Autonomous Mobile Robots CPE 470/670 Lecture 11 Instructor: Monica Nicolescu

CPE 470/670 - Lecture 112 Review Expression of behaviors –Stimulus Response –Finite State Acceptor –Situated Automata Behavioral encoding –Discrete: rule-based systems –Continuous: potential fields, motor schemas

CPE 470/670 - Lecture 113 Behavior Coordination Behavior-based systems require consistent coordination between the component behaviors for conflict resolution Coordination of behaviors can be: –Competitive: one behavior’s output is selected from multiple candidates –Cooperative: blend the output of multiple behaviors –Combination of the above two

CPE 470/670 - Lecture 114 Competitive Coordination Arbitration: winner-take-all strategy  only one response chosen Behavioral prioritization –Subsumption Architecture Action selection/activation spreading (Pattie Maes) –Behaviors actively compete with each other –Each behavior has an activation level driven by the robot’s goals and sensory information Voting strategies (DAMN architecture, Rosenblatt) –Behaviors cast votes on potential responses

CPE 470/670 - Lecture 115 Cooperative Coordination Fusion: concurrently use the output of multiple behaviors Major difficulty in finding a uniform command representation amenable to fusion Fuzzy methods Formal methods –Potential fields –Motor schemas –Dynamical systems

CPE 470/670 - Lecture 116 Emergent Behavior The resulting robot behavior may sometimes be surprising or unexpected  emergent behavior

CPE 470/670 - Lecture 117 Emergent Behavior A simple controller: –If too close on left-back, turn left –If too close on left-front, turn right –Similarly for right –Otherwise, keep straight If the robot is placed close to a wall it will follow Is this emergent? –The robot has no explicit representations of walls –The controller does not specify anything explicit about following

CPE 470/670 - Lecture 118 Emergence A “holistic” property, where the behavior of the robot is greater than the sum of its parts A property of a collection of interacting components Often occurs in reactive and behavior-based systems (BBS) Typically exploited in reactive and BBS design

CPE 470/670 - Lecture 119 Flocking How would you design a flocking behavior for a group of robots? Each robot can be programmed with the same behaviors: –Don’t get too close to other robots –Don’t get too far from other robots –Keep moving if you can When run in parallel these rules will result in the group of robots flocking

CPE 470/670 - Lecture 1110 Emergent Behavior Emergent behavior is structured behavior that is apparent at one level of the system (the observer’s point of view) and not apparent at another (the controller’s point of view) The robot generates interesting and useful behavior without explicitly being programmed to do so!! E.g.: Wall following can emerge from the interaction of the avoidance rules and the structure of the environment

CPE 470/670 - Lecture 1111 Components of Emergence The notion of emergence depends on two components –The existence of an external observer, to observe the emergent behavior and describe it –Access to the internals of the controller, to verify that the behavior is not explicitly specified in the system The combination of the two is, by many researchers, the definition of emergent behavior

CPE 470/670 - Lecture 1112 Unexpected & Emergent Behavior Some argue that the description above is not emergent behavior and that it is only a particular style of robot programming –Use of the environment and side-effects leads to the novel behavior Their view is that emergent behavior must be truly unexpected, and must come to a surprise to the external observer

CPE 470/670 - Lecture 1113 Expectation and Emergence The problem with unexpected surprise as property of behavior is that: –it entirely depends on the expectations of the observer which are completely subjective –it depends on the observer’s knowledge of the system (informed vs. naïve observer) –once observed, the behavior is no longer unexpected

CPE 470/670 - Lecture 1114 Emergent Behavior and Execution Emergent behavior cannot always be designed in advance and is indeed unexpected This happens as the system runs, and only at run-time can emergent behavior manifest itself The exact behavior of the system cannot be predicted –The real world is filled with uncertainty and dynamic properties –Perception is affected by noise –Would have to consider all possible sequences and combinations of actions in all possible environments If we could sense the world perfectly, accurate predictions could be made and emergence would not exist!

CPE 470/670 - Lecture 1115 Desirable/Undesirable Emergent Behavior New, unexpected behaviors will always occur in any complex systems interacting with the real world Not all behaviors (patterns, or structures) that emerge from the system's dynamics are desirable! Example: a robot with simple obstacle avoidance rules can oscillate and get stuck in a corner This is also emergent behavior, but regarded as a bug rather than a feature

CPE 470/670 - Lecture 1116 Sequential and Parallel Execution Emergent behavior can arise from interactions of the robot and the environment over time and/or over space Time-extended execution of behaviors and interaction with the environment (wall following) Parallel execution of multiple behaviors (flocking) Given the necessary structure in the environment and enough space and time, numerous emergent behaviors can arise

CPE 470/670 - Lecture 1117 Architectures and Emergence Different architectures have different methods for dealing with emergent behaviors: modularity directly affects emergence Reactive systems and behavior-based systems exploit emergent behavior by design –Use parallel rules and behaviors which interact with each other and the environment Deliberative systems and hybrid systems aim to minimize emergence –Sequential, no interactions between components, attempt to produce a uniform output of the system

