1. Introduction to Robots and Multi-Robot Systems Agents in Physical and Virtual Environments Lecture 1: Basic Concepts Gal A. Kaminka galk@cs.biu.ac.il

2. 2 Multi-Robot Systems © 2002- Gal Kaminka Some examples of robots

3. 3 Multi-Robot Systems © 2002- Gal Kaminka Give me a few examples. Is a rock a robot? What is a robot? Robot

4. 4 Multi-Robot Systems © 2002- Gal Kaminka What is a robot? A toy spring car can move and act. a robot can sense. Actuators (Effectors) Robot

5. 5 Multi-Robot Systems © 2002- Gal Kaminka What is a robot? A sorting algorithm senses and acts. a robot is persistent. Actuators (Effectors) Robot Sensors

6. 6 Multi-Robot Systems © 2002- Gal Kaminka What about a remote alarm? a robot is situated in an environment. What is a robot? Actuators (Effectors) Sensors Robot

7. 7 Multi-Robot Systems © 2002- Gal Kaminka We’re missing something here. a robot is responsive. What is a robot? Actuators (Effectors) Sensors Environment Robot

8. 8 Multi-Robot Systems © 2002- Gal Kaminka We’re missing something here. a robot is responsive. What is a robot? Actuators (Effectors) Sensors Environment Robot Process

9. 9 Multi-Robot Systems © 2002- Gal Kaminka Robots: Are persistent with respect to their environment Sense and act Sense/act within the same environment (situated) Respond to senses using action Here’s what we have so far Environment Robot

10. 10 Multi-Robot Systems © 2002- Gal Kaminka Here’s what we have so far Robots: Are persistent with respect to their environment Sense and act Sense/act within the same environment (situated) Respond to senses using action These characteristic are true for agents, not just robots Environment Robot

11. 11 Multi-Robot Systems © 2002- Gal Kaminka Why investigate robots? Because we want to understand how to build them. So that they do things for us. So that we can do other things instead. In other words, We are studying robotics because we are lazy.

12. 12 Multi-Robot Systems © 2002- Gal Kaminka The Agent/Environment/Task Framework We want the robot to do tasks for us (or for itself) Therefore, it must take a task into account Environment Robot Task

13. 13 Multi-Robot Systems © 2002- Gal Kaminka In this course we focus on physical environments Agents are embodied Part of the environment is their own body Sensing and acting with uncertainty Slippery grips, sensing is inaccurate Environment is dynamic, changes even without robot …. We will talk more about environments later, but first….

14. 14 Multi-Robot Systems © 2002- Gal Kaminka A Taxonomy of Environments There are a number of characteristic dimensions: Dynamic vs. static Accessible vs. inaccessible transparent vs. translucent Deterministic vs. non-deterministic Discrete vs. continuous …..

15. 15 Multi-Robot Systems © 2002- Gal Kaminka Dynamic vs. Static Dynamic: Environment changes even if agent takes no action Static: Environment does not change until agent takes action Key question: Is the agent only cause of change in the environment? Physical environment is dynamic Wind, other agents, continuous mechanical forces

16. 16 Multi-Robot Systems © 2002- Gal Kaminka Accessible vs. Inaccessible Accessible (transparent): Agent can sense everything and anything. Nothing is hidden. Inaccessible (translucent): Agent can only sense part of the environment. Some features of the environment are hidden. Key question: What can the agent sense about the environment? Physical environments typically inaccessible: Cannot see behind you, nor over long distances, nor inside people.

17. 17 Multi-Robot Systems © 2002- Gal Kaminka Determinism Deterministic: An action results in a completely predictable change Non-deterministic: An action can result in one of a range of possible changes Uncertainty in the result Key question: If agent takes action, is it sure of the outcome? Physical environment is non-deterministic: Slippery grasp, coin-flips, gambling

18. 18 Multi-Robot Systems © 2002- Gal Kaminka Discrete or continuous? Discrete: Actions or senses are clearly separated, limited number Continuous: Infinite possible values within a range Note: Different from discrete/continuous senses and actions Physical environments are continuous

19. 19 Multi-Robot Systems © 2002- Gal Kaminka A Taxonomy of Environments There are a number of characteristic dimensions: Dynamic vs. static Accessible vs. non-accessible transparent vs. translucent Deterministic vs. non-deterministic Discrete vs. continuous Open question: Quantifying the above

20. 20 Multi-Robot Systems © 2002- Gal Kaminka The Agent/Environment/Task Framework Given environment and task, how do we build a robot that carries out the task? Environment Robot Task

21. 21 Multi-Robot Systems © 2002- Gal Kaminka Agents and Environments Many different environments can exist Different techniques are used with different environments We focus on techniques used in physical environments

22. 22 Multi-Robot Systems © 2002- Gal Kaminka Agent Control In principle, our view is of an agent with three components: Effectors/actuators Sensors Think This view is sometimes referred to as sense-think-act cycle But this can be misleading: not necessarily so sequential Sense Think Act Robot Environment

