Robotic Architectures Lezione 8

Robotic architecture Computer architecture: Computer architecture:  discipline devoted to the design of highly specific and individual computers from a collection of common building blocks Robotic architecture: Robotic architecture:  design of highly specific and individual robots from a collection of common software building blocks.  provides a principled way of organizing a control system  describes a set of architectural components and how they interact  software systems and specifications that provide languages and tools for the construction of robots

Common features of behavior-based architectures There are many behavior-based architectures, but these architectures share many common features: There are many behavior-based architectures, but these architectures share many common features:  emphasis on importance of coupling sensing and action tightly  avoidance of representational symbolic knowledge  overall control system is divided into units But there are also differences: But there are also differences:  the granularity of behavioral decomposition  the basis of behavioral specification (ethological, situated activity, experimental)‏  the response encoding method (null, discrete, continuous)‏  the coordination method (competitive or cooperative)‏ programming methods, languages, reusability programming methods, languages, reusability

Behavior based architecture evaluation Support for parallelism? Support for parallelism? Hardware targetability? Mapping from SW to HW? Hardware targetability? Mapping from SW to HW? Niche targetability? Niche targetability? Support for modularity? Encapsulating behavior abstractions? Support for modularity? Encapsulating behavior abstractions? Robustness? Fault tolerance? Robustness? Fault tolerance? Timeliness in development? Development tools? Simulators? Timeliness in development? Development tools? Simulators? Run time flexibility? Run time flexibility? Performance effectiveness? Performance effectiveness?

Subsumption architecture Task-achieving behaviors in the subsumption architecture are represented as separate layers operating in parallel. Compare with the traditional approach. Task-achieving behaviors in the subsumption architecture are represented as separate layers operating in parallel. Compare with the traditional approach.

World Is Its Own Best Model This is a key principle of reactive systems and Subsumption Architecture: This is a key principle of reactive systems and Subsumption Architecture: Use the world as its own best model! Use the world as its own best model! If the world can provide the information directly (through sensing), it is best to get it that way, than to store it internally in a representation (which may be large, slow, expensive, and outdated). If the world can provide the information directly (through sensing), it is best to get it that way, than to store it internally in a representation (which may be large, slow, expensive, and outdated).

Designing in Subsumption Qualitatively specify the overall behavior needed for the task Qualitatively specify the overall behavior needed for the task Decompose that into specific and independent behaviors (layers)‏ Decompose that into specific and independent behaviors (layers)‏ The layers should be bottom-up and consisting of disjoint actions The layers should be bottom-up and consisting of disjoint actions Ground low-level behaviors in the robot’s sensors and effectors Ground low-level behaviors in the robot’s sensors and effectors Incrementally build, test, and add Incrementally build, test, and add

Arbitration in Subsumption Arbitration: deciding who has control Arbitration: deciding who has control Inhibition: prevents output signals from reaching effectors Inhibition: prevents output signals from reaching effectors Suppression: replaces input signal with the suppressing message Suppression: replaces input signal with the suppressing message The above two are the only mechanisms for coordination The above two are the only mechanisms for coordination => Results in priority-based arbitration => Results in priority-based arbitration  the rule or layer with higher priority takes over, i.e., has control of the AFSM

Example: SR diagram for a subsumption- based foraging robot

Subsumption the building blocks arbitrates among layers in an ad-hoc way the building blocks arbitrates among layers in an ad-hoc way More general behavior interactions More general behavior interactions

Subsumption Larger example -- Genghis Standing by tuning the parameters of two behaviors: the leg “swing” and the leg “lift” Standing by tuning the parameters of two behaviors: the leg “swing” and the leg “lift” Simple walking: one leg at a time Simple walking: one leg at a time Force Balancing: incorporated force sensors on the legs Force Balancing: incorporated force sensors on the legs Obstacle traversal: the legs should lift much higher if need be Obstacle traversal: the legs should lift much higher if need be Anticipation: handles touch sensors (whiskers) to detect obstacles Anticipation: handles touch sensors (whiskers) to detect obstacles Pitch stabilization: uses an inclinometer to stabilize fore/aft pitch Pitch stabilization: uses an inclinometer to stabilize fore/aft pitch Prowling: uses infrared sensors to start walking when a human approaches Prowling: uses infrared sensors to start walking when a human approaches Steering: uses the difference in two IR sensors to follow 57 FSM’s wired together ! Steering: uses the difference in two IR sensors to follow 57 FSM’s wired together !

