COMP150 Behavior-Based Robotics

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For class use only, do not distribute COMP150 Behavior-Based Robotics http://www.cs.tufts.

1 For class use only, do not distribute COMP150 Behavior-Based Robotics

2 Project directions and topics Groups: between 2 and 4 Projects have to be commensurate with group size (e.g., a group project performed by a group four people should roughly be the equivalent of two two-person group projects...) Goal of the project is to get something working on a robot note that this is a requirement for the course: every course project has to be demonstrated on a robot! There are basically three main types of projects: Architecture/algorithms development and implementation (Embodied) models of biological systems or functions (HRI) evaluations of robots or robotic aspects

3 Architecture/algorithms The goal is to (possibly) define, develop, and implement algorithms of interest in a robotic architecture and show their operation on a robot Examples (first four for n=2, last three for n>2): multi-modal map making using laser and vision data robust people-following through hallways and doors people recognition based on faces, body shape, etc. simple dialogue interactions with gestures effective navigation based localization in map soccer-playing robot that can catch and dribble a ball tour guide that can show people around the lab waiter that can take orders and serve drinks

4 Biological models The goal is to model a biological system (e.g., the behavior of groups of insects) or a biological systems function (e.g., the shift of attention and possible head movements associated with changes in visual saliency in the environment) Examples (all but last for n=2, last for n>2): swarm-based search of targets in an environment swarm-based map making and localization object tracking/recognition using optical flow coupled models of interaction distance in conversation deictic gestures accompanying deictic language expressions reaction time models of cognitive effects (e.g., Stroop effect) generation of referential phrases given perceivable context

5 (HRI) evaluations The goal is to (possibly) develop and carry out human subject experiments to evaluate an interesting aspect of the robot or the robot behavior (here code development is secondary) Examples (all but last for n=2, last for n>2): human reactions to immoral human behavior with robots differences in human perceptions between Nao and Cramer perceived differences between virtual and real robots human attitudes towards robots and robot failures the utility of emotions in robots working with humans robots as embodied reminder systems evaluation of any of the algorithms or models developed by fellow students in other projects (caveat: enough of the system needs to work early on for the evaluation to work out)

6 Brief summary from last week Talked about cognitive science and what it is (as a field), looked at the historical roots, in particular, the classical approach to explaining cognition (the computer metaphor ) Talked about the change in the 80ies and later (in particular, subsymbolic and connectionist approaches, and later the dynamical systems approaches), and contrasted the embodied approach (in cognitive science) and the behavior-based approach (in AI and robotics) to the classical paradigms (in cognitive science and robotics) Reviewed Pfeiffer's notion of embodied cognitive science and started talking about different types of architectures Next steps: clarify the notion of model (to better understand the difference between different types of behavior-based systems), review animal behavior and compare it to robot behavior, in particular, with respect to how to design and explain it)

7 Quick Excursion: Models of Intelligent Behavior What are (computational) models of intelligent behavior? Difficult issue, no clear unanimous answer available (e.g., the model muddle, see Barbara Webb's BBS article at Different kinds of models; mathematical models (e.g., system of differential equation) physical models (e.g., a mass-spring system) both may or may not be computational Other factors: real-time vs. abstract or simulated time real vs. virtual Let's look a bit more closely...

8 Lots of types of models... automata/state machine models correlation models dynamical systems models evolutionary models flow-chart/process models game-theoretic models logical models neural network/connectionist models production system models...

9 ... with lots of different properties algorithmic vs. non-algorithmic collective-based vs. individual/agent-based distributed vs. non-distributed embodied vs. non-embodied/disembodied equation-based vs. rule-based sequential vs. parallel stochastic vs. non-stochastic symbolic vs. subsymbolic temporal vs. atemporal...

