Mandayam A. Srinivasan

About

Mandayam A. Srinivasan is a pioneering researcher whose work spans the frontiers of tactile sensing, haptics, and brain-machine interfaces. His major contributions lie in developing artificial touch systems that enable robots to perceive and manipulate objects with human-like dexterity. Srinivasan’s GelSight tactile sensor, a high-resolution imaging device, has revolutionized robotic touch by allowing machines to measure shear forces, detect slip, and estimate object hardness—capabilities critical for delicate manipulation tasks. His work on using deep learning for shape-independent hardness estimation (165 citations) and real-time shear/slip measurement (277 citations) has set new standards in tactile sensing. Beyond touch, Srinivasan has made landmark contributions to neural prosthetics, including the highly cited study on real-time hand trajectory prediction from cortical ensembles (1,495 citations), which has advanced brain-machine interfaces for paralyzed individuals. His research on haptic rendering for virtual objects (183 citations) and in vivo tissue characterization for surgical simulation (72 citations) further demonstrates his impact across robotics, neuroscience, and medicine. Srinivasan’s interdisciplinary approach—bridging tactile sensing, neural control, and haptics—has fundamentally shaped how robots interact with the physical world.

Research Focus

Key Achievements

Top Papers

1. 1
   Real-time prediction of hand trajectory by ensembles of cortical neurons in primates

   1,495 citations · 2000
2. 2
   Measurement of shear and slip with a GelSight tactile sensor

   277 citations · 2015
3. 3
   Localization and manipulation of small parts using GelSight tactile sensing

   254 citations · 2014
4. 4
   Efficient Point-Based Rendering Techniques for Haptic Display of Virtual Objects

   183 citations · 1999
5. 5
   Shape-independent hardness estimation using deep learning and a GelSight tactile sensor

   165 citations · 2017
6. 6
   Continuous Shared Control for Stabilizing Reaching and Grasping With Brain-Machine Interfaces

   123 citations · 2006
7. 7
   Estimating object hardness with a GelSight touch sensor

   92 citations · 2016
8. 8
   In Vivo Mechanical Behavior of Intra-abdominal Organs

   72 citations · 2006
9. 9
   An efficient soft tissue characterization algorithm from <i>in vivo</i> indentation experiments for medical simulation

   33 citations · 2008
10. 10
    Developments in brain–machine interfaces from the perspective of robotics

    19 citations · 2009

Key Collaborators

Contact & Links