Search Results for author: Ajay Mandlekar

Found 13 papers, 3 papers with code

Error-Aware Imitation Learning from Teleoperation Data for Mobile Manipulation

no code implementations9 Dec 2021 Josiah Wong, Albert Tung, Andrey Kurenkov, Ajay Mandlekar, Li Fei-Fei, Silvio Savarese, Roberto Martín-Martín

Doing this is challenging for two reasons: on the data side, current interfaces make collecting high-quality human demonstrations difficult, and on the learning side, policies trained on limited data can suffer from covariate shift when deployed.

Imitation Learning

What Matters in Learning from Offline Human Demonstrations for Robot Manipulation

1 code implementation6 Aug 2021 Ajay Mandlekar, Danfei Xu, Josiah Wong, Soroush Nasiriany, Chen Wang, Rohun Kulkarni, Li Fei-Fei, Silvio Savarese, Yuke Zhu, Roberto Martín-Martín

Based on the study, we derive a series of lessons including the sensitivity to different algorithmic design choices, the dependence on the quality of the demonstrations, and the variability based on the stopping criteria due to the different objectives in training and evaluation.

Imitation Learning reinforcement-learning

S4RL: Surprisingly Simple Self-Supervision for Offline Reinforcement Learning

no code implementations10 Mar 2021 Samarth Sinha, Ajay Mandlekar, Animesh Garg

Offline reinforcement learning proposes to learn policies from large collected datasets without interacting with the physical environment.

Autonomous Driving Data Augmentation +4

Generalization Through Hand-Eye Coordination: An Action Space for Learning Spatially-Invariant Visuomotor Control

no code implementations28 Feb 2021 Chen Wang, Rui Wang, Ajay Mandlekar, Li Fei-Fei, Silvio Savarese, Danfei Xu

Key to such capability is hand-eye coordination, a cognitive ability that enables humans to adaptively direct their movements at task-relevant objects and be invariant to the objects' absolute spatial location.

Imitation Learning

Human-in-the-Loop Imitation Learning using Remote Teleoperation

no code implementations12 Dec 2020 Ajay Mandlekar, Danfei Xu, Roberto Martín-Martín, Yuke Zhu, Li Fei-Fei, Silvio Savarese

We develop a simple and effective algorithm to train the policy iteratively on new data collected by the system that encourages the policy to learn how to traverse bottlenecks through the interventions.

Imitation Learning

Learning Multi-Arm Manipulation Through Collaborative Teleoperation

no code implementations12 Dec 2020 Albert Tung, Josiah Wong, Ajay Mandlekar, Roberto Martín-Martín, Yuke Zhu, Li Fei-Fei, Silvio Savarese

To address these challenges, we present Multi-Arm RoboTurk (MART), a multi-user data collection platform that allows multiple remote users to simultaneously teleoperate a set of robotic arms and collect demonstrations for multi-arm tasks.

Imitation Learning

robosuite: A Modular Simulation Framework and Benchmark for Robot Learning

5 code implementations25 Sep 2020 Yuke Zhu, Josiah Wong, Ajay Mandlekar, Roberto Martín-Martín

robosuite is a simulation framework for robot learning powered by the MuJoCo physics engine.

Learning to Generalize Across Long-Horizon Tasks from Human Demonstrations

no code implementations13 Mar 2020 Ajay Mandlekar, Danfei Xu, Roberto Martín-Martín, Silvio Savarese, Li Fei-Fei

In the second stage of GTI, we collect a small set of rollouts from the unconditioned stochastic policy of the first stage, and train a goal-directed agent to generalize to novel start and goal configurations.

Imitation Learning

IRIS: Implicit Reinforcement without Interaction at Scale for Learning Control from Offline Robot Manipulation Data

no code implementations13 Nov 2019 Ajay Mandlekar, Fabio Ramos, Byron Boots, Silvio Savarese, Li Fei-Fei, Animesh Garg, Dieter Fox

For simple short-horizon manipulation tasks with modest variation in task instances, offline learning from a small set of demonstrations can produce controllers that successfully solve the task.

Scaling Robot Supervision to Hundreds of Hours with RoboTurk: Robotic Manipulation Dataset through Human Reasoning and Dexterity

no code implementations11 Nov 2019 Ajay Mandlekar, Jonathan Booher, Max Spero, Albert Tung, Anchit Gupta, Yuke Zhu, Animesh Garg, Silvio Savarese, Li Fei-Fei

We evaluate the quality of our platform, the diversity of demonstrations in our dataset, and the utility of our dataset via quantitative and qualitative analysis.

Controlling Assistive Robots with Learned Latent Actions

no code implementations20 Sep 2019 Dylan P. Losey, Krishnan Srinivasan, Ajay Mandlekar, Animesh Garg, Dorsa Sadigh

Our insight is that we can make assistive robots easier for humans to control by leveraging latent actions.

Robotics

AC-Teach: A Bayesian Actor-Critic Method for Policy Learning with an Ensemble of Suboptimal Teachers

1 code implementation9 Sep 2019 Andrey Kurenkov, Ajay Mandlekar, Roberto Martin-Martin, Silvio Savarese, Animesh Garg

The exploration mechanism used by a Deep Reinforcement Learning (RL) agent plays a key role in determining its sample efficiency.

RoboTurk: A Crowdsourcing Platform for Robotic Skill Learning through Imitation

no code implementations7 Nov 2018 Ajay Mandlekar, Yuke Zhu, Animesh Garg, Jonathan Booher, Max Spero, Albert Tung, Julian Gao, John Emmons, Anchit Gupta, Emre Orbay, Silvio Savarese, Li Fei-Fei

Imitation Learning has empowered recent advances in learning robotic manipulation tasks by addressing shortcomings of Reinforcement Learning such as exploration and reward specification.

Imitation Learning

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