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\u6b64\u5916\uff0c\u5728\u690d\u7269\u6362\u76c6\u4efb\u52a1\u4e2d\uff0c\u4ece\u5f00\u59cb\u5f55\u6f14\u793a\uff0c\u5230\u673a\u5668\u4eba\u81ea\u5df1\u4e0a\u624b\uff0c\u53ea\u82b1\u4e8611\u5206\u949f\uff0c\u76f4\u63a5\u5728\u6548\u7387\u8fd9\u4e00\u5757\uff0c\u7ed9\u4f20\u7edf\u540e\u8bad\u7ec3\u79d2\u4e86\u3002<\/p>\n

\u6700\u540e\uff0c\u5728\u6574\u4e2a\u8fc7\u7a0b\u4e2d\uff0cS1\u6ca1\u6709\u7528fine-tuning\uff0c\u4e5f\u6ca1\u4e0apost-training\uff0c\u6a21\u578b\u6743\u91cd\u5b8c\u5168\u4e0d\u53d8\uff0c\u7528\u540c\u4e00\u5957\u6743\u91cd\uff0c\u5c31\u5b8c\u6210\u4e86\u535a\u5ba2\u91cc\u5c55\u793a\u7684\u6240\u6709\u4efb\u52a1\u3002<\/p>\n

\u57fa\u672c\u5bf9\u9f50\u4e86\u5927\u6a21\u578b\u4eceBert\u9700\u8981\u7279\u5b9a\u573a\u666f\u5fae\u8c03\uff0c\u5230GPT-3\u53ea\u770b\u6f14\u793a\u5c31\u80fd\u5b8c\u6210OOD\u4efb\u52a1\u7684\u8de8\u8d8a\u3002<\/p>\n

\u552f\u4e00\u7684\u4e0d\u540c\u5c31\u662f\uff0c\u7528\u7684prompt\u4e0d\u518d\u662f\u8bed\u8a00\uff0c\u800c\u662f\u7528\u4e86\u80fd\u591f\u66f4\u597d\u5bf9\u9f50\u4e0b\u6e38\u4efb\u52a1\u7684\u52a8\u4f5c\u89c6\u9891\u3002<\/p>\n

\u5728\u5b9e\u9a8c\u90e8\u5206\uff0cSkild\u5148\u5728\uff08\u8bad\u7ec3\u6570\u636e\uff09\u89c1\u8fc7\u7684\u4efb\u52a1\u548c\u672a\u89c1\u8fc7\u7684\u4efb\u52a1\u4e0a\uff0c\u5bf9\u6bd4\u4e86ICL\u89c6\u9891Prompt\u548c\u4f20\u7edf\u7684\u8bed\u8a00Prompt VLA\u3002<\/p>\n

\u6d4b\u8bd5\u7ed3\u679c\u8868\u660e\uff0c\u5f53\u8bad\u7ec3\u6570\u636e\u53ea\u67091000\u5c0f\u65f6\u65f6\uff0c\u8bed\u8a00Prompt\u6548\u679c\u66f4\u597d\uff0c\u800c\u968f\u7740\u6570\u636e\u89c4\u6a21\u589e\u52a0\uff0cICL\u5f00\u59cb\u53cd\u8d85\u3002<\/p>\n

\u5230\u4e8610\u4e07\u5c0f\u65f6\uff0c\u8bad\u7ec3\u65f6\u89c1\u8fc7\u7684\u4efb\u52a1\u4e0a\uff1aICL 96%\uff0c\u8bed\u8a00Prompt 89%\u3002<\/p>\n

\u800c\u5728\u771f\u6b63\u5173\u952e\u7684OOD\u4efb\u52a1\u4e0a\uff1aICL 66%\uff0c\u8bed\u8a00Prompt\u53ea\u67099%\uff0c\u76f4\u63a5\u62c9\u5f00\u4e867\u500d\u4ee5\u4e0a\u7684\u5dee\u8ddd\u3002<\/p>\n

\u4e3a\u4e86\u9a8c\u8bc1S1\u7684\u6cdb\u5316\u6027\uff0cSkild\u53c8\u5728\u4e0d\u540c\u7684\u5b9e\u9a8c\u6761\u4ef6\u4e0b\u8fdb\u884c\u6d4b\u8bd5\u3002<\/p>\n

\u7ed3\u679c\u8868\u660e\uff0c\u8bed\u8a00Prompt VLA\u7684\u6027\u80fd\u4e0b\u964d\u5e45\u5ea6\uff0c\u6700\u9ad8\u8fbe\u5230ICL\u76843\u500d\u3002<\/p>\n

