{"id":17660,"date":"2024-01-17T16:53:25","date_gmt":"2024-01-17T08:53:25","guid":{"rendered":"https:\/\/aif.amtbbs.org\/?p=17660"},"modified":"2024-01-17T16:53:25","modified_gmt":"2024-01-17T08:53:25","slug":"%e6%9c%ba%e5%99%a8%e4%ba%ba%e9%a2%86%e5%9f%9f%e9%a6%96%e4%b8%aa%e5%bc%80%e6%ba%90%e8%a7%86%e8%a7%89-%e8%af%ad%e8%a8%80%e6%93%8d%e4%bd%9c%e5%a4%a7%e6%a8%a1%e5%9e%8b%ef%bc%8croboflamingo%e6%a1%86","status":"publish","type":"post","link":"https:\/\/aif.amtbbs.org\/index.php\/2024\/01\/17\/17660\/","title":{"rendered":"\u673a\u5668\u4eba\u9886\u57df\u9996\u4e2a\u5f00\u6e90\u89c6\u89c9-\u8bed\u8a00\u64cd\u4f5c\u5927\u6a21\u578b\uff0cRoboFlamingo\u6846\u67b6\u6fc0\u53d1\u5f00\u6e90VLMs\u66f4\u5927\u6f5c\u80fd"},"content":{"rendered":"
\u8fd8\u5728\u82e6\u82e6\u5bfb\u627e\u5f00\u6e90\u7684\u673a\u5668\u4eba\u5927\u6a21\u578b\uff1f\u8bd5\u8bd5RoboFlamingo\uff01<\/div>\n
\n
\n

\u8fd1\u5e74\u6765\uff0c\u5927\u6a21\u578b\u7684\u7814\u7a76\u6b63\u5728\u52a0\u901f\u63a8\u8fdb\uff0c\u5b83\u9010\u6e10\u5728\u5404\u7c7b\u4efb\u52a1\u4e0a\u5c55\u73b0\u51fa\u591a\u6a21\u6001\u7684\u7406\u89e3\u548c\u65f6\u95f4\u7a7a\u95f4\u4e0a\u7684\u63a8\u7406\u80fd\u529b\u3002\u673a\u5668\u4eba\u7684\u5404\u7c7b\u5177\u8eab\u64cd\u4f5c\u4efb\u52a1\u5929\u7136\u5c31\u5bf9\u8bed\u8a00\u6307\u4ee4\u7406\u89e3\u3001\u573a\u666f\u611f\u77e5\u548c\u65f6\u7a7a\u89c4\u5212\u7b49\u80fd\u529b\u6709\u7740\u5f88\u9ad8\u7684\u8981\u6c42\uff0c\u8fd9\u81ea\u7136\u5f15\u7533\u51fa\u4e00\u4e2a\u95ee\u9898\uff1a\u80fd\u4e0d\u80fd\u5145\u5206\u5229\u7528\u5927\u6a21\u578b\u80fd\u529b\uff0c\u5c06\u5176\u8fc1\u79fb\u5230\u673a\u5668\u4eba\u9886\u57df\uff0c\u76f4\u63a5\u89c4\u5212\u5e95\u5c42\u52a8\u4f5c\u5e8f\u5217\u5462\uff1f<\/p>\n

\u5bf9\u6b64\uff0cByteDance Research \u57fa\u4e8e\u5f00\u6e90\u7684\u591a\u6a21\u6001\u8bed\u8a00\u89c6\u89c9\u5927\u6a21\u578b OpenFlamingo \u5f00\u53d1\u4e86\u5f00\u6e90\u3001\u6613\u7528\u7684 RoboFlamingo \u673a\u5668\u4eba\u64cd\u4f5c\u6a21\u578b\uff0c\u53ea\u7528\u5355\u673a\u5c31\u53ef\u4ee5\u8bad\u7ec3\u3002\u4f7f\u7528\u7b80\u5355\u3001\u5c11\u91cf\u7684\u5fae\u8c03\u5c31\u53ef\u4ee5\u628a VLM \u53d8\u6210 Robotics VLM\uff0c\u4ece\u800c\u9002\u7528\u4e8e\u8bed\u8a00\u4ea4\u4e92\u7684\u673a\u5668\u4eba\u64cd\u4f5c\u4efb\u52a1\u3002<\/p>\n

