profile photo

Email  |  Twitter  |  Github

Ananye Agarwal

Email: ananyea [at] andrew [dot] cmu [dot] edu

I am a PhD student at Carnegie Mellon University working with Deepak Pathak. I am interested in understanding how humans and animals carry out diverse tasks in many environments and how we can teach robots to do the same.

I graduated from IIT Delhi with a B.Tech. in Computer Science and a President's Gold Medal. In the past, I have been lucky to work with Prof. Mausam on neuro-symbolic AI and with Prof. Manik Varma on Extreme Classification. I have also had the good fortune of interning at Microsoft Research India where I worked with Dr. Ankit Garg on algebraic complexity.

Publications


SPIN: Simultaneous Perception, Interaction and Navigation

Shagun Uppal, Ananye Agarwal, Haoyu Xiong, Kenneth Shaw, Deepak Pathak

CVPR 2024

webpage | arXiv
sym
SAPG: Split and Aggregate Policy Gradients

Jayesh Singla*, Ananye Agarwal*, Deepak Pathak

ICML 2024 (Oral)

webpage
Dexterous Functional Grasping

Ananye Agarwal, Shagun Uppal, Kenneth Shaw, Deepak Pathak

CoRL 2023

webpage | arXiv
Extreme Parkour with Legged Robots

Xuxin Cheng*, Kexin Shi*, Ananye Agarwal, Deepak Pathak

ICRA 2024

webpage | arXiv | code
LEAP Hand: Low-Cost, Efficient, and Anthropomorphic Hand for Robot Learning

Kenneth Shaw, Ananye Agarwal, Deepak Pathak

RSS 2023

webpage | paper
Legged Locomotion in Challenging Terrains using Egocentric Vision

Ananye Agarwal*, Ashish Kumar*, Jitendra Malik, Deepak Pathak

CoRL 2022 (Best Systems Paper Award)

webpage | arXiv | demo | in the media
Coupling Vision and Proprioception for Navigation of Legged Robots

Zipeng Fu*, Ashish Kumar*, Ananye Agarwal, Haozhi Qi, Jitendra Malik, Deepak Pathak

CVPR 2022 (Best Paper at Multimodal Learning Workshop)

webpage | pdf | arXiv | code | video
sym
SiameseXML: Siamese networks meet extreme classifiers with 100M labels

K. Dahiya, A. Agarwal, D. Saini, K. Gururaj, J. Jiao, A. Singh, S. Agarwal, P. Kar and M. Varma

ICML 2021

PDF | code

Live Demos

Our small low-cost robot can perceive and traverse challenging terrain. This uses a single neural network running onboard that directly maps pixels to joint torques.

CoRL 2021, Auckland
CVPR 2021, New Orleans

Press Coverage