-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathindex.html
293 lines (265 loc) · 22.2 KB
/
index.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
<!DOCTYPE html>
<html class="fontawesome-i2svg-active fontawesome-i2svg-complete"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<meta name="description" content="ManiCM: Real-time 3D Diffusion Policy via Consistency Model for Robotic Manipulation">
<meta name="keywords" content="Robotics, Mobile Manipulation, Consistency Model">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>ManiCM</title>
<style type="text/css">svg:not(:root).svg-inline--fa{overflow:visible}.svg-inline--fa{display:inline-block;font-size:inherit;height:1em;overflow:visible;vertical-align:-.125em}.svg-inline--fa.fa-lg{vertical-align:-.225em}.svg-inline--fa.fa-w-1{width:.0625em}.svg-inline--fa.fa-w-2{width:.125em}.svg-inline--fa.fa-w-3{width:.1875em}.svg-inline--fa.fa-w-4{width:.25em}.svg-inline--fa.fa-w-5{width:.3125em}.svg-inline--fa.fa-w-6{width:.375em}.svg-inline--fa.fa-w-7{width:.4375em}.svg-inline--fa.fa-w-8{width:.5em}.svg-inline--fa.fa-w-9{width:.5625em}.svg-inline--fa.fa-w-10{width:.625em}.svg-inline--fa.fa-w-11{width:.6875em}.svg-inline--fa.fa-w-12{width:.75em}.svg-inline--fa.fa-w-13{width:.8125em}.svg-inline--fa.fa-w-14{width:.875em}.svg-inline--fa.fa-w-15{width:.9375em}.svg-inline--fa.fa-w-16{width:1em}.svg-inline--fa.fa-w-17{width:1.0625em}.svg-inline--fa.fa-w-18{width:1.125em}.svg-inline--fa.fa-w-19{width:1.1875em}.svg-inline--fa.fa-w-20{width:1.25em}.svg-inline--fa.fa-pull-left{margin-right:.3em;width:auto}.svg-inline--fa.fa-pull-right{margin-left:.3em;width:auto}.svg-inline--fa.fa-border{height:1.5em}.svg-inline--fa.fa-li{width:2em}.svg-inline--fa.fa-fw{width:1.25em}.fa-layers svg.svg-inline--fa{bottom:0;left:0;margin:auto;position:absolute;right:0;top:0}.fa-layers{display:inline-block;height:1em;position:relative;text-align:center;vertical-align:-.125em;width:1em}.fa-layers svg.svg-inline--fa{-webkit-transform-origin:center center;transform-origin:center center}.fa-layers-counter,.fa-layers-text{display:inline-block;position:absolute;text-align:center}.fa-layers-text{left:50%;top:50%;-webkit-transform:translate(-50%,-50%);transform:translate(-50%,-50%);-webkit-transform-origin:center center;transform-origin:center center}.fa-layers-counter{background-color:#ff253a;border-radius:1em;-webkit-box-sizing:border-box;box-sizing:border-box;color:#fff;height:1.5em;line-height:1;max-width:5em;min-width:1.5em;overflow:hidden;padding:.25em;right:0;text-overflow:ellipsis;top:0;-webkit-transform:scale(.25);transform:scale(.25);-webkit-transform-origin:top right;transform-origin:top right}.fa-layers-bottom-right{bottom:0;right:0;top:auto;-webkit-transform:scale(.25);transform:scale(.25);-webkit-transform-origin:bottom right;transform-origin:bottom right}.fa-layers-bottom-left{bottom:0;left:0;right:auto;top:auto;-webkit-transform:scale(.25);transform:scale(.25);-webkit-transform-origin:bottom left;transform-origin:bottom left}.fa-layers-top-right{right:0;top:0;-webkit-transform:scale(.25);transform:scale(.25);-webkit-transform-origin:top right;transform-origin:top right}.fa-layers-top-left{left:0;right:auto;top:0;-webkit-transform:scale(.25);transform:scale(.25);-webkit-transform-origin:top left;transform-origin:top