OpenFlamingo \u5728\u673a\u5668\u4eba\u64cd\u4f5c\u6570\u636e\u96c6 CALVIN \u4e0a\u8fdb\u884c\u4e86\u9a8c\u8bc1\uff0c\u5b9e\u9a8c\u7ed3\u679c\u8868\u660e\uff0cRoboFlamingo \u53ea\u5229\u7528\u4e86 1% \u7684\u5e26\u8bed\u8a00\u6807\u6ce8\u7684\u6570\u636e\u5373\u5728\u4e00\u7cfb\u5217\u673a\u5668\u4eba\u64cd\u4f5c\u4efb\u52a1\u4e0a\u53d6\u5f97\u4e86 SOTA \u7684\u6027\u80fd\u3002\u968f\u7740 RT-X \u6570\u636e\u96c6\u5f00\u653e\uff0c\u91c7\u7528\u5f00\u6e90\u6570\u636e\u9884\u8bad\u7ec3 RoboFlamingo \u5e76 finetune \u5230\u4e0d\u540c\u673a\u5668\u4eba\u5e73\u53f0\uff0c\u5c06\u6709\u5e0c\u671b\u6210\u4e3a\u4e00\u4e2a\u7b80\u5355\u6709\u6548\u7684\u673a\u5668\u4eba\u5927\u6a21\u578b pipeline\u3002\u8bba\u6587\u8fd8\u6d4b\u8bd5\u4e86\u5404\u79cd\u4e0d\u540c policy head\u3001\u4e0d\u540c\u8bad\u7ec3\u8303\u5f0f\u548c\u4e0d\u540c Flamingo \u7ed3\u6784\u7684 VLM \u5728 Robotics \u4efb\u52a1\u4e0a\u5fae\u8c03\u7684\u8868\u73b0\uff0c\u5f97\u5230\u4e86\u4e00\u4e9b\u6709\u610f\u601d\u7684\u7ed3\u8bba\u3002<\/p>\n

\"\u56fe\u7247\"<\/p>\n

    \n
  • \u9879\u76ee\u4e3b\u9875\uff1ahttps:\/\/roboflamingo.github.io<\/li>\n
  • \u4ee3\u7801\u5730\u5740\uff1ahttps:\/\/github.com\/RoboFlamingo\/RoboFlamingo<\/li>\n
  • \u8bba\u6587\u5730\u5740\uff1ahttps:\/\/arxiv.org\/abs\/2311.01378<\/li>\n<\/ul>\n

    \u7814\u7a76\u80cc\u666f<\/h4>\n

    \"\u56fe\u7247\"<\/p>\n

    \u57fa\u4e8e\u8bed\u8a00\u7684\u673a\u5668\u4eba\u64cd\u4f5c\u662f\u5177\u8eab\u667a\u80fd\u9886\u57df\u7684\u4e00\u4e2a\u91cd\u8981\u5e94\u7528\uff0c\u5b83\u6d89\u53ca\u5230\u591a\u6a21\u6001\u6570\u636e\u7684\u7406\u89e3\u548c\u5904\u7406\uff0c\u5305\u62ec\u89c6\u89c9\u3001\u8bed\u8a00\u548c\u63a7\u5236\u7b49\u3002\u8fd1\u5e74\u6765\uff0c\u89c6\u89c9\u8bed\u8a00\u57fa\u7840\u6a21\u578b\uff08VLMs\uff09\u5df2\u7ecf\u5728\u591a\u4e2a\u9886\u57df\u53d6\u5f97\u4e86\u663e\u8457\u7684\u8fdb\u5c55\uff0c\u5305\u62ec\u56fe\u50cf\u63cf\u8ff0\u3001\u89c6\u89c9\u95ee\u7b54\u548c\u56fe\u50cf\u751f\u6210\u7b49\u3002\u7136\u800c\uff0c\u5c06\u8fd9\u4e9b\u6a21\u578b\u5e94\u7528\u4e8e\u673a\u5668\u4eba\u64cd\u4f5c\u4ecd\u7136\u5b58\u5728\u4e00\u4e9b\u6311\u6218\uff0c\u4f8b\u5982\u5982\u4f55\u5c06\u89c6\u89c9\u548c\u8bed\u8a00\u4fe1\u606f\u7ed3\u5408\u8d77\u6765\uff0c\u5982\u4f55\u5904\u7406\u673a\u5668\u4eba\u64cd\u4f5c\u7684\u65f6\u5e8f\u6027\u7b49\u3002<\/p>\n