\u4e5f\u5c31\u662f\u8bf4\uff0cICL\u5b66\u5230\u7684\u4e0d\u662f\u56fa\u5b9a\u573a\u666f\u91cc\u7684\u52a8\u4f5c\u590d\u8bfb\uff0c\u800c\u662f\u66f4\u80fd\u8ddf\u7740\u5f53\u524d\u73af\u5883\u91cd\u65b0\u89c4\u5212\u600e\u4e48\u505a\u3002<\/p>\n

\u6700\u540e\uff0c\u5728One shot learning\u65b9\u9762\u3002<\/p>\n

S1\u5728\u65b0\u4efb\u52a1\u4e0a\u5b8c\u5168\u4e0d\u505apost-training\uff0c\u53ea\u770b\u4e00\u6761\u89c6\u9891\u6f14\u793a\uff0c\u6210\u529f\u7387\u5c31\u8fbe\u523066%\u3002<\/p>\n

\u76f8\u8f83\u4e4b\u4e0b\uff0c\u4f20\u7edfVLA\u5219\u5927\u6982\u8981\u7ecf\u8fc7\u7ea6380\u6b21\u6f14\u793a\u7684\u540e\u8bad\u7ec3\uff0c\u624d\u80fd\u8ffd\u5230\u540c\u6837\u6c34\u5e73\u3002<\/p>\n

\u5f53\u7136\uff0c\u7ee7\u7eed\u5806\u52302000\u6b21\u6f14\u793a\u540e\uff0c\u4f20\u7edfpost-training\u6700\u7ec8\u80fd\u505a\u523086%\u3002<\/p>\n

\u6240\u4ee5\u7ed3\u8bba\u53ef\u80fd\u4e0d\u662f\u201c\u4ee5\u540e\u673a\u5668\u4eba\u4e0d\u7528\u8bad\u7ec3\u4e86\u201d\uff0c\u800c\u662f\uff1a<\/p>\n

\u4ee5\u524d\u4e00\u4e2a\u65b0\u6280\u80fd\u53ef\u80fd\u5f97\u6559\u51e0\u767e\u904d\uff0c\u73b0\u5728\u5148\u770b\u4e00\u904d\uff0c\u5c31\u80fd\u76f4\u63a5\u505a\u5230\u516d\u4e03\u5341\u5206\u3002<\/p>\n

\u8fd9\u4e5f\u662fSkild\u6240\u8c13\u7684ICL scaling law\uff1a<\/p>\n

\u6570\u636e\u8d8a\u591a\uff0c\u6a21\u578b\u4e0d\u53ea\u662f\u4f1a\u66f4\u591a\u6280\u80fd\uff0c\u800c\u662f\u8d8a\u6765\u8d8a\u4f1a\u201c\u4ece\u6f14\u793a\u91cc\u5b66\u6280\u80fd\u201d\u3002<\/p>\n

Skild\u6210\u7acb\u4e8e2023\u5e74\uff0c\u80cc\u540e\u7ad9\u7740\u4e24\u4e2aCMU\u673a\u5668\u4eba\u5708\u7684\u8001\u719f\u4eba\uff1aDeepak Pathak\u548cAbhinav Gupta\u3002<\/p>\n

\u4e24\u4eba\u90fd\u662f\u5361\u5185\u57fa\u6885\u9686\u5927\u5b66\u673a\u5668\u4eba\u7814\u7a76\u6240\u7684\u6559\u6388\uff0cDeepak\u4e3b\u8981\u7814\u7a76\u673a\u5668\u4eba\u5b66\u4e60\u3001\u5f3a\u5316\u5b66\u4e60\u548c\u8ba1\u7b97\u673a\u89c6\u89c9\uff0cAbhinav\u5219\u662f\u8ba1\u7b97\u673a\u89c6\u89c9\u3001\u81ea\u76d1\u7763\u5b66\u4e60\u9886\u57df\u7684\u5927\u795e\u3002<\/p>\n

\u65e9\u57282022\u5e74\uff0c\u4e24\u4eba\u5c31\u5408\u4f5c\u8fc7WHIRL\uff0c\u8ba9\u673a\u5668\u4eba\u901a\u8fc7\u89c2\u770b\u4eba\u7c7b\u89c6\u9891\u8fdb\u884cone-shot imitation\uff0c\u53ef\u4ee5\u8bf4S1\u8fd9\u6761\u8def\u7ebf\uff0c\u4e5f\u4e0d\u662f\u7a81\u7136\u4ece\u77f3\u5934\u7f1d\u91cc\u8e66\u51fa\u6765\u7684\u3002<\/p>\n

