1. Biologically-inspired robot spatial cognition based on rat neurophysiological studies Alejandra Barrera and Alfredo Weitzenfeld Auton Robot 2008. Rakesh Gosangi PRISM lab Department of Computer Science and Engineering Texas A&M University

2. Outline Introduction Related work Biologically inspired spatial cognition Experimental results Conclusion and Discussion

3. Introduction SLAM – the problem of a mobile robot acquiring a map of its environment while localizing itself in the map. Challenges in SLAM – Data association – if two features observed at different times correspond to the same object – Perceptual ambiguity – distinguish between places that provide similar or equivalent visual patterns

4. Spatial cognition in rats Data association or place recognition in rats is based on cognitive maps generated in hippocampus Cognitive maps are created from visual and kinesthetic feedback information Rats can learn and unlearn to reward locations in goal-oriented tasks

5. Contribution of the paper Neural network based spatial cognition model for a mobile robot inspired from rat’s brain structure – Build a holistic topological map of the environment – Recognize places previously visited – Learn-unlearn to reward locations – Perform goal-directed navigation – Use kinesthetic and visual cues from the environment

6. Outline Introduction Related work Biologically inspired spatial cognition Experimental results Conclusion and Discussion

7. Comparison with Milford (2006) - RatSlam The two models coincide with mapping and map adaptation but differ in goal-directed navigation Milford et al. use a topological map of experiences where each experience codifies location and orientation Transitions are associated with locomotion In this paper, the nodes correspond to visual information patterns and path integration signals Transitions correspond to orientation and locomotion of the rat

8. Experimental basis Morris’ experiment (1981) Two types of rats – Normal rats – Rats with hippocampal lesions Two experimental situations – Visible platform – Submerged platform with visual cues around the arena Normal rats relate their position with respect to visual cues and recognize target location

9. Image borrowed from - Morris, R. G. M. (1981). Spatial localization does not require the presence of local cues. Learning and Motivation, 12, 239–260.

10. Experimental basis O’Keefe’s experiment (1983) A reversal task on a T-maze Rats with Hippocampal lesions – Learned to turn to right arm in a T-maze – Gradually changed their orientation for left arm to right arm in 8-arm maze – Their behavior was based on goal-location relative to body Normal rats – Learned to turn to right arm in T-maze – The shifting from left to right was not gradual in an 8-arm maze – Their behavior was based on a spatial map constructed in hippocampus

12. Outline Introduction Related work Biologically inspired spatial cognition Experimental results Conclusion and Discussion

13. Biologically inspired spatial cognition Biological background Affordances processing Rat’s motivation Path integration Landmark processing Place representation and recognition Learning Action Selection

16. Affordance processing Affordances are coded as a linear array of cells called affordance perceptual schema An affordance corresponds to a 45° turn relative to the rat’s head Each affordance is represented as a Gaussian distribution, the activation of neuron i is give by

18. Motivation The rat’s motivation is related to its hunger drive The rat obtains a reward r(t) by the presence of food

19. Path Integration Process of updating the position of the point of departure each time the animal performs a motion Path integration helps an animal return home Path integration uses kinesthetic information – Magnitude of rotation – Magnitude of translation Path integration module is composed of two neural network layers – Dynamic Remapping Layer (DRL) – Path Integration Feature Detector Layer (PIFDL)

20. Dynamic Remapping Layer 2-D array of neurons The activation of a neuron (i, j) is computed as – (x, y) codify the anchor relative to initial coordinates in the plane The anchor position displaces each time the rat moves by the same magnitude but in the opposite direction

22. The anchor position is updated by applying convolution between DR layer and a mask M The DR Layer is updated according to C by centering the Gaussian at (r, c) – maximum value of C

23. Path Integration Feature Detector Layer PIFDL is also a 2-Dimensional array of neurons Every neuron in DLR is randomly connected to 50% on neuron in the PIFDL The weights between the two layers are learned through Hebbian learning

