RoboDesk A Multi-Task Reinforcement Learning Benchmark

Related tags

Deep Learningrobodesk
Overview

RoboDesk

PyPI

A Multi-Task Reinforcement Learning Benchmark

Robodesk Banner

If you find this open source release useful, please reference in your paper:

@misc{kannan2021robodesk,
  author = {Harini Kannan and Danijar Hafner and Chelsea Finn and Dumitru Erhan},
  title = {RoboDesk: A Multi-Task Reinforcement Learning Benchmark},
  year = {2021},
  howpublished = {\url{https://github.com/google-research/robodesk}},
}

Highlights

  • Diversity: RoboDesk includes 9 diverse tasks that test for a variety of different behaviors within the same environment, making it useful for evaluating transfer, multi-task learning, and global exploration.
  • Complexity: The high-dimensional image inputs contain objects of different shapes and colors, whose initial positions are randomized to avoid naive memorization and require learning algorithms to generalize.
  • Robustness: We carefully designed and tested RoboDesk to ensure fast and stable physics simulation. This avoids objects from intersecting, getting stuck, or quickly flying away, a common problem with some existing environments.
  • Lightweight: RoboDesk comes as a self-contained Python package with few dependencies. The source code is clean and pragmatic, making it a useful blueprint for creating new MuJoCo environments.

Training Agents

Installation: pip3 install -U robodesk

The environment follows the OpenAI Gym interface:

import robodesk

env = robodesk.RoboDesk(seed=0)
obs = env.reset()
assert obs.shape == (64, 64, 3)

done = False
while not done:
  action = env.action_space.sample()
  obs, reward, done, info = env.step(action)

Tasks

Robodesk Tasks

The behaviors above were learned using the Dreamer agent. These policies have been learned from scratch and only from pixels, not proprioceptive states.

Task Description
open_slide Push the sliding door all the way to the right, navigating around the other objects.
open_drawer Pull the dark brown drawer all the way open.
push_green Push the green button to turn the green light on.
stack_blocks Stack the upright blue block on top of the flat green block.
upright_block_off_table Push the blue upright block off the table.
flat_block_in_bin Push the green flat block into the blue bin.
flat_block_in_shelf Push the green flat block into the shelf, navigating around the other blocks.
lift_upright_block Grasp the blue upright block and lift it above the table.
lift_ball Grasp the magenta ball and lift it above the table.

Environment Details

Constructor

robodesk.RoboDesk(task='open_slide', reward='dense', action_repeat=1, episode_length=500, image_size=64)
Parameter Description
task Available tasks are open_slide, open_drawer, push_green, stack, upright_block_off_table, flat_block_in_bin, flat_block_in_shelf, lift_upright_block, lift_ball.
reward Available reward types are dense, sparse, success. Success gives only the first sparse reward during the episode, useful for computing success rates during evaluation.
action_repeat Reduces the control frequency by applying each action multiple times. This is faster than using an environment wrapper because only the needed images are rendered.
episode_length Time limit for the episode, can be None.
image_size Size of the image observations in pixels, used for both height and width.

Reward

All rewards are bound between 0 and 1. There are three types of rewards available:

  • Dense rewards are based on Euclidean distances between the objects and their target positions and can include additional terms, for example to encourage the arm to reach the object. These are the easiest rewards for learning.
  • Sparse rewards are either 0 or 1 based on whether the target object is in the target area or not, according to a fixed threshold. Learning from sparse rewards is more challenging.
  • Success rewards are equivalent to the sparse rewards, except that only the first reward is given during each episode. As a result, an episode return of 0 means failure and 1 means sucess at the task. This should only be used during evaluation.

Termination

Episodes end after 500 time steps by default. There are no early terminations.

Observation Space

Each observation is a dictionary that contains the current image, as well as additional information. For the standard benchmark, only the image should be used for learning. The observation dictionary contains the following keys:

Key Space
image Box(0, 255, (64, 64, 3), np.uint8)
qpos_robot Box(-np.inf, np.inf, (9,), np.float32)
qvel_robot Box(-np.inf, np.inf, (9,), np.float32)
qpos_objects Box(-np.inf, np.inf, (26,), np.float32)
qvel_objects Box(-np.inf, np.inf, (26,), np.float32)
end_effector Box(-np.inf, np.inf, (3,), np.float32)

Action Space

RoboDesk uses end effector control with a simple bounded action space:

Box(-1, 1, (5,), np.float32)

Acknowledgements

We thank Ben Eysenbach and Debidatta Dwibedi for their helpful feedback.

Our benchmark builds upon previously open-sourced work. We build upon the desk XMLs first introduced in [1], the Franka XMLs open-sourced in [2], and the Franka meshes open-sourced in [3].

Questions

Please open an issue on Github.

Disclaimer: This is not an official Google product.

Owner
Google Research
Google Research
This is an unofficial PyTorch implementation of Meta Pseudo Labels

This is an unofficial PyTorch implementation of Meta Pseudo Labels. The official Tensorflow implementation is here.

