walk forward.
The researchers hope similar robots will someday respond not only to damage to their own bodies but also to changes in the surrounding environment. Such responsiveness could lend autonomy to robotic explorers on other planets like Mars -- a helpful feature, since such robots can't always be in contact with human controllers on earth. Aside from practical value, the robot's abilities suggest a similarity to human thinking as the robot tries out various actions to figure out the shape of its world.
When people or animal get injured ,they compensate for minor injuries and keep limping along. But in the case of robots, even a slight injury can make them stumble and fall .Self healing robots have an ability to adapt to minor injuries and continue its job . A robot is able to indirectly infer its own morphology through selfdirected exploration and then use the resulting self-models to synthesize new behaviors.If the robot's topology unexpectedly changes, the same process restructures it's internal self-models, leading to the generation of qualitatively different, compensatory behavior. In essence, the process enables the robot to continuously diagnose and recover from damage. Unlike other approaches to damage recovery, the concept introduced here does not presuppose built-in redundancy, dedicated sensor arrays, or contingency plans designed for anticipated failures. Instead, our approach is based on the concept of multiple competing internal models and generation of actions to maximize disagreement between predictions of these models.
This research was done at the Computational Synthesis Lab at Cornell University. Team members are Josh Bongard, Viktor Zykov, and Hod Lipson. Josh Bongard was a postdoctoral researcher at Cornell while performing this research and since then moved to the University of Vermont where he is now an Assistant Professor. Victor Zykov is a Ph.D. student at CCSL, and Hod Lipson is an Assistant Professor at Cornell, and directs the Computational Synthesis Lab. This project was funded by the NASA Program on Intelligent Systems and by the National Science Foundation program in Engineering Design.