10 Intuitions shared among the different kinds of models Models and target systems (i.e., the systems to be modeled) are similar in some important respects Models and target systems are (type) different (but see Rosenblueth and Wiener 1945) Models are typically less complex than target systems (at least w.r.t. the relevant properties) Models are easier to manipulate and understand than target systems (e.g., they can be implemented on computers and exhaustive simulations can be run) Models can include non-observable or hard-to-observe causes or effects in target systems

11 The utility of models Models can illustrate mechanisms and principles of operation and proper/improper functioning of a system They help us understand neural, affective, perceptual, cognitive, and other processes Models also can be fit to empirical data Then they can be used to explain experimental effects (that might be diffictult to explain otherwise) and to make predictions that might lead to new experiments And ultimately contribute to a theory of mentality as part of the scientific experiment-theory loop

12 The two intertwined loops of scientific discovery - make observation - interpret results - formulate a theory - produce a model - run model simulations - interpret results - make empirical predictions - conduct experiments... (from Peschl and Scheutz 2001)

13 Modeling a Target System Stimulus Stimulus encoding S S(Stimulus) Behavior-Based System F Model M with parameters p1,p2,... Response=F(Stimulus) Response encoding R M(S(Stimulus)) R(F(Stimulus)) Define 1-1 functions S and R in advance note that bounding the complexity of S and R is critical! Adjust parameters p1, 1,p2,... until diagram commutes Then: M models F with respect to the set of stimuli But: this is only behavioral model, want more!

14 Modeling a Target System Stimulus Behavior-Based System F Response=F(Stimulus) Stimulus encoding S Structural mapping I Response encoding R S(Stimulus) Model M with parameters p1,p2,... M(S(Stimulus)) R(F(Stimulus)) Want some structural correspondence between components in the model and components in the cognitive system (e.g., homeomorphic embeddings)

15 Avoiding troubles with mappings... In fact, correspondence and encodings can be tricky! For sure, one could say, neurons in neural nets must have clear correspondences in brains, but... given the difficulty of precisely stating the neural counterpart of components of subsymbolic models, and given the very significant number of misses, even in the very general properties considered..., it seems advisable to keep the question open (Smolensky, 1988, BBS, p. 9) The behavior-based approach: embodied models of distributed co-active behaviors Note that embodied models (usually) need not worry about R and S...

16 Computational Models Let's try: computational models are models such that they use computers in one way or another to model (in the above sense) one or more aspects of a given (cognitive, etc.) system Note that to model means to have the computer exhibit the same behavior under the same conditions as the target system ( same is tricky!) For the books: computational models are models that are computable (in the sense of the theory of computation) implementable on standard computers and hopefully computationally feasible

17 Computational Models What are we going to model/implement in the course? sensory and motor routines aspects of intelligent/cognitive systems behaviors, etc. possibly psychological, biological models (in which case we need to build on results from psychology, biology, neuroscience, etholoy, etc.) Enginieering approach to intelligent systems: try to mimick human/animal behavior (regardless of its biological plausibility) Cognitive science approach: do it in a biologically plausible way For this course: try to get things to work either way!

18 Quick Excursion: Animal Behavior All three disciplines (psychology, ethology, neuroscience) contribute in different yet important ways to behavior-based design, analysis and implementations in robotics From psychology we get functional components (e.g., memory, emotion circuits, perceptual processing areas like the visual or auditory cortex, etc.) and architecture schemes (e.g., how functional components are connected), which help in understanding the behavior of an organism in terms of stimulus-response diagrams (behaviorism) understanding the information in the environment that the organism can use (ecological psychology) understanding the internal (cognitive) processes that drive behavior (cognitive psychology)

19 Quick Excursion: Animal Behavior From ethology we get behavioral networks (i.e., functional breakdowns of the behavioral repertoires of animals, e.g., into "reflexes", "taxes", and "fixed action patterns") understanding the ecological niche of an organism in terms of its evolutionary trajectory (evolutionary biology) understanding the animals behavior in terms of the animal's natural habitat (animal behaviorism) From neuroscience: implementation paradigms (e.g., neural networks and schemas/schemata) understanding the neural underpinnings of cognitive processes (cognitive neuroscience) understanding the architectural organization and properties of neural systems (neurophysiology)

20 Quick Excursion: Animal Behavior New field: "biorobotics"--model nature (remember: this is stronger than just being "inspired"!) Take a look at Barbara Webb's BBS article available at for an extensive bibliography ot the state-of-the-art in biorobotics Will take the "animal stance" in this course, i.e., we will design "controllers" for robots explicitly viewing them as "animals" that inhabit a particular environment have to cope with various environmental conditions need to survive (long enough...) have "reproductive goals" need to specify all of the about for every controller/robot we design