left}.fa-lg{font-size:1.3333333333em;line-height:.75em;vertical-align:-.0667em}.fa-xs{font-size:.75em}.fa-sm{font-size:.875em}.fa-1x{font-size:1em}.fa-2x{font-size:2em}.fa-3x{font-size:3em}.fa-4x{font-size:4em}.fa-5x{font-size:5em}.fa-6x{font-size:6em}.fa-7x{font-size:7em}.fa-8x{font-size:8em}.fa-9x{font-size:9em}.fa-10x{font-size:10em}.fa-fw{text-align:center;width:1.25em}.fa-ul{list-style-type:none;margin-left:2.5em;padding-left:0}.fa-ul>li{position:relative}.fa-li{left:-2em;position:absolute;text-align:center;width:2em;line-height:inherit}.fa-border{border:solid .08em #eee;border-radius:.1em;padding:.2em .25em .15em}.fa-pull-left{float:left}.fa-pull-right{float:right}.fa.fa-pull-left,.fab.fa-pull-left,.fal.fa-pull-left,.far.fa-pull-left,.fas.fa-pull-left{margin-right:.3em}.fa.fa-pull-right,.fab.fa-pull-right,.fal.fa-pull-right,.far.fa-pull-right,.fas.fa-pull-right{margin-left:.3em}.fa-spin{-webkit-animation:fa-spin 2s infinite linear;animation:fa-spin 2s infinite linear}.fa-pulse{-webkit-animation:fa-spin 1s infinite steps(8);animation:fa-spin 1s infinite steps(8)}@-webkit-keyframes fa-spin{0%{-webkit-transform:rotate(0);transform:rotate(0)}100%{-webkit-transform:rotate(360deg);transform:rotate(360deg)}}@keyframes fa-spin{0%{-webkit-transform:rotate(0);transform:rotate(0)}100%{-webkit-transform:rotate(360deg);transform:rotate(360deg)}}.fa-rotate-90{-webkit-transform:rotate(90deg);transform:rotate(90deg)}.fa-rotate-180{-webkit-transform:rotate(180deg);transform:rotate(180deg)}.fa-rotate-270{-webkit-transform:rotate(270deg);transform:rotate(270deg)}.fa-flip-horizontal{-webkit-transform:scale(-1,1);transform:scale(-1,1)}.fa-flip-vertical{-webkit-transform:scale(1,-1);transform:scale(1,-1)}.fa-flip-both,.fa-flip-horizontal.fa-flip-vertical{-webkit-transform:scale(-1,-1);transform:scale(-1,-1)}:root .fa-flip-both,:root .fa-flip-horizontal,:root .fa-flip-vertical,:root .fa-rotate-180,:root .fa-rotate-270,:root .fa-rotate-90{-webkit-filter:none;filter:none}.fa-stack{display:inline-block;height:2em;position:relative;width:2.5em}.fa-stack-1x,.fa-stack-2x{bottom:0;left:0;margin:auto;position:absolute;right:0;top:0}.svg-inline--fa.fa-stack-1x{height:1em;width:1.25em}.svg-inline--fa.fa-stack-2x{height:2em;width:2.5em}.fa-inverse{color:#fff}.sr-only{border:0;clip:rect(0,0,0,0);height:1px;margin:-1px;overflow:hidden;padding:0;position:absolute;width:1px}.sr-only-focusable:active,.sr-only-focusable:focus{clip:auto;height:auto;margin:0;overflow:visible;position:static;width:auto}.svg-inline--fa .fa-primary{fill:var(--fa-primary-color,currentColor);opacity:1;opacity:var(--fa-primary-opacity,1)}.svg-inline--fa .fa-secondary{fill:var(--fa-secondary-color,currentColor);opacity:.4;opacity:var(--fa-secondary-opacity,.4)}.svg-inline--fa.fa-swap-opacity .fa-primary{opacity:.4;opacity:var(--fa-secondary-opacity,.4)}.svg-inline--fa.fa-swap-opacity .fa-secondary{opacity:1;opacity:var(--fa-primary-opacity,1)}.svg-inline--fa mask .fa-primary,.svg-inline--fa mask .fa-secondary{fill:#000}.fad.fa-inverse{color:#fff}</style><link href="./files/css" rel="stylesheet">
<style>
.video-container {
display: grid;
grid-template-columns: repeat(8, 1fr); /* 8列 */
grid-template-rows: repeat(4, auto); /* 4行，高度根据内容自动调整 */
gap: 10px; /* 网格项之间的间隙 */
width: 100%; /* 容器宽度，可根据需要调整 */
margin: auto; /* 居中显示 */
padding: 5px; /* 容器内边距 */
border: 2px dashed #333; /* 虚线边框 */
box-sizing: border-box; /* 边框计算在宽度内 */
}
.video-item {
width: 100%; /* 视频宽度 */