    \u4e3a\u4e86\u89e3\u51b3\u8fd9\u4e9b\u95ee\u9898\uff0cByteDance Research \u7684\u673a\u5668\u4eba\u7814\u7a76\u56e2\u961f\u5229\u7528\u73b0\u6709\u7684\u5f00\u6e90 VLM\uff0cOpenFlamingo\uff0c\u8bbe\u8ba1\u4e86\u4e00\u5957\u65b0\u7684\u89c6\u89c9\u8bed\u8a00\u64cd\u4f5c\u6846\u67b6\uff0cRoboFlamingo\u3002\u5176\u4e2d VLM \u53ef\u4ee5\u8fdb\u884c\u5355\u6b65\u89c6\u89c9\u8bed\u8a00\u7406\u89e3\uff0c\u800c\u989d\u5916\u7684 policy head \u6a21\u7ec4\u88ab\u7528\u6765\u5904\u7406\u5386\u53f2\u4fe1\u606f\u3002\u53ea\u9700\u8981\u7b80\u5355\u7684\u5fae\u8c03\u65b9\u6cd5\u5c31\u80fd\u8ba9 RoboFlamingo \u9002\u5e94\u4e8e\u57fa\u4e8e\u8bed\u8a00\u7684\u673a\u5668\u4eba\u64cd\u4f5c\u4efb\u52a1\u3002<\/p>\n

    RoboFlamingo \u5728\u57fa\u4e8e\u8bed\u8a00\u7684\u673a\u5668\u4eba\u64cd\u4f5c\u6570\u636e\u96c6 CALVIN \u4e0a\u8fdb\u884c\u4e86\u9a8c\u8bc1\uff0c\u5b9e\u9a8c\u7ed3\u679c\u8868\u660e\uff0cRoboFlamingo \u53ea\u5229\u7528\u4e86 1% \u7684\u5e26\u8bed\u8a00\u6807\u6ce8\u7684\u6570\u636e\u5373\u5728\u4e00\u7cfb\u5217\u673a\u5668\u4eba\u64cd\u4f5c\u4efb\u52a1\u4e0a\u53d6\u5f97\u4e86 SOTA \u7684\u6027\u80fd\uff08\u591a\u4efb\u52a1\u5b66\u4e60\u7684 task sequence \u6210\u529f\u7387\u4e3a 66%\uff0c\u5e73\u5747\u4efb\u52a1\u5b8c\u6210\u6570\u91cf\u4e3a 4.09\uff0c\u57fa\u7ebf\u65b9\u6cd5\u4e3a 38%\uff0c\u5e73\u5747\u4efb\u52a1\u5b8c\u6210\u6570\u91cf\u4e3a 3.06\uff1bzero-shot \u4efb\u52a1\u7684\u6210\u529f\u7387\u4e3a 24%\uff0c\u5e73\u5747\u4efb\u52a1\u5b8c\u6210\u6570\u91cf\u4e3a 2.48\uff0c\u57fa\u7ebf\u65b9\u6cd5\u4e3a 1%\uff0c\u5e73\u5747\u4efb\u52a1\u5b8c\u6210\u6570\u91cf\u662f 0.67\uff09\uff0c\u5e76\u4e14\u80fd\u591f\u901a\u8fc7\u5f00\u73af\u63a7\u5236\u5b9e\u73b0\u5b9e\u65f6\u54cd\u5e94\uff0c\u53ef\u4ee5\u7075\u6d3b\u90e8\u7f72\u5728\u8f83\u4f4e\u6027\u80fd\u7684\u5e73\u53f0\u4e0a\u3002\u8fd9\u4e9b\u7ed3\u679c\u8868\u660e\uff0cRoboFlamingo \u662f\u4e00\u79cd\u6709\u6548\u7684\u673a\u5668\u4eba\u64cd\u4f5c\u65b9\u6cd5\uff0c\u53ef\u4ee5\u4e3a\u672a\u6765\u7684\u673a\u5668\u4eba\u5e94\u7528\u63d0\u4f9b\u6709\u7528\u7684\u53c2\u8003\u3002<\/p>\n

