CN105205533A - Development automatic machine with brain cognition mechanism and learning method of development automatic machine - Google Patents
Development automatic machine with brain cognition mechanism and learning method of development automatic machine Download PDFInfo
- Publication number
- CN105205533A CN105205533A CN201510628233.0A CN201510628233A CN105205533A CN 105205533 A CN105205533 A CN 105205533A CN 201510628233 A CN201510628233 A CN 201510628233A CN 105205533 A CN105205533 A CN 105205533A
- Authority
- CN
- China
- Prior art keywords
- orientation
- learning
- internal
- action
- state
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 36
- 230000007246 mechanism Effects 0.000 title claims abstract description 34
- 210000004556 brain Anatomy 0.000 title claims abstract description 15
- 238000011161 development Methods 0.000 title abstract description 14
- 230000019771 cognition Effects 0.000 title abstract 2
- 230000006870 function Effects 0.000 claims abstract description 34
- 230000009471 action Effects 0.000 claims abstract description 31
- 230000008450 motivation Effects 0.000 claims abstract description 15
- 230000006399 behavior Effects 0.000 claims abstract description 14
- VYFYYTLLBUKUHU-UHFFFAOYSA-N dopamine Chemical compound NCCC1=CC=C(O)C(O)=C1 VYFYYTLLBUKUHU-UHFFFAOYSA-N 0.000 claims abstract description 14
- 230000008569 process Effects 0.000 claims abstract description 14
- 230000004044 response Effects 0.000 claims abstract description 10
- 229960003638 dopamine Drugs 0.000 claims abstract description 7
- 238000011156 evaluation Methods 0.000 claims description 28
- 238000002474 experimental method Methods 0.000 claims description 14
- 230000001149 cognitive effect Effects 0.000 claims description 12
- 210000003710 cerebral cortex Anatomy 0.000 claims description 9
- 230000000875 corresponding effect Effects 0.000 claims description 9
- 210000001577 neostriatum Anatomy 0.000 claims description 8
- 230000033001 locomotion Effects 0.000 claims description 7
- 238000013459 approach Methods 0.000 claims description 6
- 239000011159 matrix material Substances 0.000 claims description 6
- 210000000337 motor cortex Anatomy 0.000 claims description 6
- 230000007704 transition Effects 0.000 claims description 6
- 241001573498 Compacta Species 0.000 claims description 4
- 210000001638 cerebellum Anatomy 0.000 claims description 4
- 230000003291 dopaminomimetic effect Effects 0.000 claims description 4
- 230000001953 sensory effect Effects 0.000 claims description 4
- 210000003523 substantia nigra Anatomy 0.000 claims description 4
- 210000001103 thalamus Anatomy 0.000 claims description 4
- 230000000977 initiatory effect Effects 0.000 claims description 3
- 241000764238 Isis Species 0.000 claims description 2
- 210000004227 basal ganglia Anatomy 0.000 claims description 2
- 230000002490 cerebral effect Effects 0.000 claims description 2
- 230000035807 sensation Effects 0.000 claims description 2
- 210000000225 synapse Anatomy 0.000 claims description 2
- 230000000542 thalamic effect Effects 0.000 claims description 2
- 230000018109 developmental process Effects 0.000 description 8
- 241001465754 Metazoa Species 0.000 description 7
- 238000004088 simulation Methods 0.000 description 5
- 238000004422 calculation algorithm Methods 0.000 description 4
- 238000011160 research Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000036630 mental development Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000003542 behavioural effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 239000003795 chemical substances by application Substances 0.000 description 1
- 230000003930 cognitive ability Effects 0.000 description 1
- 230000008133 cognitive development Effects 0.000 description 1
- 230000001143 conditioned effect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 239000012636 effector Substances 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 210000001320 hippocampus Anatomy 0.000 description 1
- 210000003016 hypothalamus Anatomy 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 230000003923 mental ability Effects 0.000 description 1
- 210000000653 nervous system Anatomy 0.000 description 1
- 230000000926 neurological effect Effects 0.000 description 1
- 239000011664 nicotinic acid Substances 0.000 description 1
- 230000002360 prefrontal effect Effects 0.000 description 1
- 230000011514 reflex Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 230000008054 signal transmission Effects 0.000 description 1
- 210000000278 spinal cord Anatomy 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Landscapes
- Feedback Control In General (AREA)
Abstract
The invention relates to a development automatic machine with a brain cognition mechanism and a learning method of the development automatic machine, and belongs to the technical field of intelligent robots. The development automatic machine comprises an inner state set, a system output set, an inner operation behavior set, a state transfer equation, a reward signal, a system evaluating function, a system action selecting probability and a Dopamine response differential signal. By means of the development automatic machine and the learning method, a mathematic model high in generalization ability and wide in application range is provided for the system autonomous development process with the learning automatic machine as the basic frame; by means of the method, a sensorimotor system and an intrinsic motivation mechanism are combined, the self-learning and self-adaption capacity of the system is improved, and the intelligence in the true sense is achieved.
