Workshop 4: Biological Models

“Biologically motivated models of spatial behavior: insights from animals”

Workshop at Spatial Cognition 2008

Preliminary program: Friday, 19.9.2008, 11:00-18:00
11:00 welcome
(~25-30 min per talk, ~5-10 min discussion)
11:10 Chunrong Yuan, Fabian Recktenwald and Hanspeter A. Mallot:
         Autonomous 3D Navigation of Unmanned Aerial Vehicles Based on Optical Flow
11:45 Darius Burschka and Elmar Mair:
         Biologically Motivated Optical Flow-Based Navigation
12:20 Lorenz Gerstmayr, Frank Roeben and Ralf Moeller
         From Insect Visual Homing to Autonomous Robot Cleaning
13:00-14:00: lunch break
14:05 Kai Basten and Hanspeter A. Mallot:
         Skyline cues for visual outdoor navigation: learning from desert ants
14:40 Paul Graham and Thomas Collett:
         View based navigation using feature attractors: An abstract model
15:15 Michael Mangan and Barbara Webb:
         Comparing alternative computational models of visual homing to cricket behaviour
16:00-16:30: coffee break
16:35 Denis Sheynikhovich, Thomas Stroesslin, Ricardo Chavarriaga, Angelo Arleo and Wulfram Gerstner:
         Is there a geometric module for spatial orientation? Insights from a rodent navigation model
17:10 Hanspeter A. Mallot, Sabine Gillner and Anja M. Weiss:
         Visual Homing in the Absence of Feature-Based Landmark Information
17:45 closing discussion/remarks (end 18:00)

Workshop motivation

"Biology can be viewed as a source of existence proofs for what capabilities might be possible for robots, and of ideas for mechanisms for achieving these capabilities." (B.Webb, 2000).


Animals show amazing navigation abilities: The desert ant Cataglyphis fortis, for example, succeeds extremely well in finding the direct way back to its nest even after long and winding foraging trips in an environment lacking prominent visual landmark information. The investigation and understanding of information processing underlying this and related basic spatial behavior is of great interest for researchers from different fields: The robotics community, for example, may be inspired as how to solve complex and difficult spatial problems in their field. In particular mobile robots with low computing power on-board could benefit from parsimonious models of efficient animal behavior. Biology itself, on the other hand, greatly benefits from implementations of the proposed navigation mechanisms in both, computer simulations and robots, as by these means the behavioral models are tested and quantified. In addition, insight into the true nature of the problem is often provided only by the implementation of a spatial behavior in a real agent. For these reasons, modeling (including implementation on a real robot) has been proven to be a very useful tool for behavioral biologists, giving them hints on how to design relevant further behavioral experiments. Finally, animal models of spatial behavior have been quite influential for research on human spatial cognition (e.g. Wang & Spelke, 2002).

    A prominent example of a biologically inspired model of spatial behavior that has greatly inspired robotics is the so-called “snapshot model”. It was developed to explain the homing behavior of honey-bees (Cartwright and Collett, 1983). The model assumes that bees acquire an image-like spatial representation (the “snapshot”) at relevant locations such as feeders or the hive. When returning to these locations afterwards, the model states that bees continuously compare their current sensory input with the memorized snapshot. Flying direction (i.e., direction to the remembered location) is computed such that the image differences between memorized snapshot and sensory input decreases. While there is an ongoing discussion whether honey-bees indeed use snapshot-like representations (e.g. Fry and Wehner, 2005), and if so, how they achieve robust homing behavior in complex and dynamic environments, the “snapshot model” is one of the most stimulating models of animal navigation. Robotic implementations inspired by the original Cartwright & Collett homing model have proven that a snapshot-based return to previously visited places is indeed feasible, at least in static scenes (e.g. Franz et al., 1998, Zeil, Hofmann, and Chahl, 2003; Vardy and Möller, 2005).
    This example clearly demonstrates that the investigation of sensor-based representations and efficient navigation strategies  may in fact provide promising approaches for the control of autonomous  vehicles with limited computing power

Workshop format

• The workshop will be a half-day event (19th September)
• Speakers will give 30 minute presentations (20 min presentation, 10 min discussion)
• Speakers will be asked to prepare an extended abstract (3-4 pages) that will be circulated beforehand to allow for well-prepared discussions

Important dates

• submission deadline: May 31th, 2008
• notification of acceptance: June 15th, 2008
  
• workshop: September 19th, 2008

How to participate?

• Please email submissions of 3-4 pages (including figures) to jan.wiener [at] cognition.uni-freiburg.de or wolfgang.stuerzl [at] uni-bielefeld.de.
  
• Submissions can be position statements, work in progress, or completed work.

Organizing committee

• Wolfgang Stürzl, Department of Neurobiology, Bielefeld University, wolfgang.stuerzl [at] uni-bielefeld.de
• Jan M. Wiener, Centre for Cognitive Science, University of Freiburg, jan.wiener [at] cognition.uni-freiburg.de

References

• B.A. Cartwright and T.S. Collett. Landmark learning in bees: Experiments and models. Computational Physiology, 151:521–543, 1983.
• M.O. Franz, B. Schölkopf, H.A. Mallot, and H.H. Bülthoff. Where did I take that snapshot?  Scene based homing by image matching. Biological Cybernetics, 79:191–202, 1998. 
• S.N. Fry and R. Wehner. Look and turn: landmark-based goal navigation in honey bees. Journal of Experimental Biology, 208:3945–3955, 2005.
• A. Vardy and R. Möller. Biologically plausible visual homing methods based on optical flow techniques. Connection Science, Special Issue: Navigation, 17: 47–89, 2005.
• F. Wang and E.S. Spelke. Human spatial representations: insights from animals. Trends in Cognitive Science, 6(9): 376–382, 2002 
• B. Webb. What does robotics offer animal behaviour? Animal Behaviour, 60: 545–558,2000
• J. Zeil, M.I. Hofmann, and J.S. Chahl. Catchment areas of panoramic snapshots in outdoor scenes. Journal of the Optical society of America A, 20 (3):450–469, 2003.