height: auto; /* 视频高度自适应 */
display: block; /* 显示视频 */
}
</style>
<link rel="stylesheet" href="./files/bulma.min.css">
<link rel="stylesheet" href="./files/bulma-carousel.min.css">
<link rel="stylesheet" href="./files/bulma-slider.min.css">
<link rel="stylesheet" href="./files/fontawesome.all.min.css">
<link rel="stylesheet" href="https://cdn.jsdelivr.net/gh/jpswalsh/academicons@1/css/academicons.min.css">
<link rel="stylesheet" href="./files/index.css">
<!-- <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.3/css/all.min.css" integrity="sha512-iBBXm8fW90+nuLcSKlbmrPcLa0OT92xO1BIsZ+ywDWZCvqsWgccV3gFoRBv0z+8dLJgyAHIhR35VZc2oM/gI1w==" crossorigin="anonymous" referrerpolicy="no-referrer" /> -->
<script type="text/javascript" id="www-widgetapi-script" src="./files/www-widgetapi.js" async=""></script><script src="./files/jquery.min.js"></script>
<script defer="" src="./files/fontawesome.all.min.js"></script>
<script src="./files/bulma-carousel.min.js"></script>
<script src="./files/bulma-slider.min.js"></script>
<script src="./files/index.js"></script>
<link rel="icon" href="./files/icon.jpg">
<script src="./files/iframe_api"></script>
</head>
<section class="hero">
<div class="hero-body">
<div class="container">
<div class="columns is-centered">
<div class="column is-two-thirds is-centered has-text-centered">
<h1 class="title is-1 publication-title">ManiCM</h1>
<h2 class="subtitle is-2 publication-subtitle">Real-time 3D Diffusion Policy via Consistency Model for Robotic Manipulation</h2>
<div class="is-size-4 publication-authors">
<span class="author-block">
<a href="https://guanxinglu.github.io/">Guanxing Lu</a><sup>1</sup>*
<a>Zifeng Gao</a><sup>1</sup>*
<a href="https://tianxingchen.github.io">Tianxing Chen</a><sup>2</sup>
<a href="https://github.com/Dai-Wenxun">Wenxun Dai</a><sup>2</sup> <br>
<a href="https://ziweiwangthu.github.io/">Ziwei Wang</a><sup>3</sup> and
<a href="https://andytang15.github.io/">Yansong Tang</a><sup>1†</sup>
</span>
<span class="author-block">
<sup>1</sup>Tsinghua Shenzhen International Graduate School, Tsinghua University<br>
<sup>2</sup>Shanghai AI Laboratory
<sup>3</sup>Carnegie Mellon University
</span>
<p style="font-size: medium; margin-top: 5px;"><sup>*</sup>Equal Contributions, <sup>†</sup>Corresponding author</p>
</div>
<div style="height: 20px;"></div>
<a href="https://hits.seeyoufarm.com"><img src="https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fmanicm-fast.github.io&count_bg=%23953FB2&title_bg=%2340DFB3&icon=&icon_color=%23E7E7E7&title=ManiCM+Page+Viewers&edge_flat=false"/></a>
<body data-new-gr-c-s-check-loaded="14.1176.0" data-gr-ext-installed="">
<div style="height: 20px;"></div>
<div>
<div class="publication-links">
<!-- PDF Link. -->
<span class="link-block">
<a href="https://arxiv.org/pdf/2406.01586" class="external-link button is-normal is-rounded is-dark" target="_blank">
<span class="icon">
