Adaptive Robotics COM2110 Autumn Semester 2008 Lecturer: Amanda Sharkey

1. Adaptive RoboticsCOM2110 Autumn Semester 2008 Lecturer: Amanda Sharkey

2. “Robots in the news” • Macho robot helps explain lizards' odd behaviour • 22:00 24 November 2008 by David Robson New Scientist • Why does male anolis lizard perform a series of push-ups before attempting to intimidate rivals with colourful displays? • Terry Ord at Harvard University built a robotic lizard that can inflate a colourful dewlap under its chin, bob its head, and perform push-ups

3. Cameras in Puerto Rico forests recorded when lizards turned their heads • Robot lizard’s push-ups attracted attention – without them lizards often missed much of coloured dewlap display.

4. Lect 1: what is a robot? Early robots, Shakey and GOFAI, Behaviour-based robotics • Mechanisms and robot control (and biological inspiration) • Lect 2: Grey Walter, Brooks and Subsumption Architecture. • Lect 3: Adaptation and learning • Lect 4: Artificial Neural Nets and Learning (biologically inspired, could be used to implement robot control in behaviour-based robotics approach, also move away from GOFAI) • Lect 5: Evolutionary Robotics (also biologically inspired – another way of developing robot control) • Lect 6: Swarm Robotics (reasons for, biological inspiration, local control and communication, self-organisation and emergence). • Lect 7: Biorobotics and Biological modelling. • Lect 8: Applications

5. “new wave” robotics • Aka • Behaviour-based robotics • Nouvelle AI • Embodied Cognition

6. In opposition to: • Sense-Model-Plan-Action approach • Centralised cognition • Mind as central logic engine • Memory as retrieval from stored symbolic database • Problem solving as logical inference • Environment – problem domain • Body – input device • Functionalism

7. Functionalism • Thinking, and other intelligent functions – could be carried out on different hardware. - interested in the software. • Swiss cheese? • Physical Symbol System Hypothesis • (Newell and Simon, 1975) • Emphasis on manipulation and processing of symbolic representations of the world • Little interest in how those representations are related to the objects in the world.

8. Review of defining characteristics of “new wave” robotics and “nouvelle AI” • Simplicity – “Keep it simple” • Minimal representation • Biological inspiration • Embodiment • Situatedness • Emergence • Autonomy • Interaction with the environment • Brain, body and world

9. Two guiding principles (from Maes, 1994) • Looking at complete systems often changes the problems in a favourable way • Interaction dynamics can lead to emergent complexity

10. Complete systems • Building complete systems can simplify problem • E.g. with sensors, easier to disambiguate natural language utterances because they are related to the objects the agent sees • E.g. systems with sensors and actuators can perform tests in the environment and needs less modelling and inference

11. Complete systems • Since intelligent system is situated in environment, this can be exploited • E.g. using the environment as external memory, reminding which tasks remain to be done. Also habitat constraints, e.g. usual size of doors in office, can be exploited • Time: incremental solution can be arrived at e.g NLP and asking further questions

12. Complete systems • Society: can look at other agents and other solutions. • E.g mobile robot closely following a person walking by, to avoid bumping into things.

13. Emergent complexity • Idea from ethology that animal’s behaviour can only be understood in the context of the environment in which it occurs. Simon(1969) the complexity of an ant’s behaviour reflects the complexity of its environment

14. Emergent functionality • E.g. Mataric’s (1991) wall following robot. One module steers robot towards wall when distance above threshold, and one module steers away when distance below threshold – result = wall following • Social insects following simple local rules to produce emergent complexity

15. Today - applications • Navlab – autonomous vehicles • Alternative to subsumption architecture • DARPA grand challenge • Swarm robotics – pherobots • Robot sheep dog • Leurre project: influencing the behaviour of cockroaches

16. Application areas • Physical robots • Household e.g. vacuuming, lawn mowing • Autonomous vehicles e.g. Navlab • Agricultural • Hostile terrains e.g. underwater, space, military, bridge inspections, disaster (9/11) • Urban search and Rescue (Robin Murphy) • Entertainment e.g. toys, personal robots, exhibitions, games • Companions – for the young, for the elderly • Military • Policing and surveillance • Some ethical issues in involved in the above

17. Navlab • CMU (Carnegie Mellon University) group • Robot cars, trucks, buses for autonomous navigation • 11 different Navlabs (Navlab11 on its way) • Langer, Rosenblatt and Herbert (1994) A Behaviour-based System for Off-Road Navigation. IEEE Trans Robotics and Automation, 10, 6, pp 776-782

19. Navlab 11

20. Autonomous cross-country navigation • Rugged terrain • Processing of 1000s of images • Need to avoid failure • Simple algorithms for obstacle detection and local map building in behaviour-based architecture • Underlying principle: keep things simple

21. Use of independent modules • Perception module (list of untraversable regions) • Local map module (maintains map of terrain round vehicle) • Planning module (generates steering arcs, keeping clear of untraversable regions)

