Opening Doors: An Initial SRL Approach

Opening doors is an essential task that a robot should perform. In this paper, we propose a logical approach to predict the action of opening doors, together with the action point where the action should be performed. The input of our system is a pair of bounding boxes of the door and door handle, together with background knowledge in the form of logical rules. Learning and inference are performed with the probabilistic programming language ProbLog. We evaluate our approach on a doors dataset and we obtain encouraging results. Additionally, a comparison to a propositional decision tree shows the benefits of using a probabilistic programming language such as ProbLog.

[1]  Alberto Maria Segre,et al.  Programs for Machine Learning , 1994 .

[2]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[3]  Martin A. Riedmiller,et al.  A learned feature descriptor for object recognition in RGB-D data , 2012, 2012 IEEE International Conference on Robotics and Automation.

[4]  Luc De Raedt,et al.  ProbLog: A Probabilistic Prolog and its Application in Link Discovery , 2007, IJCAI.

[5]  Luc De Raedt,et al.  A Relational Distance-based Framework for Hierarchical Image Understanding , 2012, ICPRAM.

[6]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[7]  Ashutosh Saxena,et al.  Robotic Grasping of Novel Objects , 2006, NIPS.

[8]  Wolfram Burgard,et al.  Point feature extraction on 3D range scans taking into account object boundaries , 2011, 2011 IEEE International Conference on Robotics and Automation.

[9]  Quoc V. Le,et al.  High-accuracy 3D sensing for mobile manipulation: Improving object detection and door opening , 2009, 2009 IEEE International Conference on Robotics and Automation.

[10]  Luc De Raedt,et al.  Probabilistic Rule Learning , 2010, ILP.

[11]  Xiaodong Yang,et al.  Context-based indoor object detection as an aid to blind persons accessing unfamiliar environments , 2010, ACM Multimedia.

[12]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[13]  Luc De Raedt,et al.  Learning the Parameters of Probabilistic Logic Programs from Interpretations , 2011, ECML/PKDD.

[14]  Luc De Raedt,et al.  nFOIL: Integrating Naïve Bayes and FOIL , 2005, AAAI.

[15]  Benjamin Rosman,et al.  Learning spatial relationships between objects , 2011, Int. J. Robotics Res..

[16]  Luc De Raedt,et al.  On the implementation of the probabilistic logic programming language ProbLog , 2010, Theory and Practice of Logic Programming.

[17]  Peter A. Flach,et al.  Evaluation Measures for Multi-class Subgroup Discovery , 2009, ECML/PKDD.

[18]  Dieter Fox,et al.  Object recognition with hierarchical kernel descriptors , 2011, CVPR 2011.

[19]  Luc De Raedt,et al.  Probabilistic Inductive Logic Programming , 2004, Probabilistic Inductive Logic Programming.

[20]  Dmitry Berenson,et al.  Grasp planning in complex scenes , 2007, 2007 7th IEEE-RAS International Conference on Humanoid Robots.

[21]  Luc De Raedt Logical and Relational Learning , 2008, SBIA.