Learning to Arbitrate Human and Robot Control using Disagreement between Sub-Policies

E. Gumbel, et al. , “The circular normal distribution: Theory and tables,” Journal of the American Statistical Association , vol. 48, no. 261, pp. 131–152, 1953

1953

Earlier work this paper cites.

D.-J. Kim, et al. , “How autonomy impacts performance and satisfaction: Results from a study with spinal cord injured subjects using an assistive robot,” IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans , vol. 42, no. 1, pp. 2–14, 2011

2011

Earlier work this paper cites.

A. Goil, et al. , “Using machine learning to blend human and robot controls for assisted wheelchair navigation,” in 2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR) , 2013

2013

Earlier work this paper cites.

A. D. Dragan and S. S. Srinivasa, “A policy-blending formalism for shared control,” The International Journal of Robotics Research , vol. 32, no. 7, pp. 790–805, 2013

2013

Earlier work this paper cites.

T. P. Lillicrap, et al. , “Continuous control with deep reinforcement learning,” arXiv preprint arXiv:1509.02971 , 2015

Original

2015

Earlier work this paper cites.

V. Mnih, et al. , “Human-level control through deep reinforcement learning,” Nature , vol. 518, no. 7540, pp. 529–533, 2015

2015

Earlier work this paper cites.

W. Xu, et al. , “Reinforcement learning-based shared control for walking-aid robot and its experimental verification,” Advanced Robotics , vol. 29, no. 22, pp. 1463–1481, 2015

2015

Earlier work this paper cites.

C. Phillips-Grafflin, et al. , “From autonomy to cooperative traded control of humanoid manipulation tasks with unreliable communication,” Journal of Intelligent & Robotic Systems , vol. 82, no. 3-4, pp. 341–361, 2016

2016

Earlier work this paper cites.

M. Johns, et al. , “Exploring shared control in automated driving,” ACM/IEEE Int. Conf. on Human-Robot Interaction (HRI) , 2016

2016

Earlier work this paper cites.

D. Gopinath, et al. , “Human-in-the-loop optimization of shared autonomy in assistive robotics,” IEEE Robotics and Automation Letters , vol. 2, no. 1, pp. 247–254, 2016

2016

Cited alongside, same era.

D. Silver, et al. , “Mastering the game of go with deep neural networks and tree search,” Nature , vol. 529, no. 7587, pp. 484–489, 2016

2016

Cited alongside, same era.

J. Mainprice, et al. , “Warping the workspace geometry with electric potentials for motion optimization of manipulation tasks,” IEEE/RSJ Int. Conf. on Intel. Robots And Systems (IROS) , 2016

2016

Cited alongside, same era.

K. Muelling, et al. , “Autonomy infused teleoperation with application to brain computer interface controlled manipulation,” Autonomous Robots , vol. 41, no. 6, pp. 1401–1422, 2017

2017

Cited alongside, same era.

C. Schultz, et al. , “Goal-predictive robotic teleoperation from noisy sensors,” IEEE Int. Conf. Robotics And Automation (ICRA) , 2017

S. Reddy, et al. , “Shared autonomy via deep reinforcement learning,” arXiv preprint arXiv:1802.01744 , 2018

Original

2018

Later among the works it cites.

——, “Highly parallelized data-driven mpc for minimal intervention shared control,” Robotics: Science and Systems (RSS) , 2019

2019

Later among the works it cites.

S. Jain and B. Argall, “Probabilistic human intent recognition for shared autonomy in assistive robotics,” ACM Transactions on Human-Robot Interaction (THRI) , vol. 9, no. 1, pp. 1–23, 2019

2019

Later among the works it cites.

Y. Oh, et al. , “Natural gradient shared control,” IEEE Int. Symp. on Robot and Human Interactive Communication (RO-MAN) , 2020

2020

Later among the works it cites.

C. Schaff and M. R. Walter, “Residual policy learning for shared autonomy,” arXiv preprint arXiv:2004.05097 , 2020

Original