activity
20172022
most citedLegged Locomotion in Challenging Terrains using Egocentric Vision

31 citations · 72 across the 13 of their papers we have counts for

collaborators
Showing cs.ROShow all

7 papers · 1 filter

cs.RO202231 cited

Legged Locomotion in Challenging Terrains using Egocentric Vision

Ananye Agarwal, Ashish Kumar, Jitendra Malik +1

Animals are capable of precise and agile locomotion using vision. Replicating this ability has been a long-standing goal in robotics. The traditional approach has been to decompose…

cs.RO202213 cited

In-Hand Object Rotation via Rapid Motor Adaptation

Haozhi Qi, Ashish Kumar, Roberto Calandra +2

Generalized in-hand manipulation has long been an unsolved challenge of robotics. As a small step towards this grand goal, we demonstrate how to design and learn a simple adaptive…

cs.RO20216 cited

Minimizing Energy Consumption Leads to the Emergence of Gaits in Legged Robots

Zipeng Fu, Ashish Kumar, Jitendra Malik +1

Legged locomotion is commonly studied and expressed as a discrete set of gait patterns, like walk, trot, gallop, which are usually treated as given and pre-programmed in legged rob…

cs.RO2021

End-To-End Real-Time Visual Perception Framework for Construction Automation

Mohit Vohra, Ashish Kumar, Ravi Prakash +1

In this work, we present a robotic solution to automate the task of wall construction. To that end, we present an end-to-end visual perception framework that can quickly detect and…

cs.RO20211 cited

Towards Deep Learning Assisted Autonomous UAVs for Manipulation Tasks in GPS-Denied Environments

Ashish Kumar, Mohit Vohra, Ravi Prakash +1

In this work, we present a pragmatic approach to enable unmanned aerial vehicle (UAVs) to autonomously perform highly complicated tasks of object pick and place. This paper is larg…

cs.RO2019

Learning Navigation Subroutines from Egocentric Videos

Ashish Kumar, Saurabh Gupta, Jitendra Malik

Planning at a higher level of abstraction instead of low level torques improves the sample efficiency in reinforcement learning, and computational efficiency in classical planning.…