Main

related bits

0

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

2024-08-29 20:38:32

expired found date

-

created at

2024-08-29 20:38:32

updated at

2024-09-29 16:25:39

Domain name statistics

length

18

crc

20081

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

9114

mp size raw text

3575

mp inner links count

0

mp inner links status

1 (no links)

Open Graph

title

description

Abstract

image

site name

author

updated

2026-01-31 22:47:31

raw text

DittoGym: Learning to Control Soft Shape-Shifting Robots DittoGym: Learning to Control Soft Shape-Shifting Robots Suning Huang , Boyuan Chen , Huazhe Xu , Vincent Sitzmann ICLR 2024 Paper Twitter DittoGym CFP Abstract Robot co-design, where the morphology of a robot is optimized jointly with a learned policy to solve a specific task, is an emerging area of research. It holds particular promise for soft robots, which are amenable to novel manufacturing techniques that can realize learned morphologies and actuators. Inspired by nature and recent novel robot designs, we propose to go a step further and explore the design of reconfigurable robots, defined as robots that can change their morphology within their lifetime. We formalize control of reconfigurable soft robots as a high-dimensional reinforcement learning (RL) problem. We unify morphology change, locomotion, and environment interaction in the same action space, and introduce an appropria...

Text analysis

redirect type

0 (-)

block type

0 (no issues)

detected language

1 (English)

category id

AI [en] (229)

index version

2025123101

spam phrases

0

Text statistics

text nonlatin

0

text cyrillic

0

text characters

2796

text words

502

text unique words

263

text lines

48

text sentences

21

text paragraphs

6

text words per sentence

23

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

-