GLIDE-RL: Grounded Language Instruction through DEmonstration in RL

no code implementations • 3 Jan 2024 • Chaitanya Kharyal, Sai Krishna Gottipati, Tanmay Kumar Sinha, Srijita Das, Matthew E. Taylor

However, training efficient Reinforcement Learning (RL) agents grounded in natural language has been a long-standing challenge due to the complexity and ambiguity of the language and sparsity of the rewards, among other factors.

Continual Learning reinforcement-learning +1

Spatial Relation Graph and Graph Convolutional Network for Object Goal Navigation

no code implementations • 27 Aug 2022 • D. A. Sasi Kiran, Kritika Anand, Chaitanya Kharyal, Gulshan Kumar, Nandiraju Gireesh, Snehasis Banerjee, Ruddra dev Roychoudhury, Mohan Sridharan, Brojeshwar Bhowmick, Madhava Krishna

This paper describes a framework for the object-goal navigation task, which requires a robot to find and move to the closest instance of a target object class from a random starting position.

Bayesian Inference Object +2

RP-VIO: Robust Plane-based Visual-Inertial Odometry for Dynamic Environments

1 code implementation • 18 Mar 2021 • Karnik Ram, Chaitanya Kharyal, Sudarshan S. Harithas, K. Madhava Krishna

We evaluate our approach on this dataset, and three diverse sequences from standard datasets including two real-world dynamic sequences and show a significant improvement in robustness and accuracy over a state-of-the-art monocular visual-inertial odometry system.