21 What are Robot Behaviors? First, be clear on the level of description: observable behavior or implemention of component (which implement the functions as described by the behavioral description) -- already said that we'll use the term behavior for both Example: reactive systems (keep in mind the different readings of "reactive"!) Arkin's characterization: Behaviors serve as the basic building blocks for robotic action Use of explicit abstract representational knowledge is avoided in the generation of a response Animal models of behavior often serve as a basis for these systems These systems are inherently modular from a software design perspective (cp. to the credo of evolutionary roboticists )

22 What are Robot Behaviors? Claim: planning is not necessary to achieve effective navigation (i.e., only need reactive system for navigation this is in contrast to classical systems like Shakey that used planning all the time, cp to Brooks...) Careful: this is a strong claim if read "not necessary in any circumstance" (reactive systems have very clear limitations, e.g., how to get to the airport without planning?) Important: understand what the domains are in which reactive control is advantageous Issue: where do behaviors come from? More specifically, how do we design a behavior-based robotic architecture?

23 What are Robot Behaviors? Follow-up questions: what is the overall architecture? what are the primitives (i.e., the smallest building blocks or components) that we can use? how do those building blocks correspond to functional descriptions? how do we connect these components (within the architecture and to sensors/effectors)? Keep in mind that there may not be a clear correspondence for every behavior to implemented components in the architecture (e.g., when behaviors emerge...)

24 Expressing/Describing Behaviors Need a way to capture our behavioral descriptions (e.g., formalize them) Different ways of capturing them: stimulus-repsonse diagrams (mathematical) functional notation finite state machines formal methods (e.g., robotic schemas, situated automata, JAVA programs, etc.) Will talk in detail about different ways to express/describe behaviors later

25 Designing Robot Behaviors Different design methodologies based on motivation and research aim: experimentally driven ethologically guided situated activity Experimentally driven: build mininal system exercise robot evaluate results add new behavioral competence

26 Designing Robot Behaviors Ethologically guided: consult ethological literature extract model import model to robot run robotic experiments evaluate results possibly do new biological experiments enhance models import model to robot etc.

27 Designing Robot Behaviors Situated activity: assess agent-environment dynamics partition into situation create situational responses import behaviors to robot run robotic experiments evaluate results enhance, expand, correct behavioral responses import model to robot etc.

28 Implementing Behaviors How can we enocde/implement "behaviors"? Remember: stimulus-response diagrams denote functions, i.e., mappings f(s)->r (where S is the domain of stimuli and R the domain of responses), hence, in general,, a behavior is any mapping from possible stimuli to possible responses What are possible stimuli? Depends on sensors (e.g., pixel images in the case of cameras, a frequency spectrum for mircophones, etc.) In general: a stimulus can be taken to be a tuple <p,lambda>, where p denotes a particular perceptual class and lambda denotes the intensity of the stimulus

29 Implementing Behaviors What are the possible responses? Again, this depends on effectors or actuators (e.g., motors, wireless transmitters, etc.) Responses are often expressed in terms of strength and orientation for effectors (think of directional vectors!) In general: if a motor response involves physical movement, then it can be characterized by a six-tuple (x,y,z,roll,pitch,yaw) (three translational and three rotational degrees of freedom note that an unconstrained rigid object has six DOFs) Distinguish: holonomic from non-holonomic (i.e., if the controllable DOFs are greater than or equal to the total degrees of freedom, then the robot is said to be holonomic)

30 Implementing Behaviors For both stimuli and response, a notion of "strength" has to be defined (interesting issue: what is the magnitude?) Interesting dichotomies: discrete vs. continuous (in time and/or space) analog vs. digital simple vs. complex structured vs. unstructured First Mantra of reactive BBR: the presence of a stimulus is necessary, but not sufficient to evoke a motor response in a behavior-based robot Hence, need a threshold to model behavioral mappings

31 Behavioral Mappings Formally for motors: f:perceptsrrrrrrrrrrrr E.g., (p,)(x,y,z,,,) where no output is generated if < In addition: gains to modify response strength, also used to "integrate behaviors" (will talk more about this later) E.g., (a,b,c,d,e,f)(x,y,z, (x,y,z,,,) where g=(a,b,c,d,e,f) is a vector of scalars that modify the respective components of the response r (put more succinctly: r'= '=gr) Note that f can be any function

32 Behavioral Mappings However: frequently used motor functions are constant (e.g., "move at a constant speed) "binary" threshold (e.g., "stop when wall is encountered") linear (e.g., "move faster the farther you are away from a wall") combinations of the above (e.g., "move at a constant speed while you cannot sense a wall, once you sense it slow down proportional to the distanc to the wall, and stop once you reached a critical threshold") Note that the actual control law for motors will in general be more complicated (e.g., maintaining the constant speed which is the output of a behavior might require a PID controller with shaft encoder feedback from the motors...)

