Bio
Gowrishankar Ganesh received his Bachelor of Engineering (first-class, Hons.) degree from the Delhi College of Engineering, India, in 2002 and his Master of Engineering from the National University of Singapore, in 2005, both in Mechanical Engineering. He received his Ph.D. in Bioengineering from Imperial College London, U.K., in 2010. He worked as a Researcher in Human Motor Control in the Lab of Dr. Mitsuo Kawato at the Advanced Telecommunication Research (ATR), Kyoto, Japan, from 2004 and through his PhD. Following his PhD, he worked at the National Institute of Information and Communications Technology (NICT) as a Specialist Researcher in Motor Neuroscience and Robotics till December 2013. Since January 2014, he has joined as a CR1 Researcher at the Centre National de la Recherché Scientifique (CNRS). He is a visiting researcher at the Centre for Information and Neural Networks (CINET) in Osaka, ATR in Kyoto and the Interactive Digital Humans (IDH) team at the Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier (LIRMM) in Montpellier. His research interests include human sensori-motor control and learning, robot control, social neuroscience and robot-human interactions.
Research interests
How should rehabilitation, biomedical and social robots behave such that an interacting human is comfortable with them, feels safe with them and is willing to coexist and learn from them? The answer to this questions is not trivial because human interactions, both with their environment and other humans, have complex dynamics that change not only with an interacting individual’s physiology, age and pathology, but also emotional factors like fear and anxiety, and cognitive factors like the Theory of Mind. The reason we can intuitively interact with a fellow human is because we understand his/her behaviour in all these aspects and respond accordingly, and correspondingly he/she does the same. My research aims to endow machines and robots with similar capabilities to understand humans through the development of _Human Centric Robotics_**. You can read more here.
Publications
| Title | Authors | Conference/Book | Year | bib | |
| Hierarchical motor adaptations negotiate failures during force field learning | T. Ikegami, G. Ganesh, T. Gibo, T. Yoshioka, R. Osu, M. Kawato | PLOS Computational Biology | 2021 | ||
| Galvanic Vestibular Stimulation-Based Prediction Error Decoding and Channel Optimization | Y. Shi, G. Ganesh, H. Ando, Y. Koike, E. Yoshida, N. Yoshimura | International Journal of Neural Systems | 2021 | ||
| Binary Semantic Classification using Cortical Activation with Pavlovian-conditioned Vestibular Responses in Healthy and Locked-in Individuals | N. Yoshimura, K. Umetsu, A. Tonin, Y. Maruyama, K. Harada, A. Rana, G. Ganesh, U. Chaudhary, Y. Koike, N. Birbaumer | Cerebral Cortex Communications | 2021 | ||
| A Deep Learning Framework for Tactile Recognition of Known as Well as Novel Objects. | Z. Abderrahmane, G. Ganesh, A. Crosnier, A. Cherubini | IEEE Transactions on Industrial Informatics | 2020 | ||
| Robot Movement Uncertainty Determines Human Discomfort in Co-worker Scenarios | D. Héraiz-Bekkis, G. Ganesh, E. Yoshida, N. Yamanobe | IEEE International Conference on Control, Automation and Robotics | 2020 | ||
| The Tight Game: Implicit Force Intervention in Inter-personal Physical Interactions on Playing Tug of War | A. Maekawa, S. Kasahara, H. Saito, D. Uriu, G. Ganesh, M. Inami | SIGGRAPH | 2020 | ||
| Individuals Prioritize the Reach Straightness and Hand Jerk of a Shared Avatar over Their Own | T. Hagiwara, G. Ganesh, M. Sugimoto, M. Inami, M. Kitazaki | iScience | 2020 | ||
| The where of handovers by humans: Effect of partner characteristics, distance and visual feedback | S. Kato, N. Yamanobe, G. Venture, E. Yoshida, G. Ganesh | PLoS One | 2019 | ||
| Activity in the dorsal ACC causes deterioration of sequential motor performance due to anxiety | G. Ganesh, T. Minamoto, M. Haruno | Nature Communications | 2019 | ||
| Haptic Zero-Shot Learning: Recognition of objects never touched before | Z. Abderrahmane, G. Ganesh, A. Crosnier, A. Cherubini | Robotics and Autonomous Systems | 2018 | ||
| Utilizing sensory prediction errors for movement intention decoding: A new methodology | G. Ganesh, K. Nakamura, S. Saetia, A. Tobar, E. Yoshida, H. Ando, N. Yoshimura, Y. Koike | Science Advances | 2018 | ||
| Force, Impedance and Trajectory Learning for Contact Tooling and Haptic Identification | Y. Li, G. Ganesh, N. Jarrasse, S. Haddadin, A. Albu-Schaffer, E. Burdet | IEEE Transactions on Robotics | 2018 | ||
| Humans Can Predict Where Their Partner Would Make a Handover | S. Kato, N. Yamanobe, G. Venture, G. Ganesh | International Conference on Human-Robot Interaction | 2018 | ||
| Visuo-Tactile Recognition of Daily-Life Objects Never Seen or Touched Before | Z. Abderrahmane, G. Ganesh, A. Crosnier, A. Cherubini | International Conference on Control, Automation, Robotics and Vision | 2018 | ||
| Towards Emergence of Tool Use in Robots: Automatic Tool Recognition and use without Prior Tool Learning | K. Tee, J. Li, L. Chen, K. Wan, G. Ganesh | IEEE International Conference on Robotics and Automation | 2018 | ||