25. Landmark Processing Distance and orientation of each landmark is represented as a linear array of cells (LPS) Each LPS is connected to a 2-Dimensional array of neurons called Landmark Feature Detector Layer (LFDL) The connecting weights are learned through Hebbian learning All the LFDLs are combined into a single Landmark Layer (LL) Visual information pattern is stored in an array called LP

27. Place representation and recognition Place Cell Layer (PCL) is a 2-Dimensional layer of neurons Every neuron in PIFDL is randomly connected to 50% of neurons in the PCL Every neuron in the LL(Landmark Layer) is connected to 50% of neurons in the PCL The synaptic efficacy between the two layers is learned through Hebbian learning PC encodes kinesthetic and visual information sensed by the rat at a given location and a given orientation

29. World Graph Layer The nodes in the map represent different places Arcs between the nodes represent – The direction of the rat’s head – Number of steps taken by the rat to move from one node to the other Every node can be connected to eight actor units, one for each direction Place recognition – SD is the similarity degree, N is the number of cells

31. Learning Learn and unlearn reward locations by reinforcement learning through Actor-Critic Architecture Adaptive Critic (AC) unit contains a Predictive Unit (PU) which estimates future rewards for every place – Every neuron in PCL(Place cell layer) is connected to PU and every connection has A weight w Eligibility trace e – P(t) is expected reward at time t – r’(t) is effective reinforcement signal

33. Action Selection Action selection is based on four signals – Available affordances at time t (AF) – Random rotations between available affordances (RPS) – Unexplored rotations from current location (CPS) – Global Expectation of Maximum Reward (EMR) Representation – Each affordance in AF is represented as a Gaussian – RPS is one Gaussian centered at a random array position – CPS capture the animal’s curiosity. As many Gaussians as unexecuted rotations at that location

36. Outline Introduction Related work Biologically inspired spatial cognition Experimental results Conclusion and Discussion

37. Experiments Hardware – Sony AIBO ERS-210 4 legged robot – 1.8 GHz P4 processor – A local camera with 50° horizontal view and 40° vertical view At a given time step the robot takes three non-overlapping snapshots (0°, +90°, -90°) Visual processing analyzes the number of colored pixels in the images Kinesthetic information is obtained from the external motor control, there is no odometer Four experimental conditions

38. Experiment 1 – T-maze Departure point is the base of the maze During training phase the goal is set at the end of the left arm During the testing phase the goal is shifted to the right arm Results – The robot takes 16 trials to completely unlearn the previously correct hypothesis – When the expectation of reward exceeds noise the robot starts visiting the right arm – In O’Keefe’s experiments (1983), the rats chose the right arm 90% of the time by 24 th trial

41. Experiment 2 – 8-arm radial maze The goal is set at -90° arm during training phase During the testing phase the goal is set at +90° arm Results – When the expectation of reward for -90° arm is smaller than noise the robot visits other arms randomly – By the 12 th trial the robot starts choosing the +90° arm – In O’Keefe’s experiments (1983) the rats chose the correct arm by 20 th trial

43. Experiment 3 – Multiple T-maze The robot departs at the base of vertical T-maze During training phase the goal is placed at right arm (90°) of the left horizontal T-maze During testing phase the goal is placed at right arm (270°) of the right horizontal T-maze Results – If the robot reaches the goal at the end of a path then it is positively reinforced – If a path does not lead the robot to a goal it is negatively reinforced thus unlearning the path – The robot completely unlearns previous goal by 20 th trial

46. Experiment 4 – Maze with landmarks Three colored cylinders were placed outside the maze as landmarks During testing the robot was placed at different starting locations Results – The robots use place recognition to find goals – All the robots found the goal successfully from all starting positions

48. Outline Introduction Related work Biologically inspired spatial cognition Experimental results Conclusion and Discussion

49. Discussion and conclusions The model proposed capture some behavioral aspects of rats Abilities – Build a holistic topological map in real time – Learn and unlearn goal locations – Exploit the cognitive map to recognize visited places Very simplistic perceptual system – The current model cannot deal with real environments Affordance space and landmark space is discrete – Computationally expensive to process continuous spaces

50. Questions / Comments