├── ICRA_2021_RL.csv ├── README.md ├── LICENSE ├── rlpapers.txt └── ICRA_area_summary.ipynb /ICRA_2021_RL.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tsaoyu/ICRA_2021_RL/HEAD/ICRA_2021_RL.csv -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # ICRA_2021_RL 2 | Paper list on ICRA reinforcement learning 3 | 4 | 5 | You can also use ICRA area summary notebook to generate a list like this for any area. 6 | The notebook should be self explanatory and what you need to change is the `rlpapers.txt`. 7 | 8 | ![WeChat Image_20210608095226](https://user-images.githubusercontent.com/6488896/121110114-5638f400-c83f-11eb-9daa-628a4a7f1f70.png) 9 | 10 | 11 | Simply copy all `` HTML tag of the papers you interested, the notebook will do the rest for you. 12 | This project relies on the following 13 | 14 | ``` 15 | pandas 16 | beautifulsoup 17 | requests 18 | ``` 19 | it is more likely your dependency is missing than the code is wrong. 20 | 21 | Thanks for using and happy hacking. 22 | 23 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2021 Tony Yu Cao 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /rlpapers.txt: -------------------------------------------------------------------------------- 1 | ThAT1.4, ThAT14.1, ThBT20.3, ThBT7.1, ThBT7.4, ThDT1.2, ThDT1.4, ThDT14.1, ThDT14.3, ThDT7.1, ThDT8.3, ThET12.1, ThET17.2, ThFT16.3, ThFT6.3, ThHT12.2, ThHT12.3, ThHT12.4, ThHT16.3, ThHT2.1, ThHT20.4, ThHT6.3, ThHT9.2, ThHT9.3, ThIT12.1, ThIT12.2, ThIT12.3, ThIT12.4, ThIT18.2, ThIT2.1, ThIT2.2, ThIT21.2, ThIT5.1, ThIT5.2, ThJT10.3, ThJT13.1, ThJT15.3, ThJT19.2, ThJT6.2, ThJT6.4, ThKT12.4, ThKT2.2, ThKT20.1, ThKT7.1, ThKT7.4, TuAT13.1, TuAT13.2, TuAT13.3, TuAT13.4, TuAT14.1, TuAT14.2, TuAT15.2, TuAT19.1, TuAT23.2, TuAT4.4, TuAT9.2, TuBT16.1, TuBT19.1, TuBT23.2, TuBT5.3, TuCT14.2, TuCT15.3, TuCT16.2, TuCT16.3, TuCT18.1, TuCT3.1, TuCT7.2, TuDT10.2, TuDT11.2, TuDT11.3, TuDT12.1, TuDT12.4, TuDT2.4, TuDT20.2, TuDT5.2, TuDT7.4, TuET10.1, TuET10.3, TuET10.4, TuET12.1, TuET12.2, TuET12.4, TuET21.1, TuET4.3, TuFT18.3, TuFT20.2, TuGT11.1, TuGT12.4, TuGT13.2, TuGT19.1, TuGT2.1, TuGT2.2, TuGT2.3, TuGT2.4, TuGT2.5, TuGT22.1, TuGT22.2, TuGT5.1, TuGT8.1, TuGT8.3, TuHT1.1, TuHT1.3, TuHT10.4, TuHT11.2, TuHT13.4, TuHT18.2, TuHT8.4, TuIT16.2, TuIT16.3, TuIT24.3, TuIT5.2, TuIT5.4, TuIT9.3, TuJT10.2, TuJT12.2, TuJT12.4, TuJT15.3, TuJT16.1, TuJT16.3, TuJT2.1, TuJT2.2, TuJT2.3, TuJT20.1, TuJT20.2, TuJT20.4, TuJT21.1, TuJT22.1, TuJT22.2, TuJT5.2, TuJT5.3, TuJT6.3, TuKT10.2, TuKT13.1, TuKT13.2, TuKT13.4, TuKT18.2, TuKT18.3, TuKT2.2, TuKT2.3, TuKT2.4, TuKT20.3, TuKT21.4, TuKT3.2, TuKT5.1, TuKT6.2, TuKT7.1, TuKT7.3, WeAT10.2, WeAT3.3, WeBT1.3, WeBT1.4, WeBT10.3, WeBT15.3, WeBT17.1, WeBT4.1, WeBT5.2, WeBT7.3, WeCT11.1, WeCT9.3 -------------------------------------------------------------------------------- /ICRA_area_summary.