18-07-2011, 12:39 PM
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1. INTRODUCTION
1.1 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.
1.2. 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.
Fig 1.1 Robot
2. 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.
2.1 Researchers
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.
2.2 The Starfish Robot
2.2.1 Characterizing the Target System
The target system in this study is a quadrupedal, articulated robot with eight actuated degrees of freedom. The robot consists of a rectangular body and four legs attached to it with hinge joints on each of the four sides of the robot’s body. Each leg in turn is composed of an upper and lower leg, attached together with a hinge joint. All eight hinge joints of the robot are actuated with Airtronics 94359 high torque servomotors. However, in the current study, the robot was simplified by assuming that the knee joints are frozen: all four legs are held straight when the robot is commanded to perform some action. The following table gives the overall dimensions of the robot’s parts.
1. INTRODUCTION
1.1 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.
1.2. 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.
Fig 1.1 Robot
2. 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.
2.1 Researchers
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.
2.2 The Starfish Robot
2.2.1 Characterizing the Target System
The target system in this study is a quadrupedal, articulated robot with eight actuated degrees of freedom. The robot consists of a rectangular body and four legs attached to it with hinge joints on each of the four sides of the robot’s body. Each leg in turn is composed of an upper and lower leg, attached together with a hinge joint. All eight hinge joints of the robot are actuated with Airtronics 94359 high torque servomotors. However, in the current study, the robot was simplified by assuming that the knee joints are frozen: all four legs are held straight when the robot is commanded to perform some action. The following table gives the overall dimensions of the robot’s parts.
