Balancing Shared Autonomy with Human-Robot Communication

Robotic agents that share autonomy with a human should leverage human domain knowledge and account for their preferences when completing a task. This extra knowledge can dramatically improve plan efficiency and user-satisfaction, but these gains are lost if communicating with a robot is taxing and unnatural. In this paper, we show how viewing humanrobot language through the lens of shared autonomy explains the efficiency versus cognitive load trade-offs humans make when deciding how cooperative and explicit to make their instructions.

[1]  Dinesh Manocha,et al.  Generating Realtime Motion Plans from Complex Natural Language Commands Using Dynamic Grounding Graphs , 2017, ArXiv.

[2]  Nisar R. Ahmed,et al.  Bayesian Multicategorical Soft Data Fusion for Human–Robot Collaboration , 2013, IEEE Transactions on Robotics.

[3]  Maya Cakmak,et al.  Robot Programming by Demonstration with situated spatial language understanding , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

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

[5]  Guido Bugmann,et al.  Mobile robot programming using natural language , 2002, Robotics Auton. Syst..

[6]  Frederik W. Heger,et al.  Sliding Autonomy for Complex Coordinated Multi-Robot Tasks: Analysis & Experiments , 2006, Robotics: Science and Systems.

[7]  Luke S. Zettlemoyer,et al.  A Joint Model of Language and Perception for Grounded Attribute Learning , 2012, ICML.

[8]  Matthew R. Walter,et al.  Learning models for following natural language directions in unknown environments , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[9]  Dinesh Manocha,et al.  Efficient Generation of Motion Plans from Attribute-Based Natural Language Instructions Using Dynamic Constraint Mapping , 2017, 2019 International Conference on Robotics and Automation (ICRA).

[10]  Michael A. Goodrich,et al.  Expressing homotopic requirements for mobile robot navigation through natural language instructions , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[11]  Sandra G. Hart,et al.  Nasa-Task Load Index (NASA-TLX); 20 Years Later , 2006 .

[12]  Siddhartha S. Srinivasa,et al.  Shared Autonomy via Hindsight Optimization , 2015, Robotics: Science and Systems.

[13]  Daniel Marcu,et al.  Natural Language Communication with Robots , 2016, NAACL.

[14]  Luke S. Zettlemoyer,et al.  Weakly Supervised Learning of Semantic Parsers for Mapping Instructions to Actions , 2013, TACL.

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

[16]  Siddhartha S. Srinivasa,et al.  Legibility and predictability of robot motion , 2013, 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

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

[18]  Ross A. Knepper,et al.  Asking for Help Using Inverse Semantics , 2014, Robotics: Science and Systems.

[19]  Kazuya Yoshida,et al.  Shared autonomy system for tracked vehicles on rough terrain based on continuous three‐dimensional terrain scanning , 2011, J. Field Robotics.

[20]  Tomomasa Sato,et al.  Language-aided robotic teleoperation system (LARTS) for advanced teleoperation , 1987, IEEE Journal on Robotics and Automation.

[21]  Siddhartha S. Srinivasa,et al.  Incorporating qualitative information into quantitative estimation via Sequentially Constrained Hamiltonian Monte Carlo sampling , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[22]  Siddhartha S. Srinivasa,et al.  A System for Multi-step Mobile Manipulation: Architecture, Algorithms, and Experiments , 2016, ISER.

[23]  Joyce Yue Chai,et al.  Embodied Collaborative Referring Expression Generation in Situated Human-Robot Interaction , 2015, 2015 10th ACM/IEEE International Conference on Human-Robot Interaction (HRI).