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
Automatic Idiomatic Expression Detection

IDentifier of Idiomatic Expressions via Semantic Compatibility (DISC) An Idiomatic identifier that detects the presence and span of idiomatic expressi

5 Jun 09, 2022
A python library for highly configurable transformers - easing model architecture search and experimentation.

A python library for highly configurable transformers - easing model architecture search and experimentation.

Anthony Fuller 51 Nov 20, 2022
A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion

A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion This repo intends to release code for our work: Zhaoyang Lyu*, Zhifeng

Zhaoyang Lyu 68 Jan 03, 2023
Using OpenAI's CLIP to upscale and enhance images

CLIP Upscaler and Enhancer Using OpenAI's CLIP to upscale and enhance images Based on nshepperd's JAX CLIP Guided Diffusion v2.4 Sample Results Viewpo

Tripp Lyons 5 Jun 14, 2022
TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification

TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification [NeurIPS 2021] Abstract Multiple instance learn

132 Dec 30, 2022
Towards Rolling Shutter Correction and Deblurring in Dynamic Scenes (CVPR2021)

RSCD (BS-RSCD & JCD) Towards Rolling Shutter Correction and Deblurring in Dynamic Scenes (CVPR2021) by Zhihang Zhong, Yinqiang Zheng, Imari Sato We co

81 Dec 15, 2022
Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

Mask R-CNN for Object Detection and Segmentation This is an implementation of Mask R-CNN on Python 3, Keras, and TensorFlow. The model generates bound

Matterport, Inc 22.5k Jan 04, 2023
Negative Interactions for Improved Collaborative Filtering:

Negative Interactions for Improved Collaborative Filtering: Don’t go Deeper, go Higher This notebook provides an implementation in Python 3 of the alg

Harald Steck 21 Mar 05, 2022
A Python training and inference implementation of Yolov5 helmet detection in Jetson Xavier nx and Jetson nano

yolov5-helmet-detection-python A Python implementation of Yolov5 to detect head or helmet in the wild in Jetson Xavier nx and Jetson nano. In Jetson X

12 Dec 05, 2022
Capstone-Project-2 - A game program written in the Python language

Capstone-Project-2 My Pygame Game Information: Description This Pygame project i

Nhlakanipho Khulekani Hlophe 1 Jan 04, 2022
3D ResNets for Action Recognition (CVPR 2018)

3D ResNets for Action Recognition Update (2020/4/13) We published a paper on arXiv. Hirokatsu Kataoka, Tenga Wakamiya, Kensho Hara, and Yutaka Satoh,

Kensho Hara 3.5k Jan 06, 2023
Practical tutorials and labs for TensorFlow used by Nvidia, FFN, CNN, RNN, Kaggle, AE

TensorFlow Tutorial - used by Nvidia Learn TensorFlow from scratch by examples and visualizations with interactive jupyter notebooks. Learn to compete

Alexander R Johansen 1.9k Dec 19, 2022
Automatic Image Background Subtraction

Automatic Image Background Subtraction This repo contains set of scripts for automatic one-shot image background subtraction task using the following

Oleg Sémery 6 Dec 05, 2022
This is the official repository for our paper: ''Pruning Self-attentions into Convolutional Layers in Single Path''.

Pruning Self-attentions into Convolutional Layers in Single Path This is the official repository for our paper: Pruning Self-attentions into Convoluti

Zhuang AI Group 77 Dec 26, 2022
Baselines for TrajNet++

TrajNet++ : The Trajectory Forecasting Framework PyTorch implementation of Human Trajectory Forecasting in Crowds: A Deep Learning Perspective TrajNet

VITA lab at EPFL 183 Jan 05, 2023
Implementation of "Generalizable Neural Performer: Learning Robust Radiance Fields for Human Novel View Synthesis"

Generalizable Neural Performer: Learning Robust Radiance Fields for Human Novel View Synthesis Abstract: This work targets at using a general deep lea

163 Dec 14, 2022
Code accompanying the paper on "An Empirical Investigation of Domain Generalization with Empirical Risk Minimizers" published at NeurIPS, 2021

Code for "An Empirical Investigation of Domian Generalization with Empirical Risk Minimizers" (NeurIPS 2021) Motivation and Introduction Domain Genera

Meta Research 15 Dec 27, 2022
Intel® Neural Compressor is an open-source Python library running on Intel CPUs and GPUs

Intel® Neural Compressor targeting to provide unified APIs for network compression technologies, such as low precision quantization, sparsity, pruning, knowledge distillation, across different deep l

Intel Corporation 846 Jan 04, 2023
Tutorial: Introduction to Graph Machine Learning, with Jupyter notebooks

GraphMLTutorialNLDL22 Tutorial NLDL22: Introduction to Graph Machine Learning, with Jupyter notebooks This tutorial takes place during the conference

UiT Machine Learning Group 3 Jan 10, 2022
YoloV3 Implemented in Tensorflow 2.0

YoloV3 Implemented in TensorFlow 2.0 This repo provides a clean implementation of YoloV3 in TensorFlow 2.0 using all the best practices. Key Features

Zihao Zhang 2.5k Dec 26, 2022