CPE 470/670 - Lecture 1118 Deliberative Systems Deliberative control refers to systems that take a lot of thinking to decide what actions to perform Deliberative control grew out of the field of AI AI, deliberative systems were used in non-physical domains, such as playing chess This type of reasoning was considered similar to human intelligence, and thus deliberative control was applied to robotics as well

CPE 470/670 - Lecture 1119 Shakey (1960) Early AI-based robots used computer vision techniques to process visual information from cameras Interpreting the structure of the environment from visual input involved complex processing and required a lot of deliberation Shakey used state-of-the-art computer vision techniques to provide input to a planner and decide what to do next (how to move)

CPE 470/670 - Lecture 1120 Planning Planning: –Looking ahead at the outcomes of possible actions, searching for a sequence that would reach the goal The world is represented as a set of states A path is searched that takes the robot from the current state to the goal state Searching can go from the goal backwards, or from the current state to the goal, or both ways To select an optimal path we have to consider all possible paths and choose the best one

CPE 470/670 - Lecture 1121 SPA Architectures Deliberative, planner-based architectures involve the sequential execution of three functional steps: –Sensing (S) –Planning (P) –Acting (A), executing the plan SPA has serious drawbacks for robotics

CPE 470/670 - Lecture 1122 Drawback 1: Time-Scale It takes a very long time to search in large state spaces The combined inputs from a robot’s sensors: –Digital sensors: switches, IRs –Complex sensors: cameras, sonars, lasers –Analog sensors: encoders, gauges + representations  constitutes a large state space Potential solutions: –Plan as rarely as possible –Use hierarchies of states

CPE 470/670 - Lecture 1123 Drawback 2: Space It may take a large amount of memory to represent and manipulate the robot’s state space representation The representation must be as complete as possible to ensure a correct plan: –Distances, angles, landmarks, etc. –How do you know when to stop collecting information? Generating a plan that uses this amount of information requires additional memory Space is a lesser problem than time

CPE 470/670 - Lecture 1124 Drawback 3: Information The planner assumes that the representation of the state space is accurate and up-to-date The representation must be updated and checked continuously The more information, the better Updating the world model also requires time

CPE 470/670 - Lecture 1125 Drawback 4: Use of Plans Any plan is useful only if: The representation on which the plan was based is accurate The environment does not change during the execution of the plan in a way that affects the plan The robot’s effectors are accurate enough to perfectly execute the plan, in order to make the next step possible

CPE 470/670 - Lecture 1126 Departure from SPA Alternatives were proposed in the early 1980 as a reaction to these drawbacks: reactive, hybrid, behavior-based control What happened to purely deliberative systems? –No longer used for physical mobile robots, because the combination of real-world sensors, effectors and time- scales makes them impractical –Still used effectively for problems where the environment is static, there is plenty of time to plan and the plan remains accurate: robot surgery, chess

CPE 470/670 - Lecture 1127 SPA in Robotics SPA has not been completely abandoned in robotics, but it was expanded The following improvements can be made: –Search/planning is slow  saved/cache important and/or urgent decisions –Open-loop execution is bad  use closed-loop feedback and be ready to re-plan when the plan fails

CPE 470/670 - Lecture 1128 Summary of Deliberative Control Decompose control into functional modules: sense- world, generate-plan, translate-plan-to-actions Modules are executed sequentially Require extensive and slow reasoning computation Encourage open-loop execution of generated plans

CPE 470/670 - Lecture 1129 Hybrid Control Idea: get the best of both worlds Combine the speed of reactive control and t he brains of deliberative control Fundamentally different controllers must be made to work together –Time scales: short (reactive), long (deliberative) –Representations: none (reactive), elaborate world models (deliberative) This combination is what makes these systems hybrid

CPE 470/670 - Lecture 1130 Biological Evidence Psychological experiments indicate the existence of two modes of behavior: willed and automatic Norman and Shallice (1986) have designed a system consisting of two such modules: –Automatic behavior: action execution without awareness or attention, multiple independent parallel activity threads –Willed behavior: an interface between deliberate conscious control and the automatic system Willed behavior: –Planning or decision making, troubleshooting, novel or poorly learned actions, dangerous/difficult actions, overcoming habit or temptation

CPE 470/670 - Lecture 1131 Hybrid System Components Typically, a hybrid system is organized in three layers: –A reactive layer –A planner –A layer that puts the two together They are also called three-layer architectures or three-layer systems

CPE 470/670 - Lecture 1132 The Middle Layer The middle layer has a difficult job: compensate for the limitations of both the planner and the reactive system reconcile their different time-scales deal with their different representations reconcile any contradictory commands between the two The main challenge of hybrid systems is to achieve the right compromise between the two layers

CPE 470/670 - Lecture 1133 Readings M. Matarić: Chapters 17, 18