Description
Technical Field
The invention relates to a development automaton with brain cognitive mechanism and a learning method thereof, belonging to the technical field of intelligent robots.
Technical Field
The learning and memory are the essence of the intelligent behaviors of human and animals, and various skills of the human and animals are gradually formed and developed in the process that the nervous system of the human and animals is self-learned and self-organized, so that the nervous activity and the self-regulation mechanism of the human and animals are learned and simulated and are endowed to the intelligent robot, and the learning and memory method is an important research subject of artificial intelligence and control science.
In 1996, j.weng first proposed a robot autonomous mental development concept, who thought that an agent should develop mental ability under the control of an intrinsic development program through interaction of sensors and effectors with an unknown environment on the basis of a human brain simulation. Brooks et al emphasize that interactive learning of robots with teachers and environments gradually develops the intelligence of the robots, and propose a calculation model simulating the areas of prefrontal lobes, hypothalamus, hippocampus and the like in the cerebral cortex of human and animals by combining the research theory of neuroscience to process complex problems in complex environments, which also relates to a sensorimotor system. The initial cognitive development begins with the development and development of the coordinated mechanisms of the sensorimotor system, which is in turn constantly coordinated and refined during the process of intrinsic motivation development and development. The literature of neurological relevance shows that during the learning process of human and animal, the cerebral cortex, basal nucleus and cerebellum work in parallel in a self-specific way, and in the correlation of human and animal movement, the cerebellum and basal nucleus are distributed on two sides of the route of motor signal transmission between the cerebral cortex and the spinal cord, and participate in the initiation and control of any behavioral action.
Related patents such as the invention patent of application No. CN200910086990.4 are based on the theory of automata, and propose to operate an automaton model and apply the model to autonomous learning control of a robot. The patent with application number CN201310656943.5 applies the principle of conditioned reflex operation to the field of image processing, which effectively improves the accuracy and speed of image processing by the system. The patent with the application number of 201410101272.0 mainly aims at the problems that the traditional robot is low in learning efficiency, poor in adaptability and the like, provides a bionic intelligent control method, and effectively improves the intelligent level of the robot. Application number 201410163756.8 provides an autonomous mental development cloud robot system based on cloud computing, which can effectively reduce the burden of the robot in executing operation-intensive tasks and can also realize the sharing of knowledge among different robots. However, the above patents do not relate to a learning system that simulates the cognitive mechanisms of the human brain.
Disclosure of Invention
Aiming at the technical problems, the invention takes a biological sensorimotor system as a theoretical basis and introduces an internal motivation mechanism in psychology to drive learning, provides a developmental automaton with a brain cognitive mechanism and a learning method thereof, and improves the autonomous developmental cognitive ability of a robot.
The developmental automaton with the brain cognitive mechanism comprises an internal state set, a system output set, an internal operation behavior set, a state transition equation, a reward signal, a system evaluation function, a system action selection probability and a dopamine response difference signal;
（1）represented as a finite set of internal states, corresponding to the sensory cortex in the cerebral cortex,is shown asIn one of the states, the state of the mobile terminal,the number of internal states.
（2）Represented as a set of system outputs, corresponding to the motor cortex in the cerebral cortex,is shown asThe output of the first and second processors is,indicating the number of outputs.
（3）Represented as a set of internal operating behaviors, corresponding to the cerebellar regions,is as followsThe internal action of the one or more internal actions,the number of internal actions.
（4）Is a state transition equation, i.e.State of the momentByState of the momentAnd operational behaviorJoint decisions are generally determined by an environment or model.