    \u65b9\u6cd5<\/h4>\n

    \"\u56fe\u7247\"<\/p>\n

    \u672c\u5de5\u4f5c\u5229\u7528\u5df2\u6709\u7684\u57fa\u4e8e\u56fe\u50cf – \u6587\u672c\u5bf9\u7684\u89c6\u89c9\u8bed\u8a00\u57fa\u7840\u6a21\u578b\uff0c\u901a\u8fc7\u8bad\u7ec3\u7aef\u5230\u7aef\u7684\u65b9\u5f0f\u751f\u6210\u673a\u5668\u4eba\u6bcf\u4e00\u6b65\u7684 relative action\u3002\u6a21\u578b\u7684\u4e3b\u8981\u6a21\u5757\u5305\u542b\u4e86 vision encoder\uff0cfeature fusion decoder \u548c policy head \u4e09\u4e2a\u6a21\u5757\u3002Vision encoder \u6a21\u5757\u5148\u5c06\u5f53\u524d\u89c6\u89c9\u89c2\u6d4b\u8f93\u5165\u5230 ViT \u4e2d\uff0c\u5e76\u901a\u8fc7 resampler \u5bf9 ViT \u8f93\u51fa\u7684 token \u8fdb\u884c down sample\u3002Feature fusion decoder \u5c06 text token \u4f5c\u4e3a\u8f93\u5165\uff0c\u5e76\u5728\u6bcf\u4e2a layer \u4e2d\u5148\u5c06 vision encoder \u7684 output \u4f5c\u4e3a query \u8fdb\u884c cross attention\uff0c\u4e4b\u540e\u8fdb\u884c self attention \u4ee5\u5b8c\u6210\u89c6\u89c9\u4e0e\u8bed\u8a00\u7279\u5f81\u7684\u878d\u5408\u3002\u6700\u540e\uff0c\u5bf9 feature fusion decoder \u8fdb\u884c max pooling \u540e\u5c06\u5176\u9001\u5165 policy head \u4e2d\uff0cpolicy head \u6839\u636e feature fusion decoder \u8f93\u51fa\u7684\u5f53\u524d\u548c\u5386\u53f2 token \u5e8f\u5217\u76f4\u63a5\u8f93\u51fa\u5f53\u524d\u7684 7 DoF relative action\uff0c\u5305\u62ec\u4e86 6-dim \u7684\u673a\u68b0\u81c2\u672b\u7aef\u4f4d\u59ff\u548c 1-dim \u7684 gripper open\/close\u3002<\/p>\n

    \u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\uff0cRoboFlamingo \u5229\u7528\u9884\u8bad\u7ec3\u7684 ViT\u3001LLM \u548c Cross Attention \u53c2\u6570\uff0c\u5e76\u53ea\u5fae\u8c03 resampler\u3001cross attention \u548c policy head \u7684\u53c2\u6570\u3002<\/p>\n

    \u5b9e\u9a8c\u7ed3\u679c<\/h4>\n

    \u6570\u636e\u96c6\uff1a<\/p>\n

    \"\u56fe\u7247\"<\/p>\n

    CALVIN\uff08Composing Actions from Language and Vision\uff09\u662f\u4e00\u4e2a\u5f00\u6e90\u7684\u6a21\u62df\u57fa\u51c6\u6d4b\u8bd5\uff0c\u7528\u4e8e\u5b66\u4e60\u57fa\u4e8e\u8bed\u8a00\u7684 long-horizon \u64cd\u4f5c\u4efb\u52a1\u3002\u4e0e\u73b0\u6709\u7684\u89c6\u89c9 – \u8bed\u8a00\u4efb\u52a1\u6570\u636e\u96c6\u76f8\u6bd4\uff0cCALVIN \u7684\u4efb\u52a1\u5728\u5e8f\u5217\u957f\u5ea6\u3001\u52a8\u4f5c\u7a7a\u95f4\u548c\u8bed\u8a00\u4e0a\u90fd\u66f4\u4e3a\u590d\u6742\uff0c\u5e76\u652f\u6301\u7075\u6d3b\u5730\u6307\u5b9a\u4f20\u611f\u5668\u8f93\u5165\u3002CALVIN \u5206\u4e3a ABCD \u56db\u4e2a split\uff0c\u6bcf\u4e2a split \u5bf9\u5e94\u4e86\u4e0d\u540c\u7684 context \u548c layout\u3002<\/p>\n