\u4f5c\u4e3a\u5317\u7f8e\u5177\u8eab\u5708\u7684\u660e\u661f\u4f01\u4e1a\uff0c2024\u5e74Skild\u521a\u8d70\u51fa\u9690\u8eab\u6a21\u5f0f\uff0c\u5c31\u62ff\u4e0b3\u4ebf\u7f8e\u5143A\u8f6e\u3001\u4f30\u503c15\u4ebf\u7f8e\u5143\uff1b<\/p>\n

\u5230\u4e86\u4eca\u5e741\u6708\uff0c\u53c8\u5b8c\u621014\u4ebf\u7f8e\u5143C\u8f6e\u878d\u8d44\uff0c\u4f30\u503c\u76f4\u63a5\u5e72\u5230\u4e86140\u4ebf\u7f8e\u5143\u4ee5\u4e0a\uff0cSoftBank\u9886\u6295\uff0c\u82f1\u4f1f\u8fbe\u3001\u8d1d\u7d22\u65af\u7b49\u7ee7\u7eed\u4e0a\u684c\u3002\u4e24\u5e74\u591a\u65f6\u95f4\uff0c\u4f30\u503c\u63a5\u8fd1\u7ffb\u4e8610\u500d\u3002<\/p>\n

\u6a2a\u5411\u5bf9\u6bd4\u6765\u770b\uff0cSkild\u5728\u5317\u7f8e\u5177\u8eab\u5708\u7684\u5b9a\u4f4d\u4e5f\u76f8\u5f53\u7279\u6b8a\u3002<\/p>\n

\u76f8\u6bd4\u4e8ePI\u66f4\u50cf\u662f\u5728\u641e\u201c\u673a\u5668\u4ebaGPT\u201d\uff0cGeneralist\u6700\u8fd1\u5f3a\u8c03\u7684\u662fone-shot learner\uff0c\u4ee5\u53caGenesis AI\u3001Sunday\u6700\u8fd1\u7684\u5168\u6808\u52bf\u5934\uff0cSkild\u4e00\u76f4\u5f3a\u8c03\u7684\u662f\u4e00\u53e5\u8bdd\uff1aAny robot,any task, one brain\u3002<\/p>\n

\u5b83\u66f4\u5f3a\u8c03\u8de8embodiment\uff0c\u5e0c\u671b\u540c\u4e00\u4e2aSkild Brain\u80fd\u591f\u8dd1\u5728\u673a\u68b0\u81c2\u3001\u56db\u8db3\u3001\u4eba\u5f62\u7b49\u4e0d\u540c\u8eab\u4f53\u4e0a\u3002<\/p>\n

\u8bf4\u7684\u901a\u4fd7\u4e00\u70b9\uff0c\u5c31\u662f\u60f3\u6210\u4e3a\u673a\u5668\u4eba\u65f6\u4ee3\u7684\u5b89\u5353\uff1a\u4e00\u4e2a\u667a\u80fd\u5c42\uff0c\u585e\u8fdb\u5404\u79cd\u4e0d\u540c\u7684\u8eab\u4f53\u91cc\u3002<\/p>\n

\u8fd9\u4e5f\u7531\u6b64\u51b3\u5b9a\u4e86Skild\u7684\u6570\u636e\u7b56\u7565\uff1a\u5168\u90fd\u8981\u3002<\/p>\n

Skild\u628a\u673a\u5668\u4eba\u6570\u636e\u770b\u6210hardware proximity\u3001diversity\u548cscalability\u4e09\u4e2a\u7ef4\u5ea6\uff1a<\/p>\n

\u771f\u673a\u9065\u64cd\u6700\u63a5\u8fd1\u786c\u4ef6\uff0c\u4f46\u8d35\uff1b\u7b2c\u4e00\u89c6\u89d2\u4eba\u7c7b\u89c6\u9891\u6700\u5bb9\u6613\u89c4\u6a21\u5316\uff0c\u4f46\u548c\u673a\u5668\u4eba\u8eab\u4f53\u5dee\u5f97\u8fdc\uff1b\u4eff\u771f\u4fbf\u5b9c\u80fd\u731b\u9020\uff0c\u5374\u53c8\u6709sim-to-real gap\u3002<\/p>\n

\u6240\u4ee5\u4ed6\u4eec\u5bf9\u4e8eRobot Teleop\u3001UMI\u3001Egocentric Video\u3001Simulation\uff0c\u57fa\u672c\u4e0a\u91c7\u53d6\u7684\u5c31\u662f\u90fd\u7528\u7684\u7b56\u7565\u3002<\/p>\n

\u800cS1\u7684ICL\uff0c\u5176\u5b9e\u4e5f\u662f\u8fd9\u6761\u8def\u7ebf\u81ea\u7136\u5ef6\u4f38\uff1a<\/p>\n

