I am broadly interested in the areas of Embodied Artificial Intelligence. My general interest lies in developing algorithmic formulations under real-world constraints, that are capable of synthesizing complex behaviors on physical hardware. More specifically, my current attention is focused on solving contact rich dexterous manipulation with free objects.

Biological designs are a result of years of evolution. They are effective, efficient and conceal within themselves enormous wisdom -- mechanical as well as intellectual. Complex movements exhibited by the animal kingdom is a result of a tightly integrated system of bio-muscular structures and fast reactive motor control. If artificial counterparts are to exhibit comparable behaviors, the solution is likely to be situated at the intersection of mechanism and computation. I use tools from reinforcement learning, robotics, optimal control, and optimization to reason at the intersection of mechanisms and algorithms to design robust sample-efficient techniques that scale to and succeed on the physical hardware.

Highlights

A Game Theoretic Framework for Model-Based Reinforcement Learning

Aravind Rajeswaran, Igor Mordatch, Vikash Kumar
International Conference on Machine Learning (ICML) 2020
webpage

Dynamics-Aware Unsupervised Discovery of Skills

Archit Sharma, Shixiang Gu, Sergey Levine, Vikash Kumar, Karol Hausman
International Conference on Learning Representations (ICLR) 2020
webpage

ROBEL: RObotics BEnchmarks for Learning

Michael Ahn, Henry Zhu, Kristian Hartikainen, Hugo Ponte, Abhishek Gupta, Sergey Levine, Vikash Kumar
Conference on Robot Learning (CoRL) 2019
webpage

Deep Dynamics Models for Learning Dexterous Manipulation

Anusha Nagabandi, Kurt Konolige, Sergey Levine, Vikash Kumar
Conference on Robot Learning (CoRL) 2019
webpage

Multi-Agent Manipulation via Locomotion using Hierarchical Sim2Real

Ofir Nachum, Michael Ahn, Hugo Ponte, Shane Gu, Vikash Kumar
Conference on Robot Learning (CoRL) 2019
webpage

Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations

DAPG: Demo Augmented Policy Gradient

Vikash Kumar*, Aravind Rajeshwaran*, Abhiskek Gupta, John Schulman, Emanuel Todorov, and Sergey Levine
Proceedings of Robotics: Science and Systems (RSS) 2018
Paper, suppliment, Movie, webpage, arxiv

BEST MANIPULATION PAPER AWARD

Optimal Control with Learned Local Models: Application to Dexterous Manipulation

Vikash Kumar, Emanuel Todorov, and Sergey Levine
IEEE International Conference on Robotics and Automation (ICRA) 2016
Paper, Movie

Real-time behaviour synthesis for dynamic Hand-Manipulation

Vikash Kumar, Yuval Tassa, Tom Erez and Emanuel Todorov
IEEE International Conference on Robotics and Automation (ICRA) 2014
Paper, Movie
webpage



Full list organized by area

Benchmarks

Benchmarking In-Hand Manipulation

Silvia Cruciani, Balakumar Sundaralingam, Kaiyu Hang, Vikash Kumar, Tucker Hermans, Danica Kragic
IEEE Robotics and Automation Letters (RAL) 2020
Special Issue: Benchmarking Protocols for Robotic Manipulation
webpage

ROBEL: RObotics BEnchmarks for Learning

Michael Ahn, Henry Zhu, Kristian Hartikainen, Hugo Ponte, Abhishek Gupta, Sergey Levine, Vikash Kumar
Conference on Robot Learning (CoRL) 2019
webpage

Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research

Matthias Plappert, Marcin Andrychowicz, Alex Ray, Bob McGrew, Bowen Baker, Glenn Powell, Jonas Schneider, Josh Tobin, Maciek Chociej, Peter Welinder, Vikash Kumar, Wojciech Zaremba
Teach Report 2018
Paper, Movie, webpage, arxiv

Behavior Synthesis

Reset-Free Reinforcement Learning via Multi-Task Learning:
Learning Dexterous Manipulation Behaviors without Human Intervention

Abhishek Gupta*, Justin Yu*, Tony Z. Zhao*, Vikash Kumar*, Aaron Rovinsky, Kelvin Xu, Thomas Devlin, Sergey Levine
International Conference on Robotics and Automation (ICRA) 2021
webpage

Emergent Real-World Robotic Skills via Unsupervised Reinforcement Learning

Archit Sharma, Michael Ahn, Sergey Levine, Vikash Kumar, Karol Hausman, Shixiang Gu
Robotics: Science and Systems (RSS), 2020
webpage

Time Reversal as Self-Supervision

Suraj Nair, Mohammad Babaeizadeh, Chelsea Finn, Sergey Levine, Vikash Kumar
International Conference on Robotics and Automation (ICRA) 2020
webpage

The Ingredients of Real World Robotic Reinforcement Learning

Henry Zhu, Justin Yu, Abhishek Gupta, Dhruv Shah, Kristian Hartikainen, Avi Singh, Vikash Kumar, Sergey Levine
International Conference on Learning Representations (ICLR) 2020
webpage

Dynamics-Aware Unsupervised Discovery of Skills

Archit Sharma, Shixiang Gu, Sergey Levine, Vikash Kumar, Karol Hausman
International Conference on Learning Representations (ICLR) 2020
webpage

Deep Dynamics Models for Learning Dexterous Manipulation

Anusha Nagabandi, Kurt Konolige, Sergey Levine, Vikash Kumar
Conference on Robot Learning (CoRL) 2019
webpage