（5）Is shown as a systemThe internal state of the moment isIs the internal operational action takenPost-transition to stateThe latter reward signal, relative to the thalamus which gives rise to the thalamus sensation.
(6) The input signal in the cerebral cortex contains two parts, sensory and motor cortex information, respectively, as inputs to the striatum, and thus:
（1）
the striated corpuscle is mainly an evaluation mechanism for predicting the orientation of the action of the organism, and further is an evaluation mechanism for predicting the orientation of an intrinsic motivation mechanism, and a system evaluation function is defined as follows:
（2）
wherein,is a discount factor; due to the existence of an internal motivation mechanism, the evaluation function of the systemGradually approaches to 0, thereby ensuring that the system is finally in a stable state; definition ofIs the orientation core in the intrinsic motivation mechanism, and the main function is to guide the autonomous cognitive direction; defining orientation kernelsIn the range ofThe function value of the best orientation and the function value of the worst orientation are obtained; then the motive orientation function definition within the striated corpuscle is shown in equation (3):
（3）
whereinFor the parameters of the orientation function, the difference of the orientation functions at two adjacent time instants is defined asTo determine the degree of orientation of the system, ifDescription of the inventionTime ratioThe orientation value at the moment is large, and vice versaDescription of the inventionTime ratioThe orientation value at the time is small.
(7) During the learning process of the basal ganglia, the matrix in the striatum is mainly the action selection function; one of the most important features in the learning process driven by the intrinsic motivation mechanism is to select the execution action according to the probability magnitude; adopting Boltzmann probability rules to realize the behavior selection function of the matrix, thereby realizing the probability selection mechanism of the learning automaton; firstly, defining:
（4）
the system action selection probability output for the striatal stroma, as defined in equation (4), is expressed by equation (5):
（5）
wherein,is a temperature constant, represents the degree of randomness of the selection of the motion,the greater the degree of action selection, and converselySmaller indicates less degree of action selection; when in useGradually approaches zero, thenThe corresponding action selection probability gradually approaches to 1, and the system is provided withThe value of (A) is gradually reduced along with the time, which represents that the system experiences the gradual increase of knowledge in the learning process, andgradually evolving from an unstable system to a stable system;
(8) the dopaminergic released by the substantia nigra pars compacta can be used as a guiding signal for the assessment of an action for improving the expression of a behavior resulting in a maximum future reward for the action, in order to obtain a more accurate execution of the action; in thatThe evaluation function of the time determined by the striated bodies is:
（6）
combining equation (2) and equation (6) yields equation (7):
（7）
this shows thatTime of day, evaluation functionCan useEvaluation function of timeBut the evaluation value is used due to the influence of the error existing in the initial stage of predictionTo representIs not equal to the actual value, so that reward messages output by the thalamic output and the striatum need to be processed in the substantia nigra pars compacta and release dopaminergic energyTo adjust the table of evaluation values, the dopamine response differential signal is represented by equation (8):
（8）。
the learning method of the developmental automata with the brain cognitive mechanism comprises the following steps:
(1) initialization: initial value of iterative learning step numberThe number of iterative learning isInitializing each parameter and synapse weight, wherein the probability of executing initial internal operation behavior is the same when the experiment starts;
(2) sensing a current state；
(3) Computing an evaluation function in striated bodiesDue to the existence of intrinsic motivation mechanisms, according to the presentValue of (a) to calculate an orientation function；
(4) Calculating the behavior selection probability of striatum matrix according to the orientation according to a formulaAnd actions performed by the cerebellum；
(5) According to the state transition equation, the state is represented by；
(6) Immediate reward from thalamusAnd initiating a dopamine response adjustment evaluation value;
(7) output of motion from the motor cortex of the brain；
(8) Repeating the steps (2) to (7) until the(ii) a And finishing the learning.
Compared with the prior art, the development automaton with the brain cognitive mechanism and the learning method thereof provided by the invention provide a mathematical model with strong generalization capability and wide application range for the system autonomous development process by taking the learning automaton as a basic framework; secondly, the method combines a sensory-motor system and an internal motivation mechanism, improves the self-learning and self-adaptive capacity of the system, and realizes the intelligence in the true sense.