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import pandas as pd" 10 | ] 11 | }, 12 | { 13 | "cell_type": "markdown", 14 | "metadata": {}, 15 | "source": [ 16 | "It is easy to generate papers summary for any area. See more at README\n", 17 | "\n", 18 | "\n" 19 | ] 20 | }, 21 | { 22 | "cell_type": "code", 23 | "execution_count": 2, 24 | "metadata": {}, 25 | "outputs": [], 26 | "source": [ 27 | "from html.parser import HTMLParser\n", 28 | "\n", 29 | "paper_session = []\n", 30 | "class MyHTMLParser(HTMLParser):\n", 31 | " \n", 32 | " def handle_starttag(self, tag, attrs):\n", 33 | " self.data = attrs\n", 34 | "\n", 35 | " def handle_endtag(self, tag):\n", 36 | " pass\n", 37 | "\n", 38 | " def handle_data(self, data):\n", 39 | " if len(data) > 2:\n", 40 | " #print(\"Encountered some data :\", data)\n", 41 | " paper_session.append([self.data, data])\n", 42 | " \n", 43 | "\n", 44 | "parser = MyHTMLParser()\n", 45 | "\n", 46 | "\n", 47 | "file = open('./rlpapers.txt')\n", 48 | "lines = file.read()\n", 49 | "\n", 50 | "parser.feed(lines)" 51 | ] 52 | }, 53 | { 54 | "cell_type": "code", 55 | "execution_count": 3, 56 | "metadata": {}, 57 | "outputs": [], 58 | "source": [ 59 | "full_list = []\n", 60 | "def session_processing(s):\n", 61 | " return [s[1],s[0][0][1]]\n", 62 | "for s in paper_session:\n", 63 | " full_list.append(session_processing(s))" 64 | ] 65 | }, 66 | { 67 | "cell_type": "code", 68 | "execution_count": 4, 69 | "metadata": {}, 70 | "outputs": [ 71 | { 72 | "name": "stdout", 73 | "output_type": "stream", 74 | "text": [ 75 | "There are 159 papers in your selection.\n" 76 | ] 77 | } 78 | ], 79 | "source": [ 80 | "print(\"There are {} papers in your selection.\".format(len(full_list)))" 81 | ] 82 | }, 83 | { 84 | "cell_type": "code", 85 | "execution_count": 5, 86 | "metadata": {}, 87 | "outputs": [], 88 | "source": [ 89 | "import requests" 90 | ] 91 | }, 92 | { 93 | "cell_type": "code", 94 | "execution_count": 6, 95 | "metadata": {}, 96 | "outputs": [], 97 | "source": [ 98 | "request1 = requests.get('https://ras.papercept.net/conferences/conferences/ICRA21/program/ICRA21_ContentListWeb_1.html')\n", 99 | "request2 = requests.get('https://ras.papercept.net/conferences/conferences/ICRA21/program/ICRA21_ContentListWeb_2.html')\n", 100 | "request3 = requests.get('https://ras.papercept.net/conferences/conferences/ICRA21/program/ICRA21_ContentListWeb_3.html')" 101 | ] 102 | }, 103 | { 104 | "cell_type": "code", 105 | "execution_count": 7, 106 | "metadata": {}, 107 | "outputs": [], 108 | "source": [ 109 | "data1 = request1.content\n", 110 | "data2 = request2.content\n", 111 | "data3 = request3.content" 112 | ] 113 | }, 114 | { 115 | "cell_type": "code", 116 | "execution_count": 8, 117 | "metadata": {}, 118 | "outputs": [], 119 | "source": [ 120 | "[data1, data2, data3];" 121 | ] 122 | }, 123 | { 124 | "cell_type": "code", 125 | "execution_count": 9, 126 | "metadata": {}, 127 | "outputs": [], 128 | "source": [ 129 | "from bs4 import BeautifulSoup\n", 130 | "soup = BeautifulSoup(data1, 'html.parser')" 131 | ] 132 | }, 133 | { 134 | "cell_type": "code", 135 | "execution_count": 10, 136 | "metadata": {}, 137 | "outputs": [], 138 | "source": [ 139 | "soup1, soup2, soup3 = [BeautifulSoup(data1, 'html.parser'), \n", 140 | " BeautifulSoup(data2, 'html.parser'),\n", 141 | " BeautifulSoup(data3, 'html.parser')]" 142 | ] 143 | }, 144 | { 145 | "cell_type": "code", 146 | "execution_count": 11, 147 | "metadata": {}, 148 | "outputs": [], 149 | "source": [ 150 | "soups = [soup1, soup2, soup3]" 151 | ] 152 | }, 153 | { 154 | "cell_type": "code", 155 | "execution_count": 12, 156 | "metadata": {}, 157 | "outputs": [], 158 | "source": [ 159 | "def summary_data(paper_info):\n", 160 | " web_page,paper_id = paper_info[-1].rsplit('#')\n", 161 | " soup = soups[int(web_page.split('.')