2025\u5e749\u6708LocoFormer \u2192 2026\u5e742\u6708\u9996\u6b21In-Domain ICL \u2192 5\u6708\u7b2c\u4e00\u6b21\u7ffb\u714e\u997c \u2192 8\u6708S1\u53d1\u5e03\uff0c\u76f4\u63a5\u628a\u4e0a\u4e0b\u6587\u5b66\u4e60\u63a8\u5230\u6700\u957f10\u5206\u949f\u7684OOD\u957f\u7a0b\u4efb\u52a1\u3002<\/p>\n

\u6240\u4ee5\u73b0\u5728\u771f\u6b63\u503c\u5f97\u5173\u6ce8\u7684\u95ee\u9898\uff0c\u53ef\u80fd\u5df2\u7ecf\u4e0d\u662f\u673a\u5668\u4eba\u8fd8\u80fd\u4e0d\u80fd\u5b66\u4f1a\u66f4\u591a\u6280\u80fd\uff0c\u800c\u662f\uff1a<\/p>\n

\u673a\u5668\u4eba\u5b66\u4e60\u4e00\u4e2a\u65b0\u6280\u80fd\u7684\u6210\u672c\uff0c\u80fd\u4e0d\u80fd\u771f\u7684\u4ece\u201c\u6559\u51e0\u767e\u904d\u201d\uff0c\u53d8\u6210\u201c\u4f60\u505a\u4e00\u904d\uff0c\u5b83\u770b\u4e00\u904d\u201d\u3002<\/p>\n

\u5982\u679c\u8fd9\u4e2ascaling law\u8fd8\u80fd\u7ee7\u7eed\u6210\u7acb\uff0c\u90a3\u6240\u8c13\u673a\u5668\u4eba\u7684GPT moment\uff0c\u53ef\u80fd\u771f\u7684\u4e0d\u53ea\u662f\u53c8\u4e00\u4e2a\u8425\u9500\u6897\u4e86\u3002<\/p>\n

\u53c2\u8003\u94fe\u63a5[1]https:\/\/www.skild.ai\/blogs\/s1<\/p>\n

\n

\u7248\u6743\u58f0\u660e\uff1a<\/strong><\/p>\n

\u672c\u535a\u5ba2\u90e8\u5206\u5185\u5bb9\u4e3a\u8f6c\u8f7d\u6587\u7ae0\uff0c\u65e8\u5728\u5206\u4eab\u6709\u4ef7\u503c\u7684\u4fe1\u606f\uff0c\u7248\u6743\u5f52\u539f\u4f5c\u8005\u6240\u6709\u3002<\/p>\n

\u8f6c\u8f7d\u4ec5\u4e3a\u4e2a\u4eba\u5b66\u4e60\u4e0e\u4ea4\u6d41\u76ee\u7684\uff0c\u4e0d\u5bf9\u6587\u7ae0\u89c2\u70b9\u8d1f\u8d23\uff0c\u4ea6\u4e0d\u7528\u4e8e\u4efb\u4f55\u5546\u4e1a\u7528\u9014\u3002<\/p>\n

\u5982\u6d89\u53ca\u7248\u6743\u95ee\u9898\uff0c\u8bf7\u8054\u7cfb\u672c\u4eba\u5220\u9664\u3002<\/p>\n<\/blockquote>\n","protected":false},"excerpt":{"rendered":"

\u5177\u8eab\u667a\u80fd\u8fc8\u5411GPT\u65f6\u523b<\/p>\n

\u5177\u8eab\u667a\u80fd\u7684\u5927\u7ed3\u679c\uff0c\u8981\u6765\u4e86\uff01\uff01\uff01<\/p>\n

\u81ea\u4e0a\u5468Generalist\u53d1\u5e03\u80fd\u591f\u5b66\u4e603\u523012\u79d2\u52a8\u4f5c\u7684\u5177\u8eab\u5927\u8111Gen 1.5 \u4ee5\u6765\uff5e<\/p>\n

\u521a\u521a\uff0c\u5317\u7f8e\u5177\u8eab\u521d\u521bSkild AI\u53c8\u53d1\u5e03\u4e86\u5168\u65b0\u673a\u5668\u4eba\u57fa\u7840\u6a21\u578bS1\uff0c\u76f4\u63a5\u628a\u673a\u5668\u4eba\u4e0a\u4e0b\u6587\u5b66\u4e60\u7684\u4efb\u52a1\u957f\u5ea6\u62c9\u5230\u4e8610\u5206\u949f\u5f80\u4e0a\u3002<\/p>\n

\u8fd9\u6b21\u7684S1\u4e3b\u6253\u4e0a\u4e0b\u6587\u5b66\u4e60\uff08in-context learning\uff0cICL\uff09\uff1a<\/p>\n

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