Search Results for author: Vikash Kumar

Found 28 papers, 10 papers with code

A Game Theoretic Perspective on Model-Based Reinforcement Learning

no code implementations ICML 2020 Aravind Rajeswaran, Igor Mordatch, Vikash Kumar

We point out that a large class of MBRL algorithms can be viewed as a game between two players: (1) a policy player, which attempts to maximize rewards under the learned model; (2) a model player, which attempts to fit the real-world data collected by the policy player.

Continuous Control Model-based Reinforcement Learning +1

R3M: A Universal Visual Representation for Robot Manipulation

1 code implementation23 Mar 2022 Suraj Nair, Aravind Rajeswaran, Vikash Kumar, Chelsea Finn, Abhinav Gupta

We study how visual representations pre-trained on diverse human video data can enable data-efficient learning of downstream robotic manipulation tasks.

Contrastive Learning

Policy Architectures for Compositional Generalization in Control

no code implementations10 Mar 2022 Allan Zhou, Vikash Kumar, Chelsea Finn, Aravind Rajeswaran

Many tasks in control, robotics, and planning can be specified using desired goal configurations for various entities in the environment.

Imitation Learning

Translating Robot Skills: Learning Unsupervised Skill Correspondences Across Robots

no code implementations29 Sep 2021 Tanmay Shankar, Yixin Lin, Aravind Rajeswaran, Vikash Kumar, Stuart Anderson, Jean Oh

In this paper, we explore how we can endow robots with the ability to learn correspondences between their own skills, and those of morphologically different robots in different domains, in an entirely unsupervised manner.

Frame Translation +1

Deep Neural Network Approach to Estimate Early Worst-Case Execution Time

no code implementations28 Jul 2021 Vikash Kumar

However, getting these results in the early stages of system development is an essential prerequisite for the system's dimensioning and configuration of the hardware setup.

Dynamics-Aware Unsupervised Skill Discovery

1 code implementation ICLR 2020 Archit Sharma, Shixiang Gu, Sergey Levine, Vikash Kumar, Karol Hausman

Conventionally, model-based reinforcement learning (MBRL) aims to learn a global model for the dynamics of the environment.

Model-based Reinforcement Learning

The Ingredients of Real World Robotic Reinforcement Learning

no code implementations ICLR 2020 Henry Zhu, Justin Yu, Abhishek Gupta, Dhruv Shah, Kristian Hartikainen, Avi Singh, Vikash Kumar, Sergey Levine

The success of reinforcement learning in the real world has been limited to instrumented laboratory scenarios, often requiring arduous human supervision to enable continuous learning.


The Ingredients of Real-World Robotic Reinforcement Learning

no code implementations27 Apr 2020 Henry Zhu, Justin Yu, Abhishek Gupta, Dhruv Shah, Kristian Hartikainen, Avi Singh, Vikash Kumar, Sergey Levine

In this work, we discuss the elements that are needed for a robotic learning system that can continually and autonomously improve with data collected in the real world.


Emergent Real-World Robotic Skills via Unsupervised Off-Policy Reinforcement Learning

2 code implementations27 Apr 2020 Archit Sharma, Michael Ahn, Sergey Levine, Vikash Kumar, Karol Hausman, Shixiang Gu

Can we instead develop efficient reinforcement learning methods that acquire diverse skills without any reward function, and then repurpose these skills for downstream tasks?

reinforcement-learning Unsupervised Reinforcement Learning

A Game Theoretic Framework for Model Based Reinforcement Learning

no code implementations16 Apr 2020 Aravind Rajeswaran, Igor Mordatch, Vikash Kumar

Model-based reinforcement learning (MBRL) has recently gained immense interest due to its potential for sample efficiency and ability to incorporate off-policy data.

Model-based Reinforcement Learning reinforcement-learning

Benchmarking In-Hand Manipulation

no code implementations9 Jan 2020 Silvia Cruciani, Balakumar Sundaralingam, Kaiyu Hang, Vikash Kumar, Tucker Hermans, Danica Kragic

The purpose of this benchmark is to evaluate the planning and control aspects of robotic in-hand manipulation systems.


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

1 code implementation25 Oct 2019 Abhishek Gupta, Vikash Kumar, Corey Lynch, Sergey Levine, Karol Hausman

We present relay policy learning, a method for imitation and reinforcement learning that can solve multi-stage, long-horizon robotic tasks.