[0][-1])-1]\n", 162 | " a = soup.find('a', {\"name\":paper_id})\n", 163 | " title = a.find_next('a', {\"title\":\"Click to show or hide the keywords and abstract (text summary)\"}).string\n", 164 | " abstract = a.find_next('div').text.rsplit('Abstract: ')[-1]\n", 165 | " author_list = []\n", 166 | " for name in a.find_all_next('a'):\n", 167 | " try:\n", 168 | " name[\"name\"] != None\n", 169 | " break\n", 170 | " except:\n", 171 | " if name[\"title\"] == \"Click to go to the Author Index\":\n", 172 | " author_list.append(name.text)\n", 173 | " aff_list = []\n", 174 | " td_counter = 0\n", 175 | " for name in a.find_all_next(\"td\", {\"class\":\"r\"}):\n", 176 | " if \"Add to\" in name.text:\n", 177 | " if td_counter == 1:\n", 178 | " break\n", 179 | " td_counter += 1\n", 180 | " else:\n", 181 | " aff_list.append(name.text)\n", 182 | " \n", 183 | " return title, abstract, author_list, aff_list" 184 | ] 185 | }, 186 | { 187 | "cell_type": "code", 188 | "execution_count": 13, 189 | "metadata": {}, 190 | "outputs": [], 191 | "source": [ 192 | "rl_icra = []" 193 | ] 194 | }, 195 | { 196 | "cell_type": "code", 197 | "execution_count": 14, 198 | "metadata": {}, 199 | "outputs": [], 200 | "source": [ 201 | "for s in full_list:\n", 202 | " rl_icra.append(summary_data(s))" 203 | ] 204 | }, 205 | { 206 | "cell_type": "code", 207 | "execution_count": 15, 208 | "metadata": {}, 209 | "outputs": [], 210 | "source": [ 211 | "df = pd.DataFrame(data=rl_icra)" 212 | ] 213 | }, 214 | { 215 | "cell_type": "code", 216 | "execution_count": 16, 217 | "metadata": {}, 218 | "outputs": [], 219 | "source": [ 220 | "df.columns = [\"Title\", \"Abstract\",\"Authors\", \"Affiliations\"]" 221 | ] 222 | }, 223 | { 224 | "cell_type": "code", 225 | "execution_count": 17, 226 | "metadata": {}, 227 | "outputs": [], 228 | "source": [ 229 | "df.to_csv(\"ICRA_long.csv\")" 230 | ] 231 | }, 232 | { 233 | "cell_type": "code", 234 | "execution_count": 18, 235 | "metadata": {}, 236 | "outputs": [], 237 | "source": [ 238 | "au_af = []" 239 | ] 240 | }, 241 | { 242 | "cell_type": "code", 243 | "execution_count": 19, 244 | "metadata": {}, 245 | "outputs": [], 246 | "source": [ 247 | "for i, row in df.iterrows():\n", 248 | " j = 0 \n", 249 | " for r in row[\"Authors\"]:\n", 250 | " au_af.append(r + \"_\" +row[\"Affiliations\"][j])\n", 251 | " j += 1" 252 | ] 253 | }, 254 | { 255 | "cell_type": "code", 256 | "execution_count": 20, 257 | "metadata": {}, 258 | "outputs": [], 259 | "source": [ 260 | "from collections import Counter" 261 | ] 262 | }, 263 | { 264 | "cell_type": "code", 265 | "execution_count": 21, 266 | "metadata": {}, 267 | "outputs": [], 268 | "source": [ 269 | "ct = Counter(au_af)" 270 | ] 271 | }, 272 | { 273 | "cell_type": "code", 274 | "execution_count": 22, 275 | "metadata": {}, 276 | "outputs": [], 277 | "source": [ 278 | "for i, row in df.iterrows():\n", 279 | " j = 0\n", 280 | " max_p = 0\n", 281 | " for r in row[\"Authors\"]:\n", 282 | " a = r + \"_\" +row[\"Affiliations\"][j]\n", 283 | " if ct[a] > max_p:\n", 284 | " max_p = ct[a]\n", 285 | " au_af.append(a)\n", 286 | " j += 1\n", 287 | " df.at[i,\"max_p\"] = max_p\n", 288 | " " 289 | ] 290 | }, 291 | { 292 | "cell_type": "code", 293 | "execution_count": 23, 294 | "metadata": {}, 295 | "outputs": [ 296 | { 297 | 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TitleAbstractAuthorsAffiliationsmax_p
