Interactive Robot Perception & Learning

Snehal Jauhri

I joined the PEARL/iROSA group at TU Darmstadt as a PhD researcher in June 2021. My research is focused on machine learning & computer vision for robotic perception and mobile manipulation, supervised by Dr. Georgia Chalvatzaki.

Before starting my PhD, I completed my M.Sc. from the Delft University of Technology, specializing in robot learning, controls, and embedded systems. For my master’s thesis, I worked with Dr. Jens Kober and Dr. Carlos Celemin on teaching robots through interactive imitation learning.

Research Interests

Robot Learning, 3D Computer Vision, Neural Fields, Reinforcement Learning

Key references

Jauhri, S.; Peters, J.; Chalvatzaki G. (2022). Robot Learning of Mobile Manipulation with Reachability Behavior Priors, in IEEE Robotics and Automation Letters (RA-L), and 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), vol. 7, no. 3, pp.8399-8406.
Best Mobile Manipulation Paper Award 🏆
Webpage Paper Code

Jauhri, S*.; Lueth, S*.; Chalvatzaki G. (2024). Active-Perceptive Motion Generation for Mobile Manipulation, 2024 IEEE International Conference on Robotics and Automation (ICRA).
Webpage Paper Code (coming soon)

Jauhri, S.; Lunawat, I.; Chalvatzaki G. (2023). Learning Any-View 6DoF Robotic Grasping in Cluttered Scenes via Neural Surface Rendering, (Under review), Presented at CVPR 2023 Workshop on 3D Vision & Robotics, Preprint arXiv:2306.07392.
Webpage Paper Code (coming soon)

Liu, P.; Zhang, K.; Tateo, D.; Jauhri, S.; Hu, Z.; Peters, J. Chalvatzaki, G. (2023). Safe Reinforcement Learning of Dynamic High-Dimensional Robotic Tasks: Navigation, Manipulation, Interaction, 2023 IEEE International Conference on Robotics and Automation (ICRA).
Webpage Paper Code

Liu, P.; Zhang, K.;Tateo D.; Jauhri S.; Peters J.; Chalvatzaki G.; (2022). Regularized Deep Signed Distance Fields for Reactive Motion Generation, 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
Webpage Paper Code

Jauhri, S.; Celemin, C.; Kober, J. (2020). Interactive Imitation Learning in State-Space, Proceedings of the 2020 Conference on Robot Learning (CoRL), 155, pp.682-692, PMLR.
Paper Code

Curriculum Vitae

Contact:

> Email: snehal<at>robot-learning.de, snehal.jauhri<at>tu-darmstadt.de
> Linkedin
> Postal address:
TU Darmstadt, FG PEARL,
Landwehrstraße 50a, Darmstadt, 64293
Office. Room 4, Hessian AI (S4|23)