Dobb·E: On Bringing Robots Home
Teach a Robot to FISH: Versatile Imitation from One Minute of Demonstrations
Siddhant Haldar
M.S. Computer Science New York University, Courant Graduate School of Arts & Science Email  /  GitHub  /  LinkedIn  /  CV Short Bio Hi! I am an MS in Computer Science student at NYU Courant. In the past, I have worked both as an App & Software Developer, although recently my interest in Deep Learning have lead me to take up more research positions. My main intrest lies in Deep Learning, more specifically representation learning and robot-learning. I also like to work on building scal
Risk Analysis using Trajectory Prediction in Indian Traffic (collaboration with UMD) Rahul Jha*, Anant Rai* , Rahul Kala IEEE ITSC , 2022 paper Built a module using SSD (45 mAP) and SORT to detect and track agents in novel video dataset of Indian Traffic. Developed special LSTM based network architecture to learn the agent’s behaviours and interactions for trajectory prediction in dense heterogeneous traffic. Devised Weighted-Elliptical-Model for risk modelling and combined it with trajectory prediction to
RLMU: Reinforcement Learning with Self-supervised Human Motion Analysis Anant Rai* , Umang Sharma*, Xu Cao* code | pdf Unlabelled videos of humans are a large and almost untapped source of data for robots to learn several routine tasks. The paper RLV proposes a framework that can leverage such videos to enhance learning of challeneging vision-based tasks. The drawback to their model is that it can be extremely unstable and lead to a lot of zero reward rollouts. In our work, we make the framework more robust
Dobb·E: On Bringing Robots Home
Teach a Robot to FISH: Versatile Imitation from One Minute of Demonstrations
Siddhant Haldar