<svg class="svg-inline--fa fa-file-pdf fa-w-12" aria-hidden="true" focusable="false" data-prefix="fas" data-icon="file-pdf" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 384 512" data-fa-i2svg=""><path fill="currentColor" d="M181.9 256.1c-5-16-4.9-46.9-2-46.9 8.4 0 7.6 36.9 2 46.9zm-1.7 47.2c-7.7 20.2-17.3 43.3-28.4 62.7 18.3-7 39-17.2 62.9-21.9-12.7-9.6-24.9-23.4-34.5-40.8zM86.1 428.1c0 .8 13.2-5.4 34.9-40.2-6.7 6.3-29.1 24.5-34.9 40.2zM248 160h136v328c0 13.3-10.7 24-24 24H24c-13.3 0-24-10.7-24-24V24C0 10.7 10.7 0 24 0h200v136c0 13.2 10.8 24 24 24zm-8 171.8c-20-12.2-33.3-29-42.7-53.8 4.5-18.5 11.6-46.6 6.2-64.2-4.7-29.4-42.4-26.5-47.8-6.8-5 18.3-.4 44.1 8.1 77-11.6 27.6-28.7 64.6-40.8 85.8-.1 0-.1.1-.2.1-27.1 13.9-73.6 44.5-54.5 68 5.6 6.9 16 10 21.5 10 17.9 0 35.7-18 61.1-61.8 25.8-8.5 54.1-19.1 79-23.2 21.7 11.8 47.1 19.5 64 19.5 29.2 0 31.2-32 19.7-43.4-13.9-13.6-54.3-9.7-73.6-7.2zM377 105L279 7c-4.5-4.5-10.6-7-17-7h-6v128h128v-6.1c0-6.3-2.5-12.4-7-16.9zm-74.1 255.3c4.1-2.7-2.5-11.9-42.8-9 37.1 15.8 42.8 9 42.8 9z"></path></svg><!-- <i class="fas fa-file-pdf"></i> Font Awesome fontawesome.com -->
</span>
<span>Paper</span>
</a>
</span>
<!-- arXiv Link. -->
<span class="link-block">
<a href="https://arxiv.org/abs/2406.01586" class="external-link button is-normal is-rounded is-dark" target="_blank">
<span class="icon">
<i class="ai ai-arxiv"></i>
</span>
<span>arXiv</span>
</a>
</span>
<!-- Code Link. -->
<span class="link-block">
<a href="https://github.com/ManiCM-fast/ManiCM" class="external-link button is-normal is-rounded is-dark" target="_blank">
<span class="icon">
<svg class="svg-inline--fa fa-github fa-w-16" aria-hidden="true" focusable="false" data-prefix="fab" data-icon="github" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512" data-fa-i2svg=""><path fill="currentColor" d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"></path></svg><!-- <i class="fab fa-github"></i> Font Awesome fontawesome.com -->
</span>
<span>Code</span>
</a>
</span>
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<section class="section" style="margin-top: -60px">
<div class="container">
<!-- Abstract. -->
<div class="columns is-centered has-text-centered">
<div class="column is-two-thirds">
<h2 class="title is-2">Abstract</h2>
<div class="content has-text-justified">
<p>
Diffusion models have been verified to be effective in generating complex distributions from natural images to motion trajectories. Recent diffusion-based methods show impressive performance in 3D robotic manipulation tasks, whereas they suffer from severe runtime inefficiency due to multiple denoising steps, especially with high-dimensional observations. To this end, we propose a real-time robotic manipulation model named <b>ManiCM</b> that imposes the consistency constraint to the diffusion process, so that the model can generate robot actions in only one-step inference.
</p>
</div>
</div>
</div>
</div>
</section>
<section class="section" style="margin-top: -60px">
<div class="container">
<h2 class="title is-2" style="text-align: center;">Framework of ManiCM</h2>
<div style="text-align: center; margin-top: 20px;">
<img src="./files/2024-ManiCM.png" style="width: 50%">
</div>
<div class="columns is-centered has-text-centered">
<div class="column is-two-thirds">
<div class="content has-text-justified">
<p>
Given a raw action sequence a<sub>0</sub>, we first perform a forward diffusion to introduce
noise over n + k steps. The resulting noisy sequence a<sub>n+k</sub> is then fed into both the online network and the
teacher network to predict the clean action sequence. The target network uses the teacher network’s k-step
estimation results to predict the action sequence. To enforce self-consistency, a loss function is applied to ensure
that the outputs of the online network and the target network are consistent.