33 Behavioral Mappings Distinguish: discrete vs. continuous responses: discrete responses categorizes the sensory space into discrete categories and map a particular response to each domain (as in the above examples) continuous responses do not categorize the sensory space, rather they establish "correlations" (or more to the point: functional dependencies) between stimuli and responses-- force metaphor Problem: how to integrate two or more behavioral mappings? (no issue if we only have one, e.g., as for toy problem one) Need to think of ways how to achieve this integration, since most likely we will have different behaviors that the robot should exhibit at different times

34 Assembling Behaviors Discrete vs. continuous encoding of behaviors what exactly is the difference? First cut: finitely vs. potentially infinitely many responses Note: infinitely many behavioral responses are only possible, if sensory space is also infinite (not very likely!) Second cut: granularity of response compare: "moving forward" (for some time) vs. "moving forward one yard" or "moving forward for 10 seconds at speed 1/10 yard/sec" Third cut: architectural representation of response in the former case, there is component for "moving forward" that may or may not give rise to the specific "moving forward one yard" (given other components and their interactions), whereas in the latter there is a particular component for "moving forward one yard"

35 Assembling Behaviors Continous encodings: use continuous mapping from sensors to motors, i.e., express relationship of what to do commonly used: vector fields environment is construed as a vector space where in each location a vector indicates the direction and strength of the motor response (may have to do this for different motors) vectors are used as "forces", where forces are usually related to distance in space by the "inverse-square law": force = 1/ (distance*distance) use: attractive and repulsive forces to "classify" sensory stimuli (i.e., some stimuli will produce attractive forces, others will produce repulsive forces)

36 Assembling Behaviors Different ways of discrete encodings: Condition-action rules: IF perception/condition THEN action E.g., Nilsons teleo-reactive rules (e.g., "forward at speed s"--e.g., as opposed to "forward one yard") Goal-reduction rules (Gapps, situated automata): ACHIEVE condition/action DO action ACHIEVE action/condition... ENDACHIEVE Brook's Behavior Language: WHENEVER condition &rest body- forms) Most so-called "agent architectures" specify a finite set of "possible actions" (e.g., dynamic logic and various logics used to describe the temporal behavior of systems) An agent is construed as FSM given by F:(Inputs,States) :(Inputs,States)(Actions,States)(Actions,States)

37 C is also called "coordination function (strategy)" or "action- selection function (mechanims)" Behavior Coordination Need to integrate different behaviors to get intended/interesting system behavior Design issues: what is the overall behavior the system needs to achieve how can it be broken down into components General case: given S (stimuli vector), B (vector of all behavior functions), G (vector of gains for each behavior), we get the response vector R=G*B(S) Need to select one component of R,, hence need a "coordination" function C from vectors to scalars: =C(R)

38 Behavior Coordination Different coordination/action-selection methods: competitive priority-based arbitration (e.g., through dominance hierarchies, where higher levels dominate or suppress lower levels) winner-take-all (e.g., the highest activation of all behaviors gets exclusive control of the motors) direct competition (through excitation and inhibition) voting for actions cooperative "field fusion" (remember: vector fields are "additive") "desirability vectors" (e.g., try to find action that maximizes the desirability values of the behaviors) (see also Section 2 in Scheutz & Andronache 2004:

39 Behavior Coordination Putting things together: parallel execution vs. sequencing of actions hierarchical vs. non-hierarchical organization => need to look at architectures! Issues: emergence of behavior, very tricky notion (often poorly presented and understood, for a good discussion, see Wimsatt how can we control for emergent behavior? what is the right explanation of behavior/emergent behavior? how do we evaluate architectures that give rise to emergent behavior?

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