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‎app/(default)/(project)/FoldAnything/chart/examplechart.tsx‎

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"use client"
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import { TrendingUp } from "lucide-react"
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import { CartesianGrid, Line, LineChart, XAxis } from "recharts"
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import {
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Card,
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CardContent,
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CardDescription,
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CardFooter,
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CardHeader,
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CardTitle,
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} from "@/components/ui/card"
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import {
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ChartContainer,
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ChartTooltip,
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ChartTooltipContent,
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type ChartConfig,
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} from "@/components/ui/chart"
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export const description = "A multiple line chart"
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const chartData = [
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{ month: "January", desktop: 186, mobile: 80 },
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{ month: "February", desktop: 305, mobile: 200 },
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{ month: "March", desktop: 237, mobile: 120 },
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{ month: "April", desktop: 73, mobile: 190 },
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{ month: "May", desktop: 209, mobile: 130 },
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{ month: "June", desktop: 214, mobile: 140 },
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]
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const chartConfig = {
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desktop: {
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label: "Desktop",
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color: "var(--chart-1)",
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},
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mobile: {
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label: "Mobile",
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color: "var(--chart-2)",
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},
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} satisfies ChartConfig
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export function ExampleChart() {
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return (
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<Card>
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<CardHeader>
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<CardTitle>Line Chart - Multiple</CardTitle>
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<CardDescription>January - June 2024</CardDescription>
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</CardHeader>
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<CardContent>
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<ChartContainer config={chartConfig}>
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<LineChart
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accessibilityLayer
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data={chartData}
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margin={{
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left: 12,
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right: 12,
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}}
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>
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<CartesianGrid vertical={false} />
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<XAxis
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dataKey="month"
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tickLine={false}
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axisLine={false}
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tickMargin={8}
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tickFormatter={(value) => value.slice(0, 3)}
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/>
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<ChartTooltip cursor={false} content={<ChartTooltipContent />} />
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<Line
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dataKey="desktop"
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type="monotone"
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stroke="var(--color-desktop)"
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strokeWidth={2}
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dot={false}
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/>
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<Line
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dataKey="mobile"
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type="monotone"
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stroke="var(--color-mobile)"
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strokeWidth={2}
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dot={false}
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/>
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</LineChart>
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</ChartContainer>
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</CardContent>
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<CardFooter>
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<div className="flex w-full items-start gap-2 text-sm">
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<div className="grid gap-2">
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<div className="flex items-center gap-2 leading-none font-medium">
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Trending up by 5.2% this month <TrendingUp className="h-4 w-4" />
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</div>
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<div className="text-muted-foreground flex items-center gap-2 leading-none">
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Showing total visitors for the last 6 months
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</div>
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</div>
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</div>
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</CardFooter>
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</Card>
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)
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}