Multi-Agent Manipulation via Locomotion using Hierarchical Sim2Real

Ofir Nachum, Michael Ahn, Hugo Ponte, Shane Gu, Vikash Kumar
Conference on Robot Learning (CoRL) 2019
webpage

Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning

Abhishek Gupta, Vikash Kumar, Corey Lynch, Sergey Levine, Karol Hausman
Conference on Robot Learning (CoRL) 2019
webpage,

Learning Latent Plans from Play

Corey Lynch, Mohi Khansari, Ted Xiao, Vikash Kumar, Jonathan Tompson, Sergey Levine, Pierre Sermanet
Conference on Robot Learning (CoRL) 2019
webpage

Dexterous Manipulation with Deep Reinforcement Learning:Efficient, General, and Low-Cost

Henry Zhu*, Abhishek Gupta*, Aravind Rajeswaran, Sergey Levine, Vikash Kumar
International Conference on Robotics and Automation (ICRA) 2019
webpage

Learning Deep Visuomotor Policies for Dexterous Hand Manipulation

Divye Jain, Andrew Li, Shivam Singhal, Aravind Rajeswaran, Vikash Kumar, Emanuel Todorov
International Conference on Robotics and Automation (ICRA) 2019
webpage

Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations

DAPG: Demo Augmented Policy Gradient

Vikash Kumar*, Aravind Rajeshwaran*, Abhiskek Gupta, John Schulman, Emanuel Todorov, and Sergey Levine
Proceedings of Robotics: Science and Systems (RSS) 2018
Paper, suppliment, Movie, webpage, arxiv

Divide-and-Conquer Reinforcement Learning

Dibya Ghosh, Avi Singh, Aravind Rajeswaran, Vikash Kumar, Sergey Levine
International Conference on Learning Representations (ICLR) 2018
Paper, Movie,
webpage, arxiv, OpenReview

Variance Reduction for Policy Gradient with Action-dependent Baseline

Cathy Wu, Aravind Rajeswaran, Yan Duan, Vikash Kumar, Alexandre M Bayen, Sham Kakade, Igor Mordatch, Pieter Abbeel
International Conference on Learning Representations (ICLR) 2018
webpage

Learning Dexterous Manipulation Policies from Experience and Imitation

Vikash Kumar, Abhiskek Gupta, Emanuel Todorov, and Sergey Levine
Under Review
Paper, Movie

BEST MANIPULATION PAPER AWARD

Optimal Control with Learned Local Models: Application to Dexterous Manipulation

Vikash Kumar, Emanuel Todorov, and Sergey Levine
IEEE International Conference on Robotics and Automation (ICRA) 2016
Paper, Movie

Real-time behaviour synthesis for dynamic Hand-Manipulation

Vikash Kumar, Yuval Tassa, Tom Erez and Emanuel Todorov
IEEE International Conference on Robotics and Automation (ICRA) 2014
Paper, Movie
webpage

An integrated system for real-time Model Predictive Control of humanoid robots

Tom Erez, Kendall Lowrey, Yuval Tassa, Vikash Kumar, Svetoslav Kolev, Emanuel Todorov
Humanoids 2013
Paper, Overview, Dynamics look at Dynamic walking Dynamic tracking

Synthesis of Complex Behaviors with Optimal Control

Todorov Emanuel, Tassa Yuval, Erez Tom, Mordatch Igor, Kulchenko Paul, Kumar Vikash
Computational and Systems Neuroscience (COSYNE) 2013
Abstract, Movie

Virtual reality

MuJoCo HAPTIX: A Virtual Reality System for Hand Manipulation

Vikash Kumar and Emanuel Todorov
IEEE-RAS International Conference on Humanoid Robots 2015
Paper, Movie

Tracking & calibration

Precise Calibration of Robots with small link lengths using Kinematic Extensions

Visak Chadalavada, Vikash Kumar
(Under progress)
Paper, Movie

STAC: Simultaneous Tracking and Calibration

Tingfan Wu, Yuval Tassa, Vikash Kumar, Javier Movellan, Emanuel Todorov
Humanoids 2013
Paper, Movie

Robot design

A Low-cost and Modular, 20-DOF Anthropomorphic Robotic Hand: Design, Actuation and Modeling

Zhe Xu, Vikash Kumar, Emanuel Todorov
Humanoids 2013
Paper, Movie
webpage

Fast, strong and compliant pneumatic actuation for dexterous tendon-driven hands

Kumar Vikash, Todorov Emanuel
IEEE International Conference on Robotics and Automation (ICRA) 2013
Paper, Movie
webpage

Design of an anthropomorphic robotic finger system with biomimetic artificial joints

Zhe Xhe, Kumar Vikash, Matsuoka Yoki, Todorov Emanuel
IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob) 2012
Paper, Movie
webpage

Self and Mutual learning in Robotic Arm, based on Cognitive systems

Kumar Vikash, Patil Chetan, Sachan Sachan
International MultiConference of Engineers and Computer Scientists 2010 (Shortlisted for Best paper Award)
Paper, Movie

Manuscripts

High performance pneumatics using Model Predictive Control

Vikash Kumar, Visak CV, Emanuel Todorov
Paper

Optimizing fuzzy multi-objective problems using fuzzy genetic algorithms and FZDT test functions

Kumar Vikash, Chakroborty Debjani
Paper

The UW Hand: A Low-cost, 20-DOF Tendon-driven Hand with Fast and Compliant Actuation

Zhe X, Kumar V, Todorov E.
Paper, Movie,