Main

processing priority

3

site type

5 (wiki-type site, growing by topic rather than chronologically)

review version

11

html import

20 (imported)

Events

first seen date

2024-08-10 15:41:44

expired found date

-

created at

2024-08-10 15:41:44

updated at

2024-10-11 23:17:29

Domain name statistics

length

17

crc

21442

tld

86

nm parts

0

nm random digits

0

nm rare letters

0

Connections

is subdomain of id

87719371 (github.io)

previous id

0

replaced with id

0

related id

-

dns primary id

0

dns alternative id

0

lifecycle status

0 (unclassified, or currently active)

Subdomains and pages

deleted subdomains

0

page imported products

0

page imported random

0

page imported parking

0

Error counters

count skipped due to recent timeouts on the same server IP

0

count content received but rejected due to 11-799

0

count dns errors

0

count cert errors

0

count timeouts

0

count http 429

0

count http 404

0

count http 403

0

count http 5xx

0

next operation date

-

Server

server bits

server ip

-

Mainpage statistics

mp import status

20

mp rejected date

-

mp saved date

-

mp size orig

7242

mp size raw text

2257

mp inner links count

0

mp inner links status

1 (no links)

Open Graph

title

description

image

site name

author

updated

2026-02-13 09:14:02

raw text

Motion Policy Networks Motion Policy Networks Adam Fishman 1,2 , Adithyavairan Murali 2 , Clemens Eppner 2 , Bryan Peele 2 , Byron Boots 1,2 , Dieter Fox 1,2 1 University of Washington, 2 NVIDIA Paper Code Data Abstract Collision-free motion generation in unknown environments is a core building block for robot manipulation. Generating such motions is challenging due to multiple objectives; not only should the solutions be optimal, the motion generator itself must be fast enough for real-time performance and reliable enough for practical deployment. A wide variety of methods have been proposed ranging from local controllers to global planners, often being combined to offset their shortcomings. We present an end-to-end neural model called Motion Policy Networks (MπNets) to generate collision-free, smooth motion from just a single depth camera observation. MπNets are trained on over 3 million motion planning problems in over 500,000 environments. Our...

Text analysis

redirect type

0 (-)

block type

0 (no issues)

detected language

1 (English)

category id

Military [en] (225)

index version

2025123101

spam phrases

0

Text statistics

text nonlatin

5

text cyrillic

0

text characters

1795

text words

330

text unique words

218

text lines

59

text sentences

10

text paragraphs

3

text words per sentence

33

text matched phrases

1

text matched dictionaries

2

RSS

rss path

rss status

1 (priority 1 already searched, no matches found)

rss found date

-

rss size orig

0

rss items

0

rss spam phrases

0

rss detected language

0 (awaiting analysis)

inbefore feed id

-

inbefore status

0 (new)

Sitemap

sitemap path

sitemap status

1 (priority 1 already searched, no matches found)

sitemap review version

2

sitemap urls count

0

sitemap urls adult

0

sitemap filtered products

0

sitemap filtered videos

0

sitemap found date

-

sitemap process date

-

sitemap first import date

-

sitemap last import date

-