Search Results for author: Lerrel Pinto

Found 38 papers, 17 papers with code

Hierarchically Decoupled Morphological Transfer

no code implementations ICML 2020 Donald Hejna, Lerrel Pinto, Pieter Abbeel

Learning long-range behaviors on complex high-dimensional agents is a fundamental problem in robot learning.

URLB: Unsupervised Reinforcement Learning Benchmark

1 code implementation28 Oct 2021 Michael Laskin, Denis Yarats, Hao liu, Kimin Lee, Albert Zhan, Kevin Lu, Catherine Cang, Lerrel Pinto, Pieter Abbeel

Deep Reinforcement Learning (RL) has emerged as a powerful paradigm to solve a range of complex yet specific control tasks.

Continuous Control Unsupervised Reinforcement Learning

Playful Interactions for Representation Learning

no code implementations19 Jul 2021 Sarah Young, Jyothish Pari, Pieter Abbeel, Lerrel Pinto

In this work, we propose to use playful interactions in a self-supervised manner to learn visual representations for downstream tasks.

Imitation Learning Representation Learning

GEM: Group Enhanced Model for Learning Dynamical Control Systems

no code implementations7 Apr 2021 Philippe Hansen-Estruch, Wenling Shang, Lerrel Pinto, Pieter Abbeel, Stas Tiomkin

In this work, we take advantage of these structures to build effective dynamical models that are amenable to sample-based learning.

Continuous Control Model-based Reinforcement Learning

Simultaneous Navigation and Construction Benchmarking Environments

1 code implementation31 Mar 2021 Wenyu Han, Chen Feng, Haoran Wu, Alexander Gao, Armand Jordana, Dong Liu, Lerrel Pinto, Ludovic Righetti

We need intelligent robots for mobile construction, the process of navigating in an environment and modifying its structure according to a geometric design.

Visual Navigation

Task-Agnostic Morphology Evolution

1 code implementation ICLR 2021 Donald J. Hejna III, Pieter Abbeel, Lerrel Pinto

Deep reinforcement learning primarily focuses on learning behavior, usually overlooking the fact that an agent's function is largely determined by form.

Reinforcement Learning with Prototypical Representations

1 code implementation22 Feb 2021 Denis Yarats, Rob Fergus, Alessandro Lazaric, Lerrel Pinto

Unfortunately, in RL, representation learning is confounded with the exploratory experience of the agent -- learning a useful representation requires diverse data, while effective exploration is only possible with coherent representations.

Continuous Control Representation Learning +1

Mobile Construction Benchmark

no code implementations1 Jan 2021 Wenyu Han, Chen Feng, Haoran Wu, Alexander Gao, Armand Jordana, Dongdong Liu, Lerrel Pinto, Ludovic Righetti

We need intelligent robots to perform mobile construction, the process of moving in an environment and modifying its geometry according to a design plan.

A Framework for Efficient Robotic Manipulation

no code implementations14 Dec 2020 Albert Zhan, Philip Zhao, Lerrel Pinto, Pieter Abbeel, Michael Laskin

Building on these advances, we present a Framework for Efficient Robotic Manipulation (FERM) that utilizes data augmentation and unsupervised learning to achieve extremely sample-efficient training of robotic manipulation policies with sparse rewards.

Data Augmentation Unsupervised Representation Learning

Visual Imitation Made Easy

no code implementations11 Aug 2020 Sarah Young, Dhiraj Gandhi, Shubham Tulsiani, Abhinav Gupta, Pieter Abbeel, Lerrel Pinto

We use commercially available reacher-grabber assistive tools both as a data collection device and as the robot's end-effector.

Imitation Learning Structure from Motion

Self-Supervised Policy Adaptation during Deployment

4 code implementations ICLR 2021 Nicklas Hansen, Rishabh Jangir, Yu Sun, Guillem Alenyà, Pieter Abbeel, Alexei A. Efros, Lerrel Pinto, Xiaolong Wang

A natural solution would be to keep training after deployment in the new environment, but this cannot be done if the new environment offers no reward signal.

Swoosh! Rattle! Thump! -- Actions that Sound

no code implementations3 Jul 2020 Dhiraj Gandhi, Abhinav Gupta, Lerrel Pinto

In this work, we perform the first large-scale study of the interactions between sound and robotic action.

Automatic Curriculum Learning through Value Disagreement

1 code implementation NeurIPS 2020 Yunzhi Zhang, Pieter Abbeel, Lerrel Pinto

Our key insight is that if we can sample goals at the frontier of the set of goals that an agent is able to reach, it will provide a significantly stronger learning signal compared to randomly sampled goals.

Curriculum Learning

Reinforcement Learning with Augmented Data

1 code implementation NeurIPS 2020 Michael Laskin, Kimin Lee, Adam Stooke, Lerrel Pinto, Pieter Abbeel, Aravind Srinivas

To this end, we present Reinforcement Learning with Augmented Data (RAD), a simple plug-and-play module that can enhance most RL algorithms.

Data Augmentation OpenAI Gym

State-Only Imitation Learning for Dexterous Manipulation

no code implementations7 Apr 2020 Ilija Radosavovic, Xiaolong Wang, Lerrel Pinto, Jitendra Malik

To tackle this setting, we train an inverse dynamics model and use it to predict actions for state-only demonstrations.

Imitation Learning

Learning Predictive Representations for Deformable Objects Using Contrastive Estimation

1 code implementation11 Mar 2020 Wilson Yan, Ashwin Vangipuram, Pieter Abbeel, Lerrel Pinto

Using visual model-based learning for deformable object manipulation is challenging due to difficulties in learning plannable visual representations along with complex dynamic models.

