Sanjay Krishnan
University of California
5 Papers
11 Citations
Sanjay Krishnan is an academic researcher from University of California. The author has contributed to research in topics: Markov model & Invariant (mathematics). The author has an hindex of 2, co-authored 5 publications.
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Papers
Generalizing Robot Imitation Learning with Invariant Hidden Semi-Markov Models
Ajay Kumar Tanwani,Ajay Kumar Tanwani,Jonathan Lee,Brijen Thananjeyan,Michael Laskey,Sanjay Krishnan,Roy Fox,Ken Goldberg,Sylvain Calinon +8 more
- 09 Dec 2018
TL;DR: An algorithm that learns a joint probability density function of the demonstrations with invariant formulations of hidden semi-Markov models to extract invariant segments, and smoothly follow the generated sequence of states with a linear quadratic tracking controller allows a Baxter robot to learn a pick-and-place task while avoiding a movable obstacle based on only 4 kinesthetic demonstrations.
Generalizing Robot Imitation Learning with Invariant Hidden Semi-Markov Models
Ajay Kumar Tanwani,Ajay Kumar Tanwani,Jonathan Lee,Brijen Thananjeyan,Michael Laskey,Sanjay Krishnan,Roy Fox,Ken Goldberg,Sylvain Calinon +8 more
- 22 Jan 2019
TL;DR: In this paper, the authors propose an algorithm that learns a joint probability density function of the demonstrations with invariant formulations of hidden semi-Markov models to extract invariant segments (also called sub-goals or options), and smoothly follow the generated sequence of states with a linear quadratic tracking controller.
•Posted Content
Generalizing Robot Imitation Learning with Invariant Hidden Semi-Markov Models
Ajay Kumar Tanwani,Ajay Kumar Tanwani,Jonathan Lee,Brijen Thananjeyan,Michael Laskey,Sanjay Krishnan,Roy Fox,Ken Goldberg,Sylvain Calinon +8 more
TL;DR: An algorithm that learns a joint probability density function of the demonstrations with invariant formulations of hidden semi-Markov models to extract invariant segments, and smoothly follow the generated sequence of states with a linear quadratic tracking controller allows a Baxter robot to learn a pick-and-place task while avoiding a movable obstacle based on only 4 kinesthetic demonstrations.
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Robot Learning with Invariant Hidden Semi-Markov Models
Ajay Kumar Tanwani,Jonathon Lee,Michael Laskey,Sanjay Krishnan,Roy Fox,Ken Goldberg +5 more
- 30 Jun 2018
TL;DR: This paper learns a joint probability density function of the demonstrations with invariant formulations of hidden semi-Markov model, and smoothly follow the generated sequence of states with a linear quadratic tracking controller.