Drawings
FIG. 1 is a block diagram of the system of the present invention;
FIG. 2 is a flow chart of the learning process of the present invention;
FIG. 3 is a response curve of the balance control states of the two-wheeled robot according to the embodiment;
FIG. 4 is a simulation curve of the balance control evaluation function and error of the two-wheeled robot according to the embodiment;
FIG. 5 shows simulation results of the anti-interference test of the embodiment;
FIG. 6 is a graph comparing evaluation function curves of the learning method of the embodiment and the conventional learning automaton method;
FIG. 7 is a graph comparing error curves of the learning method of the embodiment with those of the conventional learning automaton method.
Detailed Description
The invention is further described with reference to the following figures and detailed description.
In the embodiment of two-wheeled robot, the system structure is shown in fig. 1, and learning is performed according to the flow of steps shown in fig. 2.
Aiming at an incomplete two-wheel self-balancing robot, the system is an intrinsic unstable system, and before various motions are realized, the robot is ensured to be capable of keeping self balance, so the posture balance of the two-wheel robot is the primary condition for motion control. In order to verify the effectiveness, robustness and superiority of the developmental automaton with brain cognitive mechanism provided by the invention, the embodiment uses two rounds of robots as objects to research how the robots finally learn the motor skills through autonomous learning in an unknown environment.
The robot has four outputs in the experimental processThe output quantity meets the corresponding condition, namely the angular speed of the left and the right wheelsAndare all less than 3.489rad/s, and the self-inclination angle of the fuselagerad and robot pendulum angular velocityrad/s. Discount factorThe sampling time was 0.01 s. In each experiment, when the number of attempts of the robot exceeds 1000 or the number of equilibrium steps of one attempt exceeds 20000 steps, the learning of the robot is stopped and another experiment is restarted. If the robot is able to maintain balance after 20000 steps in one of the attempts, the robot is considered to have learned the skill of balance control. After each experiment fails, the initial state and each weight value are reset to random values within a certain range, and then the learning is carried out again.
Experiment 1: balance control experiment
Under the unknown environment without interference, the robot adopts the method provided by the invention, through continuous learning, 42 times of trial and 43 times of trial, the balance control skill is learned about 220 seconds, namely about 2.2 seconds, the robot shows the faster autonomous learning capability and the effectiveness of the invention, and the response curve of each state quantity of the first 3000 steps and the evaluation function and error simulation curve in the simulation result are shown in fig. 3 and 4.
Experiment 2: anti-interference experiment
In the actual operation process of the system, the input and output signals are more or less interfered by external noise, or the inaccuracy of the detection device can cause a certain error of the state quantity. Then, in order to simulate the actual environment, when the robot has learned the balance control and keeps 9800 steps, a pulse signal with the amplitude of 25 is added to each input state quantity, and if the robot can endure the interference of the pulse signal and keep balance, the experiment is considered to be successful and the invention is proved to have certain robustness. Fig. 5 shows the output response of each state after adding the pulse signal, and it can be seen that after 200 steps, i.e. 2s or so, the robot reaches the equilibrium position again.
Experiment 3: comparison experiment of the present embodiment and the conventional learning automata
The invention introduces an internal motivation mechanism to drive the robot to independently learn, thereby being beneficial to reducing the error of the system and improving the convergence rate of the algorithm. In order to prove the superiority of the invention, a balance control experiment is carried out on the two-wheeled robot by respectively applying the traditional learning automata algorithm and the invention, and the experimental result is analyzed. The parameters of the two algorithms are set to be the same in the experiment, and fig. 6 and 7 are comparison graphs of evaluation functions and error curves of the two algorithms in the first 2000 steps. It can be seen from fig. 6 that the learning of the balance control skill is completed in about 220 steps, i.e., 2.2s, while the learning is completed in about 600 steps, i.e., 6s, by the conventional learning automaton method, which proves that the convergence rate of the present invention is superior to that of the conventional learning automaton method. Fig. 7 shows that the error amplitude of the invention is superior to that of the traditional learning automaton method, and is more beneficial to the stability of the system.
