Vikash Kumar  

MyoDex: Generalizable Representations for Dexterous Physiological Manipulation

LIV: Language-Image Representations and Rewards for Robotic Control

SAR:Generalization of Dexterity via Synergistic Action Representation

ACT: Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware

Visual Dexterity: In-hand Dexterous Manipulation from Depth

GenAug: Retargeting behaviors to unseen situations via Generative Augmentation

H2R: Zero-Shot Robot Manipulation from Passive Human Videos

Dexterous Manipulation from Images: Autonomous Real-World RL via Substep Guidance

MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations

CACTI: A Framework for Scalable Multi-Task Multi-Scene Visual Imitation Learning

All the Feels: A dexterous hand with large area sensing

Real World Offline Reinforcement Learning with Realistic Data Source

VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training

Learning Dexterous Manipulation from Exemplar Object Trajectories and Pre-Grasps

Translating Robot Skills: Learning Unsupervised Skill Correspondences Across Robots

Cross-Domain Transfer via Semantic Skill Imitation

Curiosity Driven Self-supervised Tactile Exploration of Unknown Objects

R3M: A Universal Visual Representation for Robot Manipulation

Can Foundation Models Perform Zero-Shot Task Specification For Robot Manipulation?

RRL: Resnet as representation for Reinforcement Learning

MyoSuite: A contact-rich simulation suite for musculoskeletal motor control

MyoSim: A contact-rich simulation suite for musculoskeletal motor control

RB2: Robotic Manipulation Benchmarking with a Twist

Benchmarking In-Hand Manipulation

ROBEL: RObotics BEnchmarks for Learning

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

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

A Game Theoretic Framework for Model-Based Reinforcement Learning

Emergent Real-World Robotic Skills via Unsupervised Reinforcement Learning

Time Reversal as Self-Supervision

The Ingredients of Real World Robotic Reinforcement Learning

Dynamics-Aware Unsupervised Discovery of Skills

Deep Dynamics Models for Learning Dexterous Manipulation

Multi-Agent Manipulation via Locomotion using Hierarchical Sim2Real

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

Learning Latent Plans from Play

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

Learning Deep Visuomotor Policies for Dexterous Hand Manipulation

Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations

DAPG: Demo Augmented Policy Gradient

Divide-and-Conquer Reinforcement Learning

Variance Reduction for Policy Gradient with Action-dependent Baseline

Learning Dexterous Manipulation Policies from Experience and Imitation

Optimal Control with Learned Local Models: Application to Dexterous Manipulation

Real-time behaviour synthesis for dynamic Hand-Manipulation

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

Synthesis of Complex Behaviors with Optimal Control

MuJoCo HAPTIX: A Virtual Reality System for Hand Manipulation

Precise Calibration of Robots with small link lengths using Kinematic Extensions

STAC: Simultaneous Tracking and Calibration

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

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

Design of an anthropomorphic robotic finger system with biomimetic artificial joints

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

High performance pneumatics using Model Predictive Control

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

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