0Towards Efficient Multiview Object Detection w...Active vision is a desirable perceptual featur...[Xu, Qianli, Fang, Fen, Gauthier, Nicolas, Lia...[Institute for Infocomm Research, I2R, Institu...1.0
1Deep Reinforcement Learning Framework for Unde...Soft robotics is an emerging technology with e...[Li, Guanda, Shintake, Jun, Hayashibe, Mitsuhiro][Tohoku University, University of Electro-Comm...2.0
2Robotic Imitation of Human Assembly Skills Usi...Robotic assembly tasks involve complex and low...[Wang, Yan, Beltran-Hernandez, Cristian Camilo...[Osaka University, Osaka University, Osaka Uni...1.0
3Context-Aware Safe Reinforcement Learning for ...Safety is a critical concern when deploying re...[Chen, Baiming, Liu, Zuxin, Zhu, Jiacheng, Xu,...[Tsinghua University, Carnegie Mellon Universi...2.0
4Quantification of Joint Redundancy Considering...The robotic joint redundancy for executing a t...[Chai, Jiazheng, Hayashibe, Mitsuhiro][Tohoku University, Tohoku University]2.0
..................
154Multi-Target Coverage with Connectivity Mainte...This paper considers a multi-target coverage p...[Wu, Shiguang, Pu, Zhiqiang, Liu, Zhen, Qiu, T...[Chinese Academy of Sciences Beijing, China, U...1.0
155Remote-Center-Of-Motion Recommendation Toward ...Brain needle intervention is a typical diagnos...[Gao, Huxin, Xiao, Xiao, Qiu, Liang, Meng, Max...[National University of Singapore, Southern Un...1.0
156A General Approach for the Automation of Hydra...This article presents a general approach to de...[Egli, Pascal Arturo, Hutter, Marco][RSL, ETHZ, ETH Zurich]1.0
157Relational Navigation Learning in Continuous A...In this paper, a novel navigation learning met...[Zhang, Xueyou, Xi, Wei, Guo, Xian, Fang, Yong...[Nankai University, Nankai University, Nankai ...1.0
158Autonomous Overtaking in Gran Turismo Sport Us...Professional race-car drivers can execute extr...[Song, Yunlong, Lin, HaoChih, Kaufmann, Elia, ...[University of Zurich, ETH Zurich, University ...2.0
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159 rows × 5 columns

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" 417 | ], 418 | "text/plain": [ 419 | " Title \\\n", 420 | "0 Towards Efficient Multiview Object Detection w... \n", 421 | "1 Deep Reinforcement Learning Framework for Unde... \n", 422 | "2 Robotic Imitation of Human Assembly Skills Usi... \n", 423 | "3 Context-Aware Safe Reinforcement Learning for ... \n", 424 | "4 Quantification of Joint Redundancy Considering... \n", 425 | ".. ... \n", 426 | "154 Multi-Target Coverage with Connectivity Mainte... \n", 427 | "155 Remote-Center-Of-Motion Recommendation Toward ... \n", 428 | "156 A General Approach for the Automation of Hydra... \n", 429 | "157 Relational Navigation Learning in Continuous A... \n", 430 | "158 Autonomous Overtaking in Gran Turismo Sport Us... \n", 431 | "\n", 432 | " Abstract \\\n", 433 | "0 Active vision is a desirable perceptual featur... \n", 434 | "1 Soft robotics is an emerging technology with e... \n", 435 | "2 Robotic assembly tasks involve complex and low... \n", 436 | "3 Safety is a critical concern when deploying re... \n", 437 | "4 The robotic joint redundancy for executing a t... \n", 438 | ".. ... \n", 439 | "154 This paper considers a multi-target coverage p... \n", 440 | "155 Brain