Claims (2)
1. A developmental automaton with brain cognitive mechanisms, comprising: the method comprises an internal state set, a system output set, an internal operation behavior set, a state transition equation, a reward signal, a system evaluation function, a system action selection probability and a dopamine response difference signal;
（1）represented as a finite set of internal states, corresponding to the sensory cortex in the cerebral cortex,is shown asIn one of the states, the state of the mobile terminal,the number of internal states;
（2）represented as a set of system outputs, corresponding to the motor cortex in the cerebral cortex,is shown asThe output of the first and second processors is,representing the output number;
（3）represented as a set of internal operating behaviors, corresponding to the cerebellar regions,is as followsThe internal action of the one or more internal actions,the number of internal actions;
（4）is a state transition equation, i.e.State of the momentByState of the momentAnd operational behaviorJoint decisions, typically determined by environment or model;
（5）is shown as a systemThe internal state of the moment isIs the internal operational action takenPost-transition to stateA posterior reward signal, relative to the thalamus-initiated thalamus sensation;
(6) the input signal in the cerebral cortex contains two parts, sensory and motor cortex information, respectively, as inputs to the striatum, and thus:
（1）
the striated corpuscle is mainly an evaluation mechanism for predicting the orientation of the action of the organism, and further is an evaluation mechanism for predicting the orientation of an intrinsic motivation mechanism, and a system evaluation function is defined as follows:
（2）
wherein,is a discount factor; due to the existence of an internal motivation mechanism, the evaluation function of the systemGradually approaches to 0, thereby ensuring that the system is finally in a stable state; definition ofIs the orientation core in the intrinsic motivation mechanism, and the main function is to guide the autonomous cognitive direction; defining orientation kernelsIn the range ofThe function value of the best orientation and the function value of the worst orientation are obtained; then the motive orientation function definition within the striated corpuscle is shown in equation (3):
（3）
whereinDefining the orientation of two adjacent time instants as parameters of an orientation functionThe difference of the functions isTo determine the degree of orientation of the system, ifDescription of the inventionTime ratioThe orientation value at the moment is large, and vice versaDescription of the inventionTime ratioThe orientation value at the time is small;
(7) during the learning process of the basal ganglia, the matrix in the striatum is mainly the action selection function; one of the most important features in the learning process driven by the intrinsic motivation mechanism is to select the execution action according to the probability magnitude; adopting Boltzmann probability rules to realize the behavior selection function of the matrix, thereby realizing the probability selection mechanism of the learning automaton; firstly, defining:
（4）
the system action selection probability output for the striatal stroma, as defined in equation (4), is expressed by equation (5):
（5）
wherein,is a temperature constant, represents the degree of randomness of the selection of the motion,the greater the degree of action selection, and converselySmaller indicates less degree of action selection; when in useGradually approaches zero, thenThe corresponding action selection probability gradually approaches to 1, and the system is provided withThe numerical value of (A) is gradually reduced along with time, which represents that the system experiences gradually increased knowledge in the learning process and gradually evolves from an unstable system to a stable system;
(8) the dopaminergic released by the substantia nigra pars compacta can be used as a guiding signal for the assessment of an action for improving the expression of a behavior resulting in a maximum future reward for the action, in order to obtain a more accurate execution of the action; in thatThe evaluation function of the time determined by the striated bodies is:
（6）
combining equation (2) and equation (6) yields equation (7):
（7）
this shows thatTime of day, evaluation functionCan useEvaluation function of timeBut the evaluation value is used due to the influence of the error existing in the initial stage of predictionTo representIs not equal to the actual value, so that reward messages output by the thalamic output and the striatum need to be processed in the substantia nigra pars compacta and release dopaminergic energyTo adjust the table of evaluation values, the dopamine response differential signal is represented by equation (8):
（8）。
