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Ansh Shah

I am currently a research assistant at the Robotics Research Center (RRC) , IIIT Hyderabad, under the guidance of Prof Madhava Krishna . My interest lies in the field of Robot Learning and 3D Vision.

I graduated from BITS Pilani in 2023 with an M.Sc. in Physics and a B.E. in Mechanical Engineering. I joined RRC, IIIT Hyderabad for my Bachelor's Thesis, where now I am continuing as a Research Assistant. I was also fortunate to have opportunities to colloborate with Prof Arun Kumar Singh (University of Tartu, Estonia), Prof Chetan Arora (IIT Delhi) over my time here.



Research Interests & Ideas

My research interests focus on the trio of action, perception, and cognition, where I aim to build machines that can perceive and understand the real world, recognize the physical consequences of their actions, and explore novel behaviors to achieve complex goals. I am especially driven by the challenge of creating embodiment aware generalist agents that can adapt to new tasks without task-specific training data. I believe that high level cognition can be easliy mimicked (Moravec's paradox). We are limited on the low level control front. Contrary to the belief of learning low-level manipulation with imitation learning of human-teleoperated demonstrations or skill transfer from videos, I believe that robots should learn low level control by learning state based local policies with RL which helps in exploring their own capabilities. A few fundamental question in the field of robot learning that I am interested in are: 1. At what level in the TAMP hierarchy should we build robust representations for embodiment generalisation? 2. How can we learn to control robots in a data efficient manner? 3. Can learning a grounded 3D scene representation help in learning better policies?

Email: anshshahresearch@gmail.com / anshshah3009@gmail.com

Highlights

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Timeline

Publications

* Denotes equal contribution


MetricGold: Repurposing Diffusion-Based Image Generators for Metric Depth Estimation

Ansh Shah, K. Madhava Krishna
[Arxiv]

This paper explores an approach for monocular metric depth estimation that utilizes generative diffusion models and a log-scaled metric depth representation, achieving sharper and more accurate metric depth predictions across diverse scenes through synthetic data training.


Projects

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Major Projects

DeepKoopman with Control Koopman Theory in Deep Learning for Linearizing Drone Dynamics

Bhanu Teja*, Ansh Shah*, Mihir Ungarala*, Prof Arun Singh, Prof K Madhava Krishna
Robotics Research Center, IIIT Hyderabad

Can dynamics linearisation help in computationally cheaper and faster control of drones? We explore the idea of using Koopman theory to linearise the dynamics of a drone and then use a LinearMPC to control it.


Object Associtaion in rearranged scenes for change detection and loop closure registration with pairwise geometric consistency

Ansh Shah*, Aneesh Chavan*, Sarthak Chittawar, Dr. Krishna Murthy , Prof Chetan Arora, Prof K Madhava Krishna
Robotics Research Center, IIIT Hyderabad

An Embodied agent in a household setting has to localise itself in rearranged indoor spaces. Relying on normal descriptors can be tricky as small feature rich objects can get rearranges. To mitigate this problem we explored using object instance based localisation.


Multi-Agent SLAM Multi-Agent SLAM

Bhanu Teja*, Ansh Shah*, Mihir Ungarala*, Prof Amit Singh, Prof K Madhava Krishna
Robotics Research Center, IIIT Hyderabad | BITS Pilani


Autonomous Drone

Ansh Shah, Nidheesh Jain, Nikhil Agarwal
Team BITS Robocon
Github


Mini Projects

Micromoue

Ansh Shah, Samyak Sahu
Team BITS Robocon
Github


CRONUS: A Quadrupedal + Holonomic Drive surveillance bot

Ayush Agarwal, Ansh Shah, Atharv Arora, Ananya Khandelwal
BITS Pilani
Github


Teaching


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