needle intervention is a typical diagnos... \n", 441 | "156 This article presents a general approach to de... \n", 442 | "157 In this paper, a novel navigation learning met... \n", 443 | "158 Professional race-car drivers can execute extr... \n", 444 | "\n", 445 | " Authors \\\n", 446 | "0 [Xu, Qianli, Fang, Fen, Gauthier, Nicolas, Lia... \n", 447 | "1 [Li, Guanda, Shintake, Jun, Hayashibe, Mitsuhiro] \n", 448 | "2 [Wang, Yan, Beltran-Hernandez, Cristian Camilo... \n", 449 | "3 [Chen, Baiming, Liu, Zuxin, Zhu, Jiacheng, Xu,... \n", 450 | "4 [Chai, Jiazheng, Hayashibe, Mitsuhiro] \n", 451 | ".. ... \n", 452 | "154 [Wu, Shiguang, Pu, Zhiqiang, Liu, Zhen, Qiu, T... \n", 453 | "155 [Gao, Huxin, Xiao, Xiao, Qiu, Liang, Meng, Max... \n", 454 | "156 [Egli, Pascal Arturo, Hutter, Marco] \n", 455 | "157 [Zhang, Xueyou, Xi, Wei, Guo, Xian, Fang, Yong... \n", 456 | "158 [Song, Yunlong, Lin, HaoChih, Kaufmann, Elia, ... \n", 457 | "\n", 458 | " Affiliations max_p \n", 459 | "0 [Institute for Infocomm Research, I2R, Institu... 1.0 \n", 460 | "1 [Tohoku University, University of Electro-Comm... 2.0 \n", 461 | "2 [Osaka University, Osaka University, Osaka Uni... 1.0 \n", 462 | "3 [Tsinghua University, Carnegie Mellon Universi... 2.0 \n", 463 | "4 [Tohoku University, Tohoku University] 2.0 \n", 464 | ".. ... ... \n", 465 | "154 [Chinese Academy of Sciences Beijing, China, U... 1.0 \n", 466 | "155 [National University of Singapore, Southern Un... 1.0 \n", 467 | "156 [RSL, ETHZ, ETH Zurich] 1.0 \n", 468 | "157 [Nankai University, Nankai University, Nankai ... 1.0 \n", 469 | "158 [University of Zurich, ETH Zurich, University ... 2.0 \n", 470 | "\n", 471 | "[159 rows x 5 columns]" 472 | ] 473 | }, 474 | "execution_count": 23, 475 | "metadata": {}, 476 | "output_type": "execute_result" 477 | } 478 | ], 479 | "source": [ 480 | "df" 481 | ] 482 | }, 483 | { 484 | "cell_type": "code", 485 | "execution_count": 24, 486 | "metadata": {}, 487 | "outputs": [], 488 | "source": [ 489 | "df = df.sort_values(by=[\"max_p\"],ascending=False)" 490 | ] 491 | }, 492 | { 493 | "cell_type": "code", 494 | "execution_count": 25, 495 | "metadata": {}, 496 | "outputs": [ 497 | { 498 | "data": { 499 | "text/html": [ 500 | "
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TitleAbstractAuthorsAffiliationsmax_p
137Reset-Free Reinforcement Learning Via Multi-Ta...Reinforcement Learning (RL) algorithms can in ...[Gupta, Abhishek, Yu, Justin, Zhao, Zihao, Kum...[UC Berkeley, UC Berkeley, UC Berkeley, Univ. ...9.0
120DisCo RL: Distribution-Conditioned Reinforceme...Can we use reinforcement learning to instead l...[Nasiriany, Soroush, Pong, Vitchyr, Nair, Ashv...[UC Berkeley, UC Berkeley, UC Berkeley, UC Ber...9.0
33What Can I Do Here? Learning New Skills by Ima...A generalist robot equipped with learned skill...[Khazatsky, Alexander, Nair, Ashvin, Jing, Dan...[UC Berkeley, UC Berkeley, University of Calif...9.0
134Reinforcement Learning for Robust Parameterize...Developing robust walking controllers for bipe...[Li, Zhongyu, Cheng, Xuxin, Peng, Xue Bin, Abb...[University of California, Berkeley, Universit...9.0
26Model-Based Meta-Reinforcement Learning for Fl...Transporting suspended payloads is challenging...[Belkhale, Suneel, Kahn, Gregory, McAllister, ...[Stanford University, University of California...9.0
..................