2. a developmental automaton with brain cognitive mechanism according to claim 1, characterized by: the learning method comprises the following steps:
(1) initialization: iterative learningInitial value of step numberThe number of iterative learning isInitializing each parameter and synapse weight, wherein the probability of executing initial internal operation behavior is the same when the experiment starts;
(2) sensing a current state；
(3) Computing an evaluation function in striated bodiesDue to the existence of intrinsic motivation mechanisms, according to the presentValue of (a) to calculate an orientation function；
(4) Calculating the behavior selection probability of striatum matrix according to the orientation according to a formulaAnd actions performed by the cerebellum；
(5) According to the state transition equation, the state is represented by；
(6) Immediate reward from thalamusAnd initiating a dopamine response adjustment evaluation value;
(7) output of motion from the motor cortex of the brain；
(8) Repeating the steps (2) to (7) until the(ii) a And finishing the learning.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510628233.0A CN105205533B (en) | 2015-09-29 | 2015-09-29 | Development automatic machine and its learning method with brain Mechanism of Cognition |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510628233.0A CN105205533B (en) | 2015-09-29 | 2015-09-29 | Development automatic machine and its learning method with brain Mechanism of Cognition |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105205533A true CN105205533A (en) | 2015-12-30 |
| CN105205533B CN105205533B (en) | 2018-01-05 |
Family
ID=54953202
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510628233.0A Expired - Fee Related CN105205533B (en) | 2015-09-29 | 2015-09-29 | Development automatic machine and its learning method with brain Mechanism of Cognition |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105205533B (en) |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105690392A (en) * | 2016-04-14 | 2016-06-22 | 苏州大学 | Robot motion control method and device based on actor-critic method |
| CN105824251A (en) * | 2016-05-18 | 2016-08-03 | 重庆邮电大学 | Bionic thermotactic behavior method based on neural network |
| CN106598058A (en) * | 2016-12-20 | 2017-04-26 | 华北理工大学 | Intrinsically motivated extreme learning machine autonomous development system and operating method thereof |
| CN107894715A (en) * | 2017-11-13 | 2018-04-10 | 华北理工大学 | The cognitive development method of robot pose path targetpath optimization |
| CN108646550A (en) * | 2018-04-03 | 2018-10-12 | 江苏江荣智能科技有限公司 | A kind of multiple agent formation method of Behavior-based control selection |
| CN109002887A (en) * | 2018-08-10 | 2018-12-14 | 华北理工大学 | The heuristic curiosity cognitive development system of biology and its operation method |
| CN109212975A (en) * | 2018-11-13 | 2019-01-15 | 北方工业大学 | A kind of perception action cognitive learning method with developmental mechanism |
| CN112558605A (en) * | 2020-12-06 | 2021-03-26 | 北京工业大学 | Robot behavior learning system based on striatum structure and learning method thereof |
| US11948092B2 (en) | 2021-05-25 | 2024-04-02 | Nanjing University Of Aeronautics And Astronautics | Brain-inspired cognitive learning method |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101599137A (en) * | 2009-07-15 | 2009-12-09 | 北京工业大学 | Autonomous operant conditioning reflex automat and the application in realizing intelligent behavior |
| US7668796B2 (en) * | 2006-01-06 | 2010-02-23 | The United States Of America As Represented By The Administrator Of The National Aeronautics And Space Administration | Automata learning algorithms and processes for providing more complete systems requirements specification by scenario generation, CSP-based syntax-oriented model construction, and R2D2C system requirements transformation |
| CN101673354A (en) * | 2009-06-12 | 2010-03-17 | 北京工业大学 | Operant conditioning reflex automatic machine and application thereof in control of biomimetic autonomous learning |
-
2015
- 2015-09-29 CN CN201510628233.0A patent/CN105205533B/en not_active Expired - Fee Related
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7668796B2 (en) * | 2006-01-06 | 2010-02-23 | The United States Of America As Represented By The Administrator Of The National Aeronautics And Space Administration | Automata learning algorithms and processes for providing more complete systems requirements specification by scenario generation, CSP-based syntax-oriented model construction, and R2D2C system requirements transformation |
| CN101673354A (en) * | 2009-06-12 | 2010-03-17 | 北京工业大学 | Operant conditioning reflex automatic machine and application thereof in control of biomimetic autonomous learning |
| CN101599137A (en) * | 2009-07-15 | 2009-12-09 | 北京工业大学 | Autonomous operant conditioning reflex automat and the application in realizing intelligent behavior |