Deformable Object Manipulation

Hierarchically Decoupled Imitation for Morphological Transfer

1 code implementation3 Mar 2020 Donald J. Hejna III, Pieter Abbeel, Lerrel Pinto

Learning long-range behaviors on complex high-dimensional agents is a fundamental problem in robot learning.

Generalized Hindsight for Reinforcement Learning

no code implementations NeurIPS 2020 Alexander C. Li, Lerrel Pinto, Pieter Abbeel

Compared to standard relabeling techniques, Generalized Hindsight provides a substantially more efficient reuse of samples, which we empirically demonstrate on a suite of multi-task navigation and manipulation tasks.

Discovering Motor Programs by Recomposing Demonstrations

no code implementations ICLR 2020 Tanmay Shankar, Shubham Tulsiani, Lerrel Pinto, Abhinav Gupta

In this paper, we present an approach to learn recomposable motor primitives across large-scale and diverse manipulation demonstrations.

Hierarchical Reinforcement Learning

Learning to Manipulate Deformable Objects without Demonstrations

1 code implementation29 Oct 2019 Yilin Wu, Wilson Yan, Thanard Kurutach, Lerrel Pinto, Pieter Abbeel

Second, instead of jointly learning both the pick and the place locations, we only explicitly learn the placing policy conditioned on random pick points.

Deformable Object Manipulation

Swoosh! Rattle! Thump! - Actions that Sound

no code implementations25 Sep 2019 Dhiraj Gandhi, Abhinav Gupta, Lerrel Pinto

In this work, we perform the first large-scale study of the interactions between sound and robotic action.

Environment Probing Interaction Policies

1 code implementation ICLR 2019 Wenxuan Zhou, Lerrel Pinto, Abhinav Gupta

A key challenge in reinforcement learning (RL) is environment generalization: a policy trained to solve a task in one environment often fails to solve the same task in a slightly different test environment.

Sample-Efficient Learning of Nonprehensile Manipulation Policies via Physics-Based Informed State Distributions

no code implementations24 Oct 2018 Lerrel Pinto, Aditya Mandalika, Brian Hou, Siddhartha Srinivasa

This paper proposes a sample-efficient yet simple approach to learning closed-loop policies for nonprehensile manipulation.

Multiple Interactions Made Easy (MIME): Large Scale Demonstrations Data for Imitation

no code implementations16 Oct 2018 Pratyusha Sharma, Lekha Mohan, Lerrel Pinto, Abhinav Gupta

In order to make progress and capture the space of manipulation, we would need to collect a large-scale dataset of diverse tasks such as pouring, opening bottles, stacking objects etc.

Trajectory Prediction

Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias

no code implementations NeurIPS 2018 Abhinav Gupta, Adithyavairavan Murali, Dhiraj Gandhi, Lerrel Pinto

The models trained with our home dataset showed a marked improvement of 43. 7% over a baseline model trained with data collected in lab.

Robotic Grasping

Asymmetric Actor Critic for Image-Based Robot Learning

no code implementations18 Oct 2017 Lerrel Pinto, Marcin Andrychowicz, Peter Welinder, Wojciech Zaremba, Pieter Abbeel

While several recent works have shown promising results in transferring policies trained in simulation to the real world, they often do not fully utilize the advantage of working with a simulator.

Decision Making

Predictive-State Decoders: Encoding the Future into Recurrent Networks

no code implementations NeurIPS 2017 Arun Venkatraman, Nicholas Rhinehart, Wen Sun, Lerrel Pinto, Martial Hebert, Byron Boots, Kris M. Kitani, J. Andrew Bagnell

We seek to combine the advantages of RNNs and PSRs by augmenting existing state-of-the-art recurrent neural networks with Predictive-State Decoders (PSDs), which add supervision to the network's internal state representation to target predicting future observations.

Imitation Learning

CASSL: Curriculum Accelerated Self-Supervised Learning

no code implementations4 Aug 2017 Adithyavairavan Murali, Lerrel Pinto, Dhiraj Gandhi, Abhinav Gupta

Recent self-supervised learning approaches focus on using a few thousand data points to learn policies for high-level, low-dimensional action spaces.

Curriculum Learning Self-Supervised Learning

Robust Adversarial Reinforcement Learning

6 code implementations ICML 2017 Lerrel Pinto, James Davidson, Rahul Sukthankar, Abhinav Gupta

Deep neural networks coupled with fast simulation and improved computation have led to recent successes in the field of reinforcement learning (RL).

Supervision via Competition: Robot Adversaries for Learning Tasks

1 code implementation5 Oct 2016 Lerrel Pinto, James Davidson, Abhinav Gupta

Due to large number of experiences required for training, most of these approaches use a self-supervised paradigm: using sensors to measure success/failure.

Learning to Push by Grasping: Using multiple tasks for effective learning

no code implementations28 Sep 2016 Lerrel Pinto, Abhinav Gupta

The argument of the difficulty in scalability to multiple tasks is well founded, since training these tasks often require hundreds or thousands of examples.

Multi-Task Learning

The Curious Robot: Learning Visual Representations via Physical Interactions

no code implementations5 Apr 2016 Lerrel Pinto, Dhiraj Gandhi, Yuanfeng Han, Yong-Lae Park, Abhinav Gupta

We argue that biological agents use physical interactions with the world to learn visual representations unlike current vision systems which just use passive observations (images and videos downloaded from web).

Image Classification Representation Learning

Supersizing Self-supervision: Learning to Grasp from 50K Tries and 700 Robot Hours

no code implementations23 Sep 2015 Lerrel Pinto, Abhinav Gupta

Our experiments clearly show the benefit of using large-scale datasets (and multi-stage training) for the task of grasping.

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