Mosam Dabhi

I am a Ph.D. student at the Robotics Institute, Carnegie Mellon University (CMU) where I investigate uncovering the representations that could be used as neural priors and act as a placeholder for human visual intelligence.

My goal is to understand how OOD reasoning (generalization) could be achieved to attain such general multi-modal intelligence in machines. To this end, I am currently focusing on merging geometry-based neural priors and extracting inductive reasoning via graph (causal) representations to tackle this task.

In my opinion: “AI could enable the creation of a versatile language through which all the existing scientific fields could be expressed.”


News

Publications

MBW: Multi-view Bootstrapping in the Wild

MBW: Multi-view Bootstrapping in the Wild

NeurIPS, 2022

By enforcing temporal along with spatial consistencies, MBW carries out Out-of-Distribution (OOD) detection for auto-labeling at scale in a low-shot learning fashion.

High Fidelity 3D Reconstructions with Limited Physical Views

High Fidelity 3D Reconstructions with Limited Physical Views

3DV, 2021

Enforcing multi-view equivariance with modern deep 3D lifting enables generation of high-fidelity 3D reconstructions using just 2-3 cameras, compared to >100 cameras.

Real-Time Information-Theoretic Exploration with Gaussian Mixture Model Maps

Real-Time Information-Theoretic Exploration with Gaussian Mixture Model Maps

RSS, 2019

Representing environment using Gaussian Mixture Models (GMMs) over voxel grids enables map transfer from Mars to Earth in 21 seconds compared to 1260 seconds.

Fast and agile vision‑based flight with teleoperation and collision avoidance on a multirotor

Fast and agile vision‑based flight with teleoperation and collision avoidance on a multirotor

ISER, 2018

Aggressive autonomous flight and collision-free teleoperation in unstructured, GPS-denied environments at speeds exceeding 12 m/s^2.

Aggressive Flight Performance using Robust Experience-driven Predictive Control Strategies: Experimentation and Analysis

Aggressive Flight Performance using Robust Experience-driven Predictive Control Strategies: Experimentation and Analysis

Robotics Institute, CMU (Technical Report), 2018

By storing crucial control policies, you can re-use them at a later stage without spending valuable compute resources on a resource-constrained Micro Air Vehicle (MAV).