Non-Patent Citations (3)
| Title |
|---|
| SHI TAO: "A study on autonomous learning mechanism of Cognitive robot", 《CONTROL AND DECISION CONFERENCE》 * |
| SHI TAO: "Research on robust bionic learning algorithm in balance control for the robot", 《CONTROL AND DECISION CONFERENCE》 * |
| 任红格: "一种内在动机驱动的FRBF网络自主学习算法", 《河北联合大学学报（自然科学版）》 * |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105690392A (en) * | 2016-04-14 | 2016-06-22 | 苏州大学 | Robot motion control method and device based on actor-critic method |
| CN105824251A (en) * | 2016-05-18 | 2016-08-03 | 重庆邮电大学 | Bionic thermotactic behavior method based on neural network |
| CN105824251B (en) * | 2016-05-18 | 2018-06-15 | 重庆邮电大学 | It is a kind of based on neural network it is bionical become warm behavioral approach |
| CN106598058A (en) * | 2016-12-20 | 2017-04-26 | 华北理工大学 | Intrinsically motivated extreme learning machine autonomous development system and operating method thereof |
| CN107894715A (en) * | 2017-11-13 | 2018-04-10 | 华北理工大学 | The cognitive development method of robot pose path targetpath optimization |
| CN108646550A (en) * | 2018-04-03 | 2018-10-12 | 江苏江荣智能科技有限公司 | A kind of multiple agent formation method of Behavior-based control selection |
| CN108646550B (en) * | 2018-04-03 | 2022-03-22 | 江苏江荣智能科技有限公司 | Multi-agent formation method based on behavior selection |
| CN109002887A (en) * | 2018-08-10 | 2018-12-14 | 华北理工大学 | The heuristic curiosity cognitive development system of biology and its operation method |
| CN109212975A (en) * | 2018-11-13 | 2019-01-15 | 北方工业大学 | A kind of perception action cognitive learning method with developmental mechanism |
| CN112558605A (en) * | 2020-12-06 | 2021-03-26 | 北京工业大学 | Robot behavior learning system based on striatum structure and learning method thereof |
| US11948092B2 (en) | 2021-05-25 | 2024-04-02 | Nanjing University Of Aeronautics And Astronautics | Brain-inspired cognitive learning method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105205533B (en) | 2018-01-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105205533A (en) | Development automatic machine with brain cognition mechanism and learning method of development automatic machine | |
| Paredes-Vallés et al. | Unsupervised learning of a hierarchical spiking neural network for optical flow estimation: From events to global motion perception | |
| Milde et al. | Obstacle avoidance and target acquisition for robot navigation using a mixed signal analog/digital neuromorphic processing system | |
| Galluppi et al. | Event-based neural computing on an autonomous mobile platform | |
| US20150127149A1 (en) | Apparatus and methods for online training of robots | |
| Zaidel et al. | Neuromorphic NEF-based inverse kinematics and PID control | |
| CN106598058A (en) | Intrinsically motivated extreme learning machine autonomous development system and operating method thereof | |
| Schillaci et al. | Random movement strategies in self-exploration for a humanoid robot | |
| Pretorius et al. | Simulating robots without conventional physics: A neural network approach | |
| CN107894715A (en) | The cognitive development method of robot pose path targetpath optimization | |
| Azimirad et al. | Experimental study of reinforcement learning in mobile robots through spiking architecture of thalamo-cortico-thalamic circuitry of mammalian brain | |
| Egbert et al. | Habit-Based Regulation Of Essential Variables. | |
| CN112405542A (en) | Musculoskeletal robot control method and system based on brain inspiring multitask learning | |
| Baladron et al. | The contribution of the basal ganglia and cerebellum to motor learning: A neuro-computational approach | |
| Tieck et al. | Learning target reaching motions with a robotic arm using brain-inspired dopamine modulated STDP | |
| Lechner et al. | Neuronal circuit policies | |
| Atyabi et al. | Cooperative area extension of PSO-transfer learning vs. uncertainty in a simulated swarm robotics | |
| Silva et al. | A case study on the scalability of online evolution of robotic controllers | |
| Azimirad et al. | Optimizing the parameters of spiking neural networks for mobile robot implementation | |
| Pretorius et al. | A neural network-based kinematic and light-perception simulator for simple robotic evolution | |
| Elmenreich et al. | Genetic evolution of a neural network for the autonomous control of a four-wheeled robot | |
| Schmidgall et al. | Synaptic motor adaptation: A three-factor learning rule for adaptive robotic control in spiking neural networks | |
| Ren et al. | A computational model of cognitive development for the motor skill learning from curiosity | |
| Liang et al. | Robust learning and recognition of visual patterns in neuromorphic electronic agents | |
| Salt | Optimising a neuromorphic locust looming detector for UAV obstacle avoidance |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20180105 Termination date: 20180929 |