Expressing homotopic requirements for mobile robot navigation through natural language instructions

Allowing a human to express topological requirements to a robot in language enables untrained users to guide robot movement without requiring the human to understand sophisticated robot algorithms. By using a homotopy class or classes to represent one or more topological requirements, we build a framework that helps a robot understand a human's intent. This paper reviews a homotopic decomposition method that is used to convert any path into a string, which allows homotopic path equivalence to be performed by comparing strings. We then integrate the Homotopic Distributed Correspondence Graph (HoDCG) to infer the homotopic constraint in the format of strings from a language instruction. Finally, we use a homotopic path-planning algorithm that finds the optimal paths for a given objective and homotopic constraint. Experiment results show how a language instruction is converted into a path driven by an implicit topological requirement.

[1]  John Hershberger,et al.  Computing Minimum Length Paths of a Given Homotopy Class (Extended Abstract) , 1991, WADS.

[2]  Matthew R. Walter,et al.  On the performance of hierarchical distributed correspondence graphs for efficient symbol grounding of robot instructions , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[3]  Luke S. Zettlemoyer,et al.  Learning to Parse Natural Language Commands to a Robot Control System , 2012, ISER.

[4]  Matthew R. Walter,et al.  Understanding Natural Language Commands for Robotic Navigation and Mobile Manipulation , 2011, AAAI.

[5]  Matthew R. Walter,et al.  A multimodal interface for real-time soldier-robot teaming , 2016, SPIE Defense + Security.

[6]  Subhrajit Bhattacharya,et al.  Search-Based Path Planning with Homotopy Class Constraints in 3D , 2010, AAAI.

[7]  B. Chandrasekaran,et al.  A framework of Voronoi diagram for planning multiple paths in free space , 2013, J. Exp. Theor. Artif. Intell..

[8]  Stefanie Tellex,et al.  A natural language planner interface for mobile manipulators , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[9]  Michael A. Goodrich,et al.  Homotopy-aware RRT*: Toward human-robot topological path-planning , 2016, 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[10]  Jorge Nocedal,et al.  On the limited memory BFGS method for large scale optimization , 1989, Math. Program..

[11]  Dima Grigoriev,et al.  Polytime algorithm for the shortest path in a homotopy class amidst semi-algebraic obstacles in the plane , 1998, ISSAC '98.

[12]  Stefanie Tellex,et al.  Toward understanding natural language directions , 2010, HRI 2010.

[13]  Michael A. Goodrich,et al.  Supporting task-oriented collaboration in human-robot teams using semantic-based path planning , 2014, Defense + Security Symposium.

[14]  Jean Oh,et al.  Grounding spatial relations for outdoor robot navigation , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[15]  Pere Ridao,et al.  A comparison of homotopic path planning algorithms for robotic applications , 2015, Robotics Auton. Syst..

[16]  Raymond J. Mooney,et al.  Learning to Interpret Natural Language Navigation Instructions from Observations , 2011, Proceedings of the AAAI Conference on Artificial Intelligence.