07-01-2011, 02:49 PM
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Submitted by AKHIL
ABSTRACT
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.
INTRODUCTION
ROBOTS
A robot is a mechanical or virtual, artificial agent. It is usually an electromechanical system, which, by its appearance or movements, conveys a sense that it has intent or agency of its own.
A typical robot will have several, though not necessarily all of the following properties:
• Is not 'natural' i.e. has been artificially created.
• Can sense its environment.
• Can manipulate things in its environment.
• Has some degree of intelligence or ability to make choices based on the environment or automatic control / pre-programmed sequence.
• Is programmable.
• Can move with one or more axes of rotation or translation.
• Can make dexterous coordinated movements.
• Appears to have intent or agency (reification, anthropomorphisation or
Pathetic fallacy).
Robotic systems are of growing interest because of their many practical applications as well as their ability to help understand human and animal behavior, cognition, and physical performance. Although industrial robots have long been used for repetitive tasks in structured environments, one of the long-standing challenges is achieving robust performance under uncertainty. Most robotic systems use a manually constructed mathematical model that captures the robot’s dynamics and is then used to plan actions. Although some parametric identification methods exist for automatically improving these models, making accurate models is difficult for complex machines, especially when trying to account for possible topological changes
to the body, such as changes resulting from damage.
ERROR RECOVERY
Recovery from error, failure or damage is a major concern in robotics. A majority of effort in programming automated systems is dedicated to error recovery. The need for automated error recovery is even more acute in the field of remote robotics, where human operators cannot manually repair or provide compensation for damage or failure.
Here, its explained how the four legged robot automatically synthesizes a predictive model of its own topology (where and how its body parts are connected) through limited yet self-directed interaction with its environment, and then uses this model to synthesize successful new locomotive behaviour before and after damage. These findings may help develop more robust robotics, as well as shed light on the relation between curiosity and cognition in animals and humans.
SELF HEALING OR SELF MODELLING ROBOTS
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 self- directed 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.
