K. Niranjan Kumar

I am a PhD candidate at Georgia Institute of Technology working with Sehoon Ha and Irfan Essa. My research is in the union of

Robot learning + Legged robots + Interactive perception

I am in the job market for post-doc and industry research roles in robotics. Please reach out to me if you have any opportunities.

Email  /  CV  /  Google Scholar  /  Twitter

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  • 12/2023: I was invited to present my work at Can we build Baymax? workshop at Humanoids 2023.
  • 11/2023: Presented my recent work "Words into action" at CoRL 2023 LangRob workshop.
  • 08/2023: My work CCRL now published as an article on Robotics and Automation Letters 2023


My current research focus involves creating learning-based policies that enable high-DOF robots to exhibit natural fluid behaviors, all while reducing the reliance on intricate reward engineering. To this end, my ongoing work involves using Large Language Models (LLMs) for iterative, human-guided refinement for learned control policies.

Words into Action: Learning Diverse Humanoid Robot Behaviors using Language Guided Iterative Motion Refinement
K. Niranjan Kumar, Irfan Essa, Sehoon Ha
LangRob workshop CoRL, 2023
project page / arXiv

Learning rich humanoid behaviors from human language instructions.

BayRnTune: Adaptive Bayesian Domain Randomization via Strategic Fine-tuning
Tianle Huang Nitish Sontakke K. Niranjan Kumar, Irfan Essa, Sehoon Ha Stefanos Nikolaidis Dennis W Hong
Under Review, 2023

Tune simulation parameters to efficiently transfer to target environment.

Cascaded Compositional Residual Learning for Complex Interactive Behaviors
K. Niranjan Kumar, Irfan Essa, Sehoon Ha
Robotics and Automation Letters, 2023
project page / arXiv / video

Cascaded Compositional Residual Learning (CCRL), which learns composite skills by recursively leveraging a library of previously learned control policies.

Graph-based Cluttered Scene Generation and Interactive Exploration using Deep Reinforcement Learning
K. Niranjan Kumar, Irfan Essa, Sehoon Ha
ICRA, 2022
arXiv / video

Finding hidden objects in structured cluttered environments using deep reinforcement learning.

Estimating Mass Distribution of Articulated Objects using Non-prehensile Manipulation
K. Niranjan Kumar, Irfan Essa, Sehoon Ha, Karen Liu
Object Representations for Learning and Reasoning Workshop NeurIPS, 2020
arXiv / video

Learning interaction policies to estimate mass distribution of articulated objects in minimal steps.


Motion textures from static images

Built an algorithm that converts an image into an infinitely looping video using a neural network and user input.

Realistic video generation of hand-object interactions

We generate realistic hand manipulations of object by using a Generative Adversarial Network by conditioning on the hand and finger pose. We adopt an encoder-decoder architecture where we input a rough rendering of the skeletal pose and get a realistic rendering of a hand as the output. We generate datasets with simulated and real human arms at different poses and use it to train our model. Given an hand pose and object pose we showcase a pipeline to generate an image conditioned on the poses.

Automatic eye-dropper

Design an automated eye-dropper that detects the patients eye, adminsters the eye drop and registers if the adminstration was successful using CV (eye-tracking, blink detection, drop tracking).

Tele-operated mobile robot

Built a controller for tele-operated mobile robot that can be controlled using a computer keyboard.


Web Chair, CoRL 2023
Reviewer for TRO, THRI, IROS, RAL


GTA for CS6475 Computational Photography

Mentoring Experience

I have had the pleasure of working with the following students over the years:

Alex Kim
Hrishikesh Kale
Xingfang Yuan
Mili Das

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