K. Niranjan Kumar — accepted AI-conference papers

2024

BayRnTune: Adaptive Bayesian Domain Randomization via Strategic Fine-tuning

Tianle Huang, Nitish Sontakke, K. Niranjan Kumar, Irfan Essa, Stefanos Nikolaidis, Dennis W. Hong +1

IROS 2024poster

Domain randomization (DR), which entails training a policy with randomized dynamics, has proven to be a simple yet effective algorithm for reducing the gap between simulation and the real world. However, DR often requires careful tuning of randomization parameters. Methods like Bayesian Domain Rando…

Cited by 3 Source ↗Scholar ↗

2023

Cascaded Compositional Residual Learning for Complex Interactive Behaviors

K. Niranjan Kumar, Irfan Essa, Sehoon Ha

RA-L 2023

Real-world autonomous missions often require rich interaction with nearby objects, such as doors or switches, along with effective navigation. However, such complex behaviors are difficult to learn because they involve both high-level planning and low-level motor control. We present a novel framewor

Cited by 16 Source ↗Scholar ↗

2022

Graph-based Cluttered Scene Generation and Interactive Exploration using Deep Reinforcement Learning

K. Niranjan Kumar, Irfan Essa, Sehoon Ha

ICRA 2022poster

We introduce a novel method to teach a robotic agent to interactively explore cluttered yet structured scenes, such as kitchen pantries and grocery shelves, by leveraging the physical plausibility of the scene. We propose a novel learning framework to train an effective scene exploration policy to d…

Cited by 17 Source ↗Scholar ↗