52A Peg-In-Hole Task Strategy for Holes in ConcreteA method that enables an industrial robot to a...[Yasutomi, André Yuji, Mori, Hiroki, Ogata, Te...[Hitachi Ltd, Waseda University, Waseda Univer...1.0
51Learning from Demonstration without Demonstrat...State-of-the-art reinforcement learning (RL) a...[Blau, Tom, Morere, Philippe, Francis, Gilad][University of Sydney, University of Sydney, T...1.0
50Dreaming: Model-Based Reinforcement Learning b...In the present paper, we propose a decoder-fre...[Okada, Masashi, Taniguchi, Tadahiro][Panasonic Corporation, Ritsumeikan University]1.0
49Sample Efficient Reinforcement Learning Via Mo...Model-based deep reinforcement learning has ac...[Yao, Yao, Xiao, Li, An, Zhicheng, Zhang, Wanp...[Tsinghua-Berkeley Shenzhen Institute, Tsinghu...1.0
79Sample-Efficient Reinforcement Learning in Rob...Reinforcement learning (RL) has recently shown...[Tebbe, Jonas, Krauch, Lukas, Gao, Yapeng, Zel...[University of Tübingen, University of Tübinge...1.0
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159 rows × 5 columns

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" 618 | ], 619 | "text/plain": [ 620 | " Title \\\n", 621 | "137 Reset-Free Reinforcement Learning Via Multi-Ta... \n", 622 | "120 DisCo RL: Distribution-Conditioned Reinforceme... \n", 623 | "33 What Can I Do Here? Learning New Skills by Ima... \n", 624 | "134 Reinforcement Learning for Robust Parameterize... \n", 625 | "26 Model-Based Meta-Reinforcement Learning for Fl... \n", 626 | ".. ... \n", 627 | "52 A Peg-In-Hole Task Strategy for Holes in Concrete \n", 628 | "51 Learning from Demonstration without Demonstrat... \n", 629 | "50 Dreaming: Model-Based Reinforcement Learning b... \n", 630 | "49 Sample Efficient Reinforcement Learning Via Mo... \n", 631 | "79 Sample-Efficient Reinforcement Learning in Rob... \n", 632 | "\n", 633 | " Abstract \\\n", 634 | "137 Reinforcement Learning (RL) algorithms can in ... \n", 635 | "120 Can we use reinforcement learning to instead l... \n", 636 | "33 A generalist robot equipped with learned skill... \n", 637 | "134 Developing robust walking controllers for bipe... \n", 638 | "26 Transporting suspended payloads is challenging... \n", 639 | ".. ... \n", 640 | "52 A method that enables an industrial robot to a... \n", 641 | "51 State-of-the-art reinforcement learning (RL) a... \n", 642 | "50 In the present paper, we propose a decoder-fre... \n", 643 | "49 Model-based deep reinforcement learning has ac... \n", 644 | "79 Reinforcement learning (RL) has recently shown... \n", 645 | "\n", 646 | " Authors \\\n", 647 | "137 [Gupta, Abhishek, Yu, Justin, Zhao, Zihao, Kum... \n", 648 | "120 [Nasiriany, Soroush, Pong, Vitchyr, Nair, Ashv... \n", 649 | "33 [Khazatsky, Alexander, Nair, Ashvin, Jing, Dan... \n", 650 | "134 [Li, Zhongyu, Cheng, Xuxin, Peng, Xue Bin, Abb... \n", 651 | "26 [Belkhale, Suneel, Kahn, Gregory, McAllister, ... \n", 652 | ".. ... \n", 653 | "52 [Yasutomi, André Yuji, Mori, Hiroki, Ogata, Te... \n", 654 | "51 [Blau, Tom, Morere, Philippe, Francis, Gilad] \n", 655 | "50 [Okada, Masashi, Taniguchi, Tadahiro] \n", 656 | "49 [Yao, Yao, Xiao, Li, An, Zhicheng, Zhang, Wanp... \n", 657 | "79 [Tebbe, Jonas, Krauch, Lukas, Gao, Yapeng, Zel... \n", 658 | "\n", 659 | " Affiliations max_p \n", 660 | "137 [UC Berkeley, UC Berkeley, UC Berkeley, Univ. ... 9.0 \n", 661 | "120 [UC Berkeley, UC Berkeley, UC Berkeley, UC Ber... 9.0 \n", 662 | "33 [UC Berkeley, UC Berkeley, University of Calif... 9.0 \n", 663 | "134 [University of California, Berkeley, Universit... 9.0 \n", 664 | "26 [Stanford University, University of California... 9.0 \n", 665 | ".. ... ... \n", 666 | "52 [Hitachi Ltd, Waseda University, Waseda Univer... 1.0 \n", 667 | "51 [University of Sydney, University of Sydney, T... 1.0 \n", 668 | "50 [Panasonic Corporation, Ritsumeikan University] 1.0 \n", 669 | "49 [Tsinghua-Berkeley Shenzhen Institute, Tsinghu... 1.0 \n", 670 | "79 [University of Tübingen, University of Tübinge... 1.0 \n", 671 | "\n", 672 | "[159 rows x 5 columns]" 673 | ] 674 | }, 675 | "execution_count": 25, 676 | "metadata": {}, 677 | "output_type": "execute_result" 678 | } 679 | ], 680 | "source": [ 681 | "df" 682 | ] 683 | }, 684 | { 685 | "cell_type": "code", 686 | "execution_count": 26, 687 | "metadata": {}, 688 | "outputs": [], 689 | "source": [ 690 | "df.to_csv(\"ICRA_long_sorted.csv\")" 691 | ] 692 | }, 693 | { 694 | "cell_type": "code", 695 | "execution_count": 27, 696 | "metadata": {}, 697 | "outputs": [], 698 | "source": [ 699 | "short_info = []" 700 | ] 701 | }, 702 | { 703 | "cell_type": "code", 704 | "execution_count": 28, 705 | "metadata": {}, 706 | "outputs": [], 707 | "source": [ 708 | "for i, row in df.iterrows():\n", 709 | " short_info.append([row[\"Title\"], row[\"Authors\"][0], row[\"Affiliations\"][0]])" 710 | ] 711 | }, 712 | { 713 | "cell_type": "code", 714 | "execution_count": 29, 715 | "metadata": {}, 716 | "outputs": [], 717 | "source": [ 718 | "pd.DataFrame(short_info).to_csv(\"ICRA_short.csv\")" 719 | ] 720 | }, 721 | { 722 | "cell_type": "code", 723 | "execution_count": null, 724 | "metadata": {}, 725 | "outputs": [], 726 | "source": [] 727 | } 728 | ], 729 | "metadata": { 730 | "kernelspec": { 731 | "display_name": "Python 3", 732 | "language": "python", 733 | "name": "python3" 734 | }, 735 | "language_info": { 736 | "codemirror_mode": { 737 | "name": "ipython", 738 | "version": 3 739 | }, 740 | "file_extension": ".py", 741 | "mimetype": "text/x-python", 742 | "name": "python", 743 | "nbconvert_exporter": "python", 744 | "pygments_lexer": "ipython3", 745 | "version": "3.7.6" 746 | } 747 | }, 748 | "nbformat": 4, 749 | "nbformat_minor": 5 750 | } 751 | --------------------------------------------------------------------------------