Case Robot Herbert: subsumption architecture Developed by Jonathan Connell Developed by Jonathan Connell Herbert's mission is to collect empty soda cans. Herbert's mission is to collect empty soda cans. HW: N (max 24) x Hitachi 8bit microprocessor connectedwith 2-wire serial cables (300 baud)‏ HW: N (max 24) x Hitachi 8bit microprocessor connectedwith 2-wire serial cables (300 baud)‏ Controlling architecture is using the subsumption architecture Controlling architecture is using the subsumption architecture No central bus, no backplane, no blackboard No central bus, no backplane, no blackboard

Herbert

Part of Herbert’s behaviors

Subsumption Evaluation Strengths: Strengths:  Reactivity (speed, real-time nature)‏  Parallelism  Incremental design => robustness  Generality Weaknesses: Weaknesses:  Inflexibility at run-time  Expertise needed in design

Schema architecture Schema is a pattern of action, a basic unit of action from which complex actions are constructed Schema is a pattern of action, a basic unit of action from which complex actions are constructed Two kinds of schemas Two kinds of schemas  Motor schemas and  Perceptual schemas A schema stores both how to react and the way that reaction can be realized A schema stores both how to react and the way that reaction can be realized Provides a language for connecting action and perception Provides a language for connecting action and perception Activation levels are associated with schemas that determine their readiness or applicability for acting Activation levels are associated with schemas that determine their readiness or applicability for acting Many systems exists? e.g. Callisto - a foraging robot, GT Hummer Many systems exists? e.g. Callisto - a foraging robot, GT Hummer

What are Schemas? Schemas are Schemas are  functional units (intermediate between overall behavior and neural function) for analysis of cooperative competition in the brain  program units especially suited for a system which has continuing perception of, and interaction with, its environment  a programming language for new systems in computer vision, robotics and expert systems  a bridging language between Distributed AI and neural networks for specific subsystems

Perceptual And Motor Schemas A perceptual schema embodies the process whereby the system determines whether a given domain of interaction is present in the environment. A perceptual schema embodies the process whereby the system determines whether a given domain of interaction is present in the environment. A schema assemblage combines an estimate of environmental state with a representation of goals and needs A schema assemblage combines an estimate of environmental state with a representation of goals and needs The internal state is also updated by knowledge of the state of execution of current plans made up of motor schemas The internal state is also updated by knowledge of the state of execution of current plans made up of motor schemas which are similar to control systems but distinguished by the fact that they can be combined to form coordinated control programs which are similar to control systems but distinguished by the fact that they can be combined to form coordinated control programs

Perception-action schema relationships

Example schemas

Example Schemas

Combined schemas

Schemas: Deadlock Problem

Schemas: Navigational templates

Schemas: the effect of using different gain vector values g i

Behavioral coordination: action selection

Designing with Schemas Characterize motor behaviors needed Characterize motor behaviors needed Decompose to most primitive level, use biological guidelines where appropriate Decompose to most primitive level, use biological guidelines where appropriate Develop formulas to express reaction Develop formulas to express reaction Conduct simple simulations Conduct simple simulations Determine perceptual needs to satisfy motor schema inputs Determine perceptual needs to satisfy motor schema inputs Design specific perceptual algorithms Design specific perceptual algorithms Integrate/test/evaluate/iterate Integrate/test/evaluate/iterate

Strengths and Weaknesses Strengths: Strengths:  support for parallelism;  run-time flexibility;  timeliness for development;  support for modularity Weaknesses: Weaknesses:  niche targetability;  hardware retargetability;  combination pitfalls (local minima, oscillations)‏

Emergent Behavior Important but not well-understood phenomenon Important but not well-understood phenomenon Often found in behavior-based robotics Often found in behavior-based robotics Robot behaviors “emerge” from Robot behaviors “emerge” from  Interactions of rules  Interactions of behaviors  Interactions of either with environment

Distinction Coded behavior Coded behavior  In the programming scheme Observed behavior Observed behavior  In the eyes of the observer  Emergence There is no one-to-one mapping between the two! There is no one-to-one mapping between the two!

Why? sum is greater than the parts sum is greater than the parts emergent behavior is more than the controller that produces it emergent behavior is more than the controller that produces it

Interaction and Emergence Interactions between rules, behaviors and environment Interactions between rules, behaviors and environment Source of expressive power for a designer i.e., Source of expressive power for a designer i.e., Systems can be designed to take advantage of emergent behavior Systems can be designed to take advantage of emergent behavior

Example: Wall Following

Can be implemented with these rules: Can be implemented with these rules:  If too far, move closer  If too close, move away  Otherwise, keep on Over time, in an environment with walls, this will result in wall-following Over time, in an environment with walls, this will result in wall-following Is this emergent behavior? Is this emergent behavior?