23. 23 Multi-Robot Systems © 2002- Gal Kaminka Three components, three challenges* The action selection problem: Given task/goals, how to select the next action(s) The sensor planning problem: Given task/goals, how to use sensors The pose planning problem: Given needed target body position, how to get there Sense Think Act Robot Environment

24. 24 Multi-Robot Systems © 2002- Gal Kaminka Three components, three challenges* The sensor planning problem: Which sensors to use? When? How to integrate their information (sensor fusion)? How to overcome uncertainty in their readings? May depend on what think is thinking, and may need to influence what action to take Sense Think Act Robot Environment

25. 25 Multi-Robot Systems © 2002- Gal Kaminka Three components, three challenges* The pose planning problem: Which (combination of) actuators to use to achieve pose? What trajectory should they take? How to compensate for actuation uncertainty? May depend on what think is thinking, and may need to depend what sense reads, and needs Sense Think Act Robot Environment

26. 26 Multi-Robot Systems © 2002- Gal Kaminka Three components, three challenges* The action-selection problem (our focus): How to select action in real-time? How to select action that is good for task/goal? How to integrate competing needs of different subtasks? Depends on the capabilities of sense and act Sense Think Act Robot Environment

27. 27 Multi-Robot Systems © 2002- Gal Kaminka Three challenges These three challenges are highly coupled Not easy to separate them out. Many systems/techniques provide integrated solutions Multiple levels at which can be addressed: hardware, control, software, … Example: better vision by blurring camera Example: using probabilistic inference to handle uncertainty Example: sensor placement affects foraging behavior Robotics is a highly inter-disciplinary field.

28. 28 Multi-Robot Systems © 2002- Gal Kaminka Empirical research As you can see, these are complex concepts Many of problems/solutions affect each other in very subtle ways Physical environments very uncertain, unpredictable Difficult to predict system behavior from analysis Cannot just browse at the algorithms and hardware involved Use empirical research methods in investigations

29. 29 Multi-Robot Systems © 2002- Gal Kaminka Empirical research Experiment design issues: Study system with and without proposed techniques Compare performance of many systems Compare performance across different environments or tasks Faces generality problems in drawing conclusions Tied to the actual challenges of the real world:

30. 30 Multi-Robot Systems © 2002- Gal Kaminka Simulations Significance issues: Run many experiments, draw statistical conclusions Simulation is very useful here Many roboticists frown at simulations (I was called “a theoretician”) Simulation and virtual environment are not same thing

31. 31 Multi-Robot Systems © 2002- Gal Kaminka Science and Scientists Scruffies and Neaties The revolution of 86: Plans are not enough!

32. 32 Multi-Robot Systems © 2002- Gal Kaminka The Sense-Think-Act Cycle: What's in Think (for scruffies) in late 80's? No need to Think: If sensors read X, then do Y Reactive Camp (Brooks 1986, Schoppers 1987) Limited thinking: Behavior-based control Behaviors may have state, memory, procedures Arkin, Firby (1986), Maes,... Deep thinking: integrated planning, monitoring e.g., IPEM (1988) Hybrid architectures (e.g., Gat 1992)

33. 33 Multi-Robot Systems © 2002- Gal Kaminka The Sense-Think-Act Cycle: What's in Think (for neaties) in late 80's? "The Old View" Plans as sequences of actions for execution Plans as mental attitudes (Pollack 1992) Plans as recipes: Some get executed, some just known BDI: Belief-Desire-Intention (approximately): Belief: What the agent knows Desire: What the agents ideally wants to see happening Intention: What the agents actually acts towards Commitments

34. 34 Multi-Robot Systems © 2002- Gal Kaminka An Historical Perspective on Teamwork: From a Single Agent to Multiple Agents Time Scruffiness Neatness Integrating Planning, Execution, Monitoring, Re-Planning, Architectures Reactive-Plans, Architectures Behavior-Based Architectures Mental Attitudes, Belief, Desire, Intention (BDI) Plans as Attitude '86 '90 '96 Subjective

35. 35 Multi-Robot Systems © 2002- Gal Kaminka שאלות?

36. 36 Multi-Robot Systems © 2002- Gal Kaminka Readings Related readings: Empirical methods in artificial intelligence (book by Paul Cohen) AI Magazine articles by same Toby Tyrell: the action selection problem Readings for next week: On the web page

37. 37 Multi-Robot Systems © 2002- Gal Kaminka Homework Give 3 examples of environments that are different from the real world along at least one dimension Characterize the “information environment” existing on the web, where agents may discover, exchange, and manipulate information. Can non-determinism occur when an environment is static? Give an example Extra credit: Propose a way of measuring environments quantitatively along the 4 dimensions discussed in class. Discuss: Is a human a robot? Is a cockroach? Is a cell? Is a thermostat? Is a web-server?

38. 38 Multi-Robot Systems © 2002- Gal Kaminka The End