Unexpected Situations in Service Robot Environment: Classification and Reasoning Using Naive Physics
Despite perfect functioning of its internal components, a robot can be unsuccessful in performing its tasks because of unforeseen situations. Mostly these situations arise from the interaction of a robot with its ever-changing environment. In this paper we refer to these unsuccessful operations as external unknown faults. We reason along the most frequent failures in typical scenarios which we observed during real-world demonstrations and competitions using our Care-O-bot III robot. These events take place in an apartment-like environment.
We create four different - for now adhoc - fault classes, which refer to faults caused by a) disturbances, b) imperfect perception, c) inadequate planning or d) chaining of action sequences. These four fault classes can then be mapped to a handful of partly known, partly extended fault handling techniques.
In addition to existing techniques we propose an approach that uses naive physics concepts to find information about these kinds of situations. Here the naive physics knowledge is represented by the physical properties of objects which are formalized in a logical framework. The proposed approach applies a qualitative version of physical laws to these properties to reason about the fault. By interpreting the results the robot finds the information about the situations which can cause the fault. We apply this approach to scenarios in which a robot performs manipulation tasks (pick and place). The results show that naive physics hold great promises for reasoning about unknown external faults in the field of robotics.
Keywordsfaults in robotics unexpected situations naive physics
- 1.Nilsson, N.J.: Shakey the robot. Technical Report 323, AI Center, SRI International, 333 Ravenswood Ave, Menlo Park, CA 94025 (April 1984)Google Scholar
- 3.Steinbauer, G.: Survey on faults of robots used in robocup (2012), http://www.ist.tugraz.at/rfs/
- 4.Gspandl, S., Pill, I., Reip, M., Steinbauer, G., Ferrein, A.: Belief management for high-level robot programs. In: 22nd International Joint Conference on Artificial Intelligence (2011)Google Scholar
- 5.Karg, M., Sachenbacher, M., Kirsch, A.: Towards expectation-based failure recognition for human robot interaction. In: 22nd International Workshop on Principles of Diagnosis (2011)Google Scholar
- 6.Sundvall, P., Jensfelt, P.: Fault detection for mobile robots using redundant positioning systems. In: IEEE International Conference on Robotics and Automation, ICRA 2006 (2006)Google Scholar
- 7.Ueda, R., Kakiuchi, Y., Nozawa, S., Okada, K., Inaba, M.: Anytime error recovery by integrating local and global feedback with monitoring task states. In: IEEE, 15th International Conference on Advanced Robotics ICAR, pp. 298–303 (2011)Google Scholar
- 8.Mendoza, J., Veloso, M., Simmons, R.: Motion interference detection in mobile robots. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS (2012)Google Scholar
- 9.Hayes, P.J.: The naive physics manifesto. In: Michie, D. (ed.) Expert Systems in the Micro Electronic Age, pp. 242–270. Edinburgh University Press (1979)Google Scholar
- 10.Davis, E.: The naive physics perplex. AI Magazine 19, 51–79 (1998)Google Scholar
- 11.Kleer, J.D., Brown, J.S.: A qualitative physics based on confluences, pp. 83–126. Morgan Kaufmann Publishers Inc., San Francisco (1990)Google Scholar
- 15.Reiner, M., Slotta, J., Chi, M., Resnick, L.: Naive physics reasoning: A commitment to substance-based conceptions. Cognition and Instruction (2000)Google Scholar
- 16.Akhtar, N.: Fault reasoning based on naive physics. Technical report, Hochschule Bonn-Rhein-Sieg, Sankt Augustin, Germany (April 2011)Google Scholar