ICRA 2011 Paper Abstract


Paper WeP212.5

O'Callaghan, Simon Timothy (University of Sydney), Singh, Surya (University of Sydney), Alempijevic, Alen (University of Technology Sydney (FEIT)), Ramos, Fabio (University of Sydney)

Learning Navigational Maps by Observing Human Motion Patterns

Scheduled for presentation during the Regular Sessions "Learning and Adaptive Systems II" (WeP212), Wednesday, May 11, 2011, 16:25−16:40, Room 5H

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on July 14, 2020

Keywords Learning and Adaptive Systems, Robot Companions and Social Human-Robot Interaction, Autonomous Navigation


Observing human motion patterns is informative for social robots that share the environment with people. This paper presents a methodology to allow a robot to navigate in a complex environment by observing pedestrian positional traces. A continuous probabilistic function is determined using Gaussian process learning and used to infer the direction a robot should take in different parts of the environment. The approach learns and filters noise in the data producing a smooth underlying function that yields more natural movements. Our method combines prior conventional planning strategies with most probable trajectories followed by people in a principled statistical manner, and adapts itself online as more observations become available. The use of learning methods are automatic and require minimal tuning as compared to potential fields or spline function regression. This approach is demonstrated testing in cluttered office and open forum environments using laser and vision sensing modalities. It yields paths that are similar to the expected human behaviour without any a priori knowledge of the environment or explicit programming



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