Emergent Wall Following It is argued yes because It is argued yes because  Robot itself is not aware of a wall, it only reacts to distance readings  Concepts of “wall” and “following” are not stored in the robot’s controller  The system is just a collection of rules

Emergent Flocking Program multiple robots: Program multiple robots:  Don’t run into any other robot  Don’t get too far from other robots  Keep moving if you can When run in parallel on many robots, the result is flocking When run in parallel on many robots, the result is flocking

Necessary Conditions Notion of emergence depends on two aspects: Notion of emergence depends on two aspects:  Existence of an external observer, to observe and describe the behavior of the system  Access to the internals of the controller itself, to verify that the behavior is not explicitly specified anywhere in the system

Unexpected vs Emergent Some researchers say the above is not enough for behavior to be emergent, because above is programmed into the system and the “emergence” is a matter of semantics Some researchers say the above is not enough for behavior to be emergent, because above is programmed into the system and the “emergence” is a matter of semantics So emergence must imply something unexpected, something “surreptitiously discovered” by observing the system So emergence must imply something unexpected, something “surreptitiously discovered” by observing the system

Subjective Expectations “Unexpected” is highly subjective, “Unexpected” is highly subjective, Because it depends on what the observer was expecting Because it depends on what the observer was expecting Naïve observers are often surprised! Naïve observers are often surprised! Informed observers are rarely surprised Informed observers are rarely surprised

Observation and Emergence Once a behavior is observed, it is no longer unexpected Once a behavior is observed, it is no longer unexpected Is new behavior then “predictable”? Is new behavior then “predictable”?

Formalization Look for behaviors that are not apparent at system level (robot’scontroller) but are apparent at observer’s level Look for behaviors that are not apparent at system level (robot’scontroller) but are apparent at observer’s level

Execution and Emergence So now even simple wall following example given can be called “emergent” So now even simple wall following example given can be called “emergent” This means system has to execute in order for behavior to emerge This means system has to execute in order for behavior to emerge

Uncertainty and Emergence Not difficult to achieve -- environment is uncertain, so exact behavior of a system is very hard to predict! Not difficult to achieve -- environment is uncertain, so exact behavior of a system is very hard to predict! If behavior contains novel and rich patterns, then it is “emergent” If behavior contains novel and rich patterns, then it is “emergent” If world were completely predictable, then we’d remove “emergent behaviors” by this definition If world were completely predictable, then we’d remove “emergent behaviors” by this definition

Emergence Is Unavoidable Some interesting and unexpected behaviors will always emerge in systems that interact with the physical world Some interesting and unexpected behaviors will always emerge in systems that interact with the physical world Some may be labeled “emergent” Some may be labeled “emergent” Some are not desirable! Some are not desirable!

Emergent Bugs Unexpected, emergent behavior that is undesirable Unexpected, emergent behavior that is undesirable  e.g., Oscillations Try to avoid them, but still want to exploit desirable, unexpected behaviors Try to avoid them, but still want to exploit desirable, unexpected behaviors System needs to know how to distinguish between the two System needs to know how to distinguish between the two

Sequential vs Parallel Emergent behaviors can arise from parallel execution of multiple behaviors (e.g., Flocking)‏ Emergent behaviors can arise from parallel execution of multiple behaviors (e.g., Flocking)‏ Also from sequential interaction with an interesting environment Also from sequential interaction with an interesting environment

Architectures and Emergence Different architectures affect the likelihood of generating and using emergent behaviors Different architectures affect the likelihood of generating and using emergent behaviors  Deliberative: always aim to eliminate them  Reactive: aim to exploit them  Hybrid: typically aim to eliminate them  BBS: aim to exploit them

Modularity and Emergence Modularity directly effects emergence Modularity directly effects emergence Reactive and BBS employ parallel rules and behaviors which interact with each other and the environment, thus directly producing and exploiting emergent behavior Reactive and BBS employ parallel rules and behaviors which interact with each other and the environment, thus directly producing and exploiting emergent behavior

Avoiding and Exploiting Hybrid systems follow deliberative model in attempt to produce a coherent, uniform output of the system, minimizing interactions and emergence Hybrid systems follow deliberative model in attempt to produce a coherent, uniform output of the system, minimizing interactions and emergence