Imitation Learning reinforcement-learning

Deep Dynamics Models for Learning Dexterous Manipulation

2 code implementations25 Sep 2019 Anusha Nagabandi, Kurt Konoglie, Sergey Levine, Vikash Kumar

Dexterous multi-fingered hands can provide robots with the ability to flexibly perform a wide range of manipulation skills.

ROBEL: Robotics Benchmarks for Learning with Low-Cost Robots

1 code implementation25 Sep 2019 Michael Ahn, Henry Zhu, Kristian Hartikainen, Hugo Ponte, Abhishek Gupta, Sergey Levine, Vikash Kumar

ROBEL introduces two robots, each aimed to accelerate reinforcement learning research in different task domains: D'Claw is a three-fingered hand robot that facilitates learning dexterous manipulation tasks, and D'Kitty is a four-legged robot that facilitates learning agile legged locomotion tasks.

Continuous Control reinforcement-learning

Multi-Agent Manipulation via Locomotion using Hierarchical Sim2Real

no code implementations13 Aug 2019 Ofir Nachum, Michael Ahn, Hugo Ponte, Shixiang Gu, Vikash Kumar

Our method hinges on the use of hierarchical sim2real -- a simulated environment is used to learn low-level goal-reaching skills, which are then used as the action space for a high-level RL controller, also trained in simulation.

Dynamics-Aware Unsupervised Discovery of Skills

3 code implementations2 Jul 2019 Archit Sharma, Shixiang Gu, Sergey Levine, Vikash Kumar, Karol Hausman

Conventionally, model-based reinforcement learning (MBRL) aims to learn a global model for the dynamics of the environment.

Model-based Reinforcement Learning

Learning Latent Plans from Play

no code implementations5 Mar 2019 Corey Lynch, Mohi Khansari, Ted Xiao, Vikash Kumar, Jonathan Tompson, Sergey Levine, Pierre Sermanet

Learning from play (LfP) offers three main advantages: 1) It is cheap.


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

no code implementations14 Oct 2018 Henry Zhu, Abhishek Gupta, Aravind Rajeswaran, Sergey Levine, Vikash Kumar

Dexterous multi-fingered robotic hands can perform a wide range of manipulation skills, making them an appealing component for general-purpose robotic manipulators.


Time Reversal as Self-Supervision

no code implementations2 Oct 2018 Suraj Nair, Mohammad Babaeizadeh, Chelsea Finn, Sergey Levine, Vikash Kumar

We test our method on the domain of assembly, specifically the mating of tetris-style block pairs.

Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines

no code implementations ICLR 2018 Cathy Wu, Aravind Rajeswaran, Yan Duan, Vikash Kumar, Alexandre M. Bayen, Sham Kakade, Igor Mordatch, Pieter Abbeel

To mitigate this issue, we derive a bias-free action-dependent baseline for variance reduction which fully exploits the structural form of the stochastic policy itself and does not make any additional assumptions about the MDP.

Policy Gradient Methods reinforcement-learning

Divide-and-Conquer Reinforcement Learning

1 code implementation ICLR 2018 Dibya Ghosh, Avi Singh, Aravind Rajeswaran, Vikash Kumar, Sergey Levine

In this paper, we develop a novel algorithm that instead partitions the initial state space into "slices", and optimizes an ensemble of policies, each on a different slice.

Policy Gradient Methods reinforcement-learning

Domain Randomization and Generative Models for Robotic Grasping

no code implementations17 Oct 2017 Joshua Tobin, Lukas Biewald, Rocky Duan, Marcin Andrychowicz, Ankur Handa, Vikash Kumar, Bob McGrew, Jonas Schneider, Peter Welinder, Wojciech Zaremba, Pieter Abbeel

In this work, we explore a novel data generation pipeline for training a deep neural network to perform grasp planning that applies the idea of domain randomization to object synthesis.

Robotic Grasping

Learning Dexterous Manipulation Policies from Experience and Imitation

no code implementations15 Nov 2016 Vikash Kumar, Abhishek Gupta, Emanuel Todorov, Sergey Levine

We demonstrate that such controllers can perform the task robustly, both in simulation and on the physical platform, for a limited range of initial conditions around the trained starting state.

Cannot find the paper you are looking for? You can Submit a new open access paper.