Jungdae Kim 320 Jan 08, 2023
Traffic4D: Single View Reconstruction of Repetitious Activity Using Longitudinal Self-Supervision

Traffic4D: Single View Reconstruction of Repetitious Activity Using Longitudinal Self-Supervision Project | PDF | Poster Fangyu Li, N. Dinesh Reddy, X

25 Dec 21, 2022
E-Ink Magic Calendar that automatically syncs to Google Calendar and runs off a battery powered Raspberry Pi Zero

MagInkCal This repo contains the code needed to drive an E-Ink Magic Calendar that uses a battery powered (PiSugar2) Raspberry Pi Zero WH to retrieve

2.8k Dec 28, 2022
Losslandscapetaxonomy - Taxonomizing local versus global structure in neural network loss landscapes

Taxonomizing local versus global structure in neural network loss landscapes Int

Yaoqing Yang 8 Dec 30, 2022
Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels

The official code for the NeurIPS 2021 paper Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels

13 Dec 22, 2022
Intent parsing and slot filling in PyTorch with seq2seq + attention

PyTorch Seq2Seq Intent Parsing Reframing intent parsing as a human - machine translation task. Work in progress successor to torch-seq2seq-intent-pars

Sean Robertson 160 Jan 07, 2023
In this work, we will implement some basic but important algorithm of machine learning step by step.

WoRkS continued English 中文 Français Probability Density Estimation-Non-Parametric Methods(概率密度估计-非参数方法) 1. Kernel / k-Nearest Neighborhood Density Est

liziyu0104 1 Dec 30, 2021
NR-GAN: Noise Robust Generative Adversarial Networks

Lexicon Enhanced Chinese Sequence Labeling Using BERT Adapter Code and checkpoints for the ACL2021 paper "Lexicon Enhanced Chinese Sequence Labelling

Takuhiro Kaneko 59 Dec 11, 2022
CMP 414/765 course repository for Spring 2022 semester

CMP414/765: Artificial Intelligence Spring2021 This is the GitHub repository for course CMP 414/765: Artificial Intelligence taught at The City Univer

ch00226855 4 May 16, 2022
Sound Event Detection with FilterAugment

Sound Event Detection with FilterAugment Official implementation of Heavily Augmented Sound Event Detection utilizing Weak Predictions (DCASE2021 Chal

43 Aug 28, 2022
This repository includes the official project for the paper: TransMix: Attend to Mix for Vision Transformers.

TransMix: Attend to Mix for Vision Transformers This repository includes the official project for the paper: TransMix: Attend to Mix for Vision Transf

Jie-Neng Chen 130 Jan 01, 2023
M3DSSD: Monocular 3D Single Stage Object Detector

M3DSSD: Monocular 3D Single Stage Object Detector Setup pytorch 0.4.1 Preparation Download the full KITTI detection dataset. Then place a softlink (or

mumianyuxin 64 Dec 27, 2022
Deep generative modeling for time-stamped heterogeneous data, enabling high-fidelity models for a large variety of spatio-temporal domains.

Neural Spatio-Temporal Point Processes [arxiv] Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel Abstract. We propose a new class of parameterizations

Facebook Research 75 Dec 19, 2022
Implementation of the Transformer variant proposed in "Transformer Quality in Linear Time"

FLASH - Pytorch Implementation of the Transformer variant proposed in the paper Transformer Quality in Linear Time Install $ pip install FLASH-pytorch

Phil Wang 209 Dec 28, 2022
"Projelerle Yapay Zeka Ve Bilgisayarlı Görü" Kitabımın projeleri

"Projelerle Yapay Zeka Ve Bilgisayarlı Görü" Kitabımın projeleri Bu Github Reposundaki tüm projeler; kaleme almış olduğum "Projelerle Yapay Zekâ ve Bi

Ümit Aksoylu 4 Aug 03, 2022
A curated list of awesome Model-Based RL resources

Awesome Model-Based Reinforcement Learning This is a collection of research papers for model-based reinforcement learning (mbrl). And the repository w

OpenDILab 427 Jan 03, 2023
Extracting and filtering paraphrases by bridging natural language inference and paraphrasing

nli2paraphrases Source code repository accompanying the preprint Extracting and filtering paraphrases by bridging natural language inference and parap

Matej Klemen 1 Mar 09, 2022
Demo code for ICCV 2021 paper "Sensor-Guided Optical Flow"

Sensor-Guided Optical Flow Demo code for "Sensor-Guided Optical Flow", ICCV 2021 This code is provided to replicate results with flow hints obtained f

10 Mar 16, 2022
A Kitti Road Segmentation model implemented in tensorflow.

KittiSeg KittiSeg performs segmentation of roads by utilizing an FCN based model. The model achieved first place on the Kitti Road Detection Benchmark

Marvin Teichmann 890 Jan 04, 2023
To model the probability of a soccer coach leave his/her team during Campeonato Brasileiro for 10 chosen teams and considering years 2018, 2019 and 2020.

To model the probability of a soccer coach leave his/her team during Campeonato Brasileiro for 10 chosen teams and considering years 2018, 2019 and 2020.

Larissa Sayuri Futino Castro dos Santos 1 Jan 20, 2022