Apoorva Khairnar
Apoorva Khairnar has a total of 10 co-authors. Here are the top co-authors by number of shared publications:
- A. K. Kelkar
College of Engineering
1 shared publication - Arman Tekinalp
Department of Mechanical Science and Engineering
1 shared publication - H. S. Khade
College of Engineering
1 shared publication Keshav Shivam
The Grainger College of Engineering
1 shared publication- Mattia Gazzola
Mechanical Science and Engineering University of Illinois at Urbana‐Champaign Urbana Illinois USA
1 shared publication - Noel Naughton
Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA, USA
1 shared publication
Latest Publications
- Open Access
- 24 April 2026
- Published by National Academy of Sciences in Proceedings of the National Academy of Sciences
- Vol. 123 (17)
Neural reservoir control of a bio-hybrid soft arm
A long-standing engineering problem, the control of soft robots is difficult because of their highly nonlinear, heterogeneous, anisotropic, and distributed nature. Here, bridging engineering and biology, neural reservoirs are employed for the dynamic control of a bio-hybrid model arm made of multiple muscle-tendon groups enveloping an elastic spine. We show how the use of reservoirs facilitates simultaneous control and self-modeling across challenging tasks, outperforming classic neural network approaches. Further, through the use of spiking reservoirs on neuromorphic hardware, energy efficiency gains of up to 75 and 45 times are obtained relative to standard and high-efficiency CPUs, with implications for the on-board control of untethered, small-scale systems.
- 24 April 2026
- Published by National Academy of Sciences in Proceedings of the National Academy of Sciences
- Vol. 123 (17)
Open AccessNeural reservoir control of a bio-hybrid soft arm
A long-standing engineering problem, the control of soft robots is difficult because of their highly nonlinear, heterogeneous, anisotropic, and distributed nature. Here, bridging engineering and biology, neural reservoirs are employed for the dynamic control of a bio-hybrid model arm made of multiple muscle-tendon groups enveloping an elastic spine. We show how the use of reservoirs facilitates simultaneous control and self-modeling across challenging tasks, outperforming classic neural network approaches. Further, through the use of spiking reservoirs on neuromorphic hardware, energy efficiency gains of up to 75 and 45 times are obtained relative to standard and high-efficiency CPUs, with implications for the on-board control of untethered, small-scale systems.
- 29 November 2022
- Published by Springer Nature
- p. 201-213
A Model for Prediction of Water Level and Pressure in an Industrial Boiler Using Multivariate Regression
- 29 November 2022
- Published by Springer Nature
- p. 201-213
A Model for Prediction of Water Level and Pressure in an Industrial Boiler Using Multivariate Regression