    \u5b9a\u91cf\u5206\u6790\uff1a<\/p>\n

    \"\u56fe\u7247\"<\/p>\n

    RoboFlamingo \u5728\u5404\u8bbe\u7f6e\u548c\u6307\u6807\u4e0a\u7684\u6027\u80fd\u5747\u4e3a\u6700\u4f73\uff0c\u8bf4\u660e\u4e86\u5176\u5177\u6709\u5f88\u5f3a\u7684\u6a21\u4eff\u80fd\u529b\u3001\u89c6\u89c9\u6cdb\u5316\u80fd\u529b\u4ee5\u53ca\u8bed\u8a00\u6cdb\u5316\u80fd\u529b\u3002Full \u548c Lang \u8868\u793a\u6a21\u578b\u662f\u5426\u4f7f\u7528\u672a\u914d\u5bf9\u7684\u89c6\u89c9\u6570\u636e\u8fdb\u884c\u8bad\u7ec3\uff08\u5373\u6ca1\u6709\u8bed\u8a00\u914d\u5bf9\u7684\u89c6\u89c9\u6570\u636e\uff09\uff1bFreeze-emb \u6307\u7684\u662f\u51bb\u7ed3\u878d\u5408\u89e3\u7801\u5668\u7684\u5d4c\u5165\u5c42\uff1bEnriched \u8868\u793a\u4f7f\u7528 GPT-4 \u589e\u5f3a\u7684\u6307\u4ee4\u3002<\/p>\n

    \u6d88\u878d\u5b9e\u9a8c\uff1a<\/p>\n

    \"\u56fe\u7247\"<\/p>\n

    \u4e0d\u540c\u7684 policy head\uff1a<\/p>\n

    \u5b9e\u9a8c\u8003\u5bdf\u4e86\u56db\u79cd\u4e0d\u540c\u7684\u7b56\u7565\u5934\u90e8\uff1aMLP w\/o hist\u3001MLP w hist\u3001GPT \u548c LSTM\u3002\u5176\u4e2d\uff0cMLP w\/o hist \u76f4\u63a5\u6839\u636e\u5f53\u524d\u89c2\u6d4b\u9884\u6d4b\u5386\u53f2\uff0c\u5176\u6027\u80fd\u6700\u5dee\uff0cMLP w hist \u5c06\u5386\u53f2\u89c2\u6d4b\u5728 vision encoder \u7aef\u8fdb\u884c\u878d\u5408\u540e\u9884\u6d4b action\uff0c\u6027\u80fd\u6709\u6240\u63d0\u5347\uff1bGPT \u548c LSTM \u5728 policy head \u5904\u5206\u522b\u663e\u5f0f\u3001\u9690\u5f0f\u5730\u7ef4\u62a4\u5386\u53f2\u4fe1\u606f\uff0c\u5176\u8868\u73b0\u6700\u597d\uff0c\u8bf4\u660e\u4e86\u901a\u8fc7 policy head \u8fdb\u884c\u5386\u53f2\u4fe1\u606f\u878d\u5408\u7684\u6709\u6548\u6027\u3002<\/p>\n

    \u89c6\u89c9-\u8bed\u8a00\u9884\u8bad\u7ec3\u7684\u5f71\u54cd\uff1a<\/p>\n

    \u9884\u8bad\u7ec3\u5bf9\u4e8e RoboFlamingo \u7684\u6027\u80fd\u63d0\u5347\u8d77\u5230\u4e86\u5173\u952e\u4f5c\u7528\u3002\u5b9e\u9a8c\u663e\u793a\uff0c\u901a\u8fc7\u9884\u5148\u5728\u5927\u578b\u89c6\u89c9-\u8bed\u8a00\u6570\u636e\u96c6\u4e0a\u8fdb\u884c\u8bad\u7ec3\uff0cRoboFlamingo \u5728\u673a\u5668\u4eba\u4efb\u52a1\u4e2d\u8868\u73b0\u5f97\u66f4\u597d\u3002<\/p>\n

    \u6a21\u578b\u5927\u5c0f\u4e0e\u6027\u80fd\uff1a<\/p>\n

    \u867d\u7136\u901a\u5e38\u66f4\u5927\u7684\u6a21\u578b\u4f1a\u5e26\u6765\u66f4\u597d\u7684\u6027\u80fd\uff0c\u4f46\u5b9e\u9a8c\u7ed3\u679c\u8868\u660e\uff0c\u5373\u4f7f\u662f\u8f83\u5c0f\u7684\u6a21\u578b\uff0c\u4e5f\u80fd\u5728\u67d0\u4e9b\u4efb\u52a1\u4e0a\u4e0e\u5927\u578b\u6a21\u578b\u5ab2\u7f8e\u3002<\/p>\n

    \u6307\u4ee4\u5fae\u8c03\u7684\u5f71\u54cd\uff1a<\/p>\n

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