‎app/(default)/(project)/FoldAnything/page.tsx‎

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import { ExampleChart } from "./chart/examplechart"
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import { Separator } from "@/components/ui/separator"
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import { AspectRatio } from "@/components/ui/aspect-ratio"
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<h1 className="font-bold text-t1 leading-tight">
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Mastering Garment Manipulation from 0 to 100% in 500,000 rmb/20 Hours
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</h1>
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<i>
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TL;DR: In this blogpost, we investigate an effective pathway to achieve robost manipulation for live-streaming broadcast without heavy resources (compute, data, infra, etc.), just in one day.
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</i>
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<div className="flex flex-row gap-20">
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<div className="flex flex-col">
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<h2>
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<div className="w-full flex flex-col items-center">
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<div className="w-full flex flex-col items-center px-6 mt-20">
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<div className="w-full flex flex-col items-center my-20 px-6">
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<div className="w-full flex flex-col items-center">
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<div className="w-full max-w-4xl flex flex-col relative p-10 gap-6">
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<div className="absolute top-0 left-0 w-8 h-8 border-t-6 border-l-6 border-black"></div>
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<div className="absolute bottom-0 right-0 w-8 h-8 border-b-6 border-r-6 border-black"></div>
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<div className="flex flex-row gap-6 justify-center">
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<div></div>
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<p className="w-full leading-relaxed max-w-4xl">
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Is massive scale the only path to robotic dexterity? While foundational models like Pi05 and GO-1 leverage massive compute and data to achieve task-generalization, we explore a "resource-aware" alternative for state-generalization. We present a comprehensive guideline for mastering long-horizon garment manipulation, taking a system from 0% to 100% reliability with a fraction of the standard cost—specifically, within 20 hours of human demonstration, 8*A100 gpus, rather than tens of thousands of hrs data and hundreds of GPUs which are typically required.
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</p>
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<div></div>
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</div>
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<div className="mt-6 flex flex-row gap-6 justify-center">
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<div></div>
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<p className="w-full leading-relaxed max-w-4xl">
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Our system enables collaborative, dual-arm robots to perform complex folding tasks over extended horizons (up to 6 minutes), demonstrating exceptional robustness to the compounding errors that plague collaborative handovers. This reliability extends to "in-the-wild" scenarios, handling interruptions and varied lighting with ease.
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</p>
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<div></div>
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</div>
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<div className="mt-6 flex flex-row gap-6 justify-center">
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<div></div>
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<p className="w-full leading-relaxed max-w-4xl">
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The core of our efficiency lies in a new philosophical approach to Imitation Learning:
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</p>
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<div></div>
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</div>
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<div className="mt-6 flex flex-row gap-6 justify-center px-6">
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<ol className="space-y-3 w-full leading-relaxed max-w-4xl list-decimal px-4 md:px-6">
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<li>
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<b>
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Mode Consistency:&nbsp;
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</b>
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We argue that not all data is created equal. By strictly aligning the human demonstration distribution ($P_train$), the model's knowledge ($Q_model$), and the test-time distribution ($P_test$), we minimize in-distribution shifts that lead to failure.
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</li>
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<li>
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<b>
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Model Arithmetic:&nbsp;
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</b>
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Rather than relying on a single checkpoint, we identify that models trained on different data subsets learn distinct manifolds. We introduce a "shortcut" to merge these manifolds—condensing several diverse models into a single, adaptive policy without the complexity of MoE architectures.
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</li>
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<li>
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<b>
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Distribution-Aware Orchestration:&nbsp;
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</b>
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A specialized mechanism to handle the "last mile" of precision (the final 80-100% performance gain).
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</li>
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</ol>
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</div>
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<div className="mt-6 flex flex-row gap-6 justify-center">
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<div></div>
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<p className="w-full leading-relaxed max-w-4xl">
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This recipe is universally applicable, functioning across different embodiments (AgileX Piper, ARX X5) and model architectures (pi series, x-vla, go-1), proving that efficient, high-performance manipulation is accessible without industrial-scale resources.
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</p>
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<div></div>
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</div>
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<div className="mt-6 flex flex-row gap-6 justify-center">
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<div></div>
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<p className="w-full leading-relaxed max-w-4xl">
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We will release data, checkpoints and host Challenge in 2026.
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</p>
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<div></div>
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</div>
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<div className="w-full flex flex-col items-center px-6">
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<h2 className="w-full max-w-4xl mt-20 text-3xl font-bold">
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<Link href="#abstract" className="scroll-mt-32 group flex items-center flex-row" id="abstract">
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<div className="mt-6 flex flex-row gap-6 justify-center">
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<div></div>
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<p className="leading-relaxed max-w-4xl">
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文字部分：Turning Stumbles into Strides: Mode Consistency between Data and model. (P_train (human action), Q_model (action projection, weights), P_test (policy action) 三者). ( 0 - 20%, 基建)文字部分：Turning Stumbles into Stridesand model. (P_train (human action), Q_model (action projection, weights), P_test (policy action) 三者). ( 0 - 20%, 基建)文字部分：Turning Stumbles into Strides: Mode Consistency between Data and model. (P_train (human action), Q_model (action projection, weights), P_test (policy action) 三者). ( 0 - 20%, 基建)
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<Link className="text-foreground animated-underline hover:text-mred" href="/" >this is a link</Link><br></br>文字部分：Turning Stumbles into Strides: Mode Consistency between Data and model. (P_train (human action), Q_model (action projection, weights), P_test (policy action) 三者). ( 0 - 20%, 基建)文字部分：Turning Stumbles into Stridesand model. (P_train (human action), Q_model (action projection, weights), P_test (policy action) 三者). ( 0 - 20%, 基建)文字部分：Turning Stumbles into Strides: Mode Consistency between Data and model. (P_train (human action), Q_model (action projection, weights), P_test (policy action) 三者). ( 0 - 20%, 基建)
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<div></div>
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</div>
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</p>
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<div></div>
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</div>
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<div className="mt-6 flex flex-row justify-center">
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<div className="max-w-7xl">
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<ExampleChart />
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</div>
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</div>
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</div>
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);
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}

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