| Distinct motor contagions during and after observation of actions by a humanoid co-worker | A. Vasalya, G. Ganesh, A. Kheddar | International Symposium on Robot and Human Interactive Communication | 2018 | ||
| Accurate Decoding of Material Textures Using a finger Mounted Accelerometer | K. Sakurada, G. Ganesh, W. Yu, K. Kita | International Conference on Robotics and Biomimetics | 2018 | ||
| More than just co-worker influences human performance | A. Vasalya, G. Ganesh, A. Kheddar | PLoS One | 2018 | ||
| Haptic communication between humans is tuned by the hard or soft mechanics of interaction | A. Takagi, F. Usai, G. Ganesh, V. Sanguineti, E. Burdet | PLOS Computational Biology | 2018 | ||
| Prediction error induced motor contagions in human behaviors | T. Ikegami, G. Ganesh, T. Takeuchi, H. Nakamoto | eLife | 2018 | ||
| Physically interacting individuals estimate the partner's goal to enhance their movements | A. Takagi, G. Ganesh, T. Yoshioka, M. Kawato, E. Burdet | Nature Human Behaviour | 2017 | ||
| Hitting the sweet spot: automatic optimization of energy transfer during tool-held hits | J. Vogel, N. Takemura, H. Hoppner, P. Van Der Smagt, G. Ganesh | IEEE International Conference on Robotics and Automation | 2017 | ||
| Presence and absence of prediction errors during action observation induce distinct motor contagions. | T. Ikegami, G. Ganesh, H. Nakamoto | Society for Neuroscience | 2017 | ||
| Activity in the dorsal ACC causes deterioration of sequential motor performance due to anxiety | G. Ganesh, T. Minamoto, M. Haruno | Nature Communications | 2017 | ||
| Non-human Looking Robot Arms Induce Illusion of Embodiment | L. Aymerich-Franch, D. Petit, G. Ganesh, A. Kheddar | International Journal of Social Robotics | 2017 | ||
| Shared Mechanisms in the Estimation of Self-Generated Actions and the Prediction of Other's Actions by Humans | T. Ikegami, G. Ganesh | eNeuro | 2017 | ||
| Object Touch by a Humanoid Robot Avatar Induces Haptic Sensation in the Real Hand | L. Aymerich-Franch, D. Petit, G. Ganesh, A. Kheddar | Journal of Computer-Mediated Communication | 2017 | ||
| The second me: seeing the real body during humanoid embodiment produces an illusion of bi-location. | L. Aymerich-Franch, D. Petit, G. Ganesh, A. Kheddar | Consciousness and Cognition | 2016 | ||
| Dissociable learning processes underlie human pain conditioning | S. Zhang, H. Mano, G. Ganesh, T. Robbins, B. Seymour | Current Biology | 2016 | ||
| The role of functionality in the body model for self-attribution | L. Aymerich-Franch, G. Ganesh | Neuroscience Research | 2016 | ||
| Forward modelling the rubber hand: illusion of ownership modifies motor-sensory predictions by the brain | L. Aymerich-Franch, D. Petit, A. Kheddar, G. Ganesh | Royal Society Open Science | 2016 | ||
| Multiple learning processes underlie human pain conditioning | S. Zhang, H. Mano, G. Ganesh, T. Robbins, B. Seymour | Current Biology | 2016 | ||
| Beyond Watching: Action understanding by humans and its implications for interacting robots | G. Ganesh, T. Ikegami | Dance Notations and Robot Motion, in the Springer Tracts in Advanced Robotics | 2016 | ||
| Embodiment of a humanoid robot is preserved during partial and delayed control | L. Aymerich-Franch, D. Petit, G. Ganesh, A. Kheddar | IEEE International Conference on Advanced Robotics and its Social Impacts | 2015 | ||
| Watching novice action degrades expert’s performance- Evidence that the motor system is involved in action understanding by humans. | T. Ikegami, G. Ganesh | Nature Scientific Reports | 2014 | ||
| Immediate tool incorporation processes determine human motor planning with tools | G. Ganesh, T. Yoshioka, R. Osu, T. Ikegami | Nature Communications | 2014 | ||
| Two is better than one: Physical interactions improve motor performance in humans | G. Ganesh, A. Takagi, R. Osu, T. Yoshioka, M. Kawato, E. Burdet | Scientific Reports | 2014 | ||
| Watching novice action degrades expert's performance -- Evidence that the motor system is involved in action understanding by humans | T. Ikegami, G. Ganesh | Scientific Reports | 2014 | ||
| Artificial proprioceptive feedback for myoelectric control | T. Pistohl, D. Joshi, G. Ganesh, A. Jackson, K. Nazarpour | IEEE Transactions on Neural Systems and Rehabilitation Engineering | 2014 | ||
| Two is better than one: Physical interactions improve motor performance in humans | G. Ganesh, A. Takagi, R. Osu, T. Yoshioka, M. Kawato, E. Burdet | Nature Scientific Reports | 2014 | ||
| Immediate tool incorporation processes determine human motor planning with tools. | G. Ganesh, T. Yoshioka, R. Osu, T. Ikegami | Nature Communications | 2014 | ||
| Artificial Proprioceptive Feedback for Myoelectric Prosthesis Control | T Pistohl, D Joshi, G. Ganesh, A Jackson, K. Nazarpour | IEEE Neural Systems & Rehabilitation Engineering | 2014 | ||
| Feeling the force: Returning sensory signals determine effort expenditure during motor coordination | G. Ganesh, R. Osu, E. Naito | Nature Scientific Reports | 2013 |