23. Perception • Takes single image as input and produces list of untraversable regions • Terrain classification algorithm: based on grid system • Each cell in grid corresponds to 20cm x 20cm • Each cell classified as traversable or not

25. Terrain classification • Strengths: simple system, each image processed individually without terrain matching and merging • Limitations – some misclassification – dense vegetation can appear as an obstacle • Problems with regions with poor reflectance e.g. water • Dependence on good sensors

26. Local Map Management • Purpose – to maintain list of untraversable cells in region round vechicle • Module called Ganesha • Uses 2D grid-based representation of local map • Core of system is single loop • Read current position of vehicle, update coordinates of cells. Discard cells outside bounds of active regions • Get obstacle cells and place in local map • Update internal cell attributes • Send list of obstacle cells to planning system

28. Planning • Use map to generate commands to steer round obstacles • Used DAMN (Distributed Architecture for Mobile Navigation) behaviour-based architecture • Like subsumption architecture • Uses specialised task-achieving modules that operate independently and are responsible for only part of vehicle control • Some internal representation of world • Activation selection – relies on command fusion

31. DAMN cont. • Each behaviour votes for or against set of vehicle actions • Votes between –1 and +1 for each of 15 steering commands • Weighted sum of votes computed. Steering arc with maximum vote is found • Speed also decided by voting • Obstacle avoidance – each behaviour has list of current obstacles • Votes for trajectories free of obstacles • Votes against paths with obstacles • Other behaviours: goals seeking, drive straight, maintain turn.

33. 2 behaviours • Obstacle avoidance and goal seeking • Arbiter combines votes and issues new driving command every 100 ms • Weights 0.8 for obstacle avoidance and 0.2 for goal seeking

34. Limitations • Can’t deal with some situations e.g dead ends such as closed corridor with depth greater than field of view of sensor • Limited range and speed of sensor • Non-real-time nature • Poor performance of perception on certain types of environment. • BUT 1km traverse shows robustness

35. DARPA Grand Challenge Robot Vehicle race 2005 • Unmanned vehicles on 132 mile course in Mojave desert • - of 23 entrants, five completed. • (previous year none got further than 11 km) • Winner: Stanley, Stanford University • 6 hours, 53 minutes • (2,000,000 dollars) • 2nd place: Sandstorm, CMU • See www.grandchallenge.org

37. Route given 2 hours before competition, in form of GPS coordinates • Teams could program routes into vehicles • Sebastian Thrun – Stanley had vision-based speed switch: drove faster when it detected straight road ahead without obstacles

38. Stanley • Used: • GPS • Laser Range Finder to map the road 30 meters ahead • Video camera to scan 80 meters ahead • Odometry

39. DARPA Grand Challenge 2007“Urban challenge” • “Autonomous ground vehicles executing simulated military supply missions safely and effectively in a mock urban area” (DARPA press release) • Challenge completed November 2007

40. 60 mile urban area course, to be completed in less than 6 hours. • Rules – following all traffic regulations, negotiating with other traffic and obstacles. • E.g. maintaining precedence at 4 way stop intersection • 11 teams given development funding. This challenge less physically demanding, but involved encounters with other vehicles.

41. $2000,000 Winner: Tartan Racing (Carnegie Mellon University) • Averaged 14 miles per hour throughout course. • 2nd: Stanford Racing (Stanford University).

43. Next Grand Challenge • Where do you think that robots could most usefully be employed? • (I.e Where should funding be put?) • What kind of robots do you think are likely to be developed?

44. Swarm Robotics and applications • Swarm robotic principles • Biological inspiration from social insects • Simple autonomous agents • Decentralised local control • Minimal communication and representation • Reactive behaviour • Interaction with environment • Emergence, situatedness, embodiment

45. Advantages for applications • Cheap, expendable autonomous robots • Able to negotiate and exploit environment to achieve emergent cooperative solutions to practical problems • Redundancy and simplicity means robots can be added, or removed, without requiring recalibration or mission failure

46. Possible application areas • Areas that are hostile or inaccessible to humans e.g. clearing up toxic waste or contaminated buildings e.g. mine fields e.g. planetary exploration e.g. burning or collapsed buildings e.g. battlefield search for survivors

47. Pheromone robotics • David Payton et al (HRL labs) (2004) • Uses ‘virtual pheromones’ • Imagined scenario: rescue team enters unfamiliar building and needs to find survivors • Swarm of robots explores – one finds survivor and emits message. • Message relayed locally among neighbouring robots • Virtual pheromone gradient propagated back to rescue workers.

48. Pherobot PalmV PDA used as main control computer

49. Virtual pheromones implemented via infrared signals • 8 radially oriented directional infrared receivers and transmitters on each robot • Robots can transmit and receive messages directionally relative to current orientation • Pheromone message also contains hop-count field which can be decremented as it is passed on – creating a pheromone gradient