</p>
</div>
</div>
</div>
</div>
</div>
</section>
<section class="section" style="margin-top: -60px">
<div class="container">
<!-- Abstract. -->
<div class="columns is-centered has-text-centered">
<div class="column is-two-thirds">
<h2 class="title is-2">Results</h2>
</div>
</div>
<div class="container">
<!-- Abstract. -->
<div class="columns is-centered has-text-centered">
<div class="column is-two-thirds">
<div class="content has-text-justified" style="font-size: larger; font-weight: 800">
31 tasks (Adroit and MetaWorld)
</div>
<div class="video-container" id="videoContainer">
<!-- 视频标签将通过JavaScript动态添加 -->
</div>
<script>
// 获取视频容器
var container = document.getElementById('videoContainer');
// 函数用于创建视频元素并添加到容器
function createVideoElement(index) {
var video = document.createElement('video');
video.className = 'video-item';
video.setAttribute('controls', '');
video.setAttribute('autoplay', '');
video.setAttribute('loop', '');
video.muted = true; // 静音自动播放
var source = document.createElement('source');
source.src = 'videos/' + index + '.mp4';
source.type = 'video/mp4';
video.appendChild(source);
// 视频加载完成后自动播放
video.addEventListener('loadedmetadata', function() {
video.style.display = 'block'; // 显示视频
video.play(); // 播放视频
});
return video;
}
// 动态创建并添加32个视频到页面
for (var i = 1; i <= 32; i++) {
container.appendChild(createVideoElement(i));
}
</script>
<div class="content has-text-justified" style="margin-top: 10px">
We conduct our experiments in the well-recognized MetaWorld and Adroit benchmarks, resulting in a total of 31 tasks. These tasks range from simple pick-and-place tasks to more challenging scenarios such as dexterous manipulation, which ensure that the model is effective across various scenarios.
</div>
</div>
</div>
<div class="container">
<!-- Abstract. -->
<div class="columns is-centered has-text-centered">
<div class="column is-two-thirds">
<div class="content has-text-justified" style="font-size: larger; font-weight: 800">
Comparisons on Runtime
</div>
<div style="text-align: center; margin-top: 20px;">
<img src="./files/result1.jpg" style="width: 80%">
</div>
<div class="content has-text-justified">
We evaluate 100 episodes on 31 challenging tasks from Adroit and Metaworld across 3 random seeds and report the time consumption per step (s) with standard deviation. The second results are underlined and the best results are bold. ‘∗’ denotes the reproduced version. The performance of our ManiCM in one-step inference surpasses all state-of-the-art models, providing ample evidence for the effectiveness of consistency distillation.
</div>
</div>
</div>
<div class="container">
<!-- Abstract. -->
<div class="columns is-centered has-text-centered">
<div class="column is-two-thirds">
<div class="content has-text-justified" style="font-size: larger; font-weight: 800">
Comparisons on Success Rate
</div>
<div style="text-align: center; margin-top: 20px;">
<img src="./files/result2.jpg" style="width: 80%">
</div>
<div class="content has-text-justified">
We evaluate 100 episodes on 31 challenging tasks from Adroit and Metaworld across 3 random seeds and report the success rates (%) with standard deviation. The second results are underlined and the best results are bold. ‘∗’ denotes the reproduced version. The performance of our ManiCM in one-step inference surpasses all state-of-the-art models, providing ample evidence for the effectiveness of consistency distillation.
</div>
</div>
</div>
</div>
</section>
<section class="section" style="margin-top: -60px">
<div class="container">
<!-- Abstract. -->
<div class="columns is-centered has-text-centered">
<div class="column is-two-thirds">
<h2 class="title is-2">Acknowledgements</h2>
<div class="content has-text-justified">
<p>
The author team would like to acknowledge <a href="https://liang-zx.github.io/">Zhixuan Liang </a>and <a href="https://yaomarkmu.github.io/">Yao Mu</a> from the University of Hong Kong for their helpful technical discussion and suggestions.<br><br>
Our code is built upon
<a href="https://github.com/YanjieZe/3D-Diffusion-Policy" target="_blank">3D Diffusion Policy</a>,
<a href="https://github.com/Dai-Wenxun/MotionLCM" target="_blank">MotionLCM</a>,
<a href="https://github.com/luosiallen/latent-consistency-model" target="_blank">Latent Consistency Model</a>,
<a href="https://github.com/real-stanford/diffusion_policy" target="_blank">Diffusion Policy</a>,
<a href="https://github.com/microsoft/VRL3" target="_blank">VRL3</a>,
<a href="https://github.com/Farama-Foundation/Metaworld" target="_blank">Metaworld</a>,
and
<a href="https://github.com/GuanxingLu/ManiGaussian" target="_blank">ManiGaussian</a>.
We would like to thank the authors for their excellent works.
</p>
</div>
</div>
</div>
</div>
</section>
<section class="section" id="BibTeX" style="margin-top: -60px">
<div class="container content">
<h2 class="titile">BibTeX</h2>
<pre><code>@article{lu2024manicm,
title={ManiCM: Real-time 3D Diffusion Policy via Consistency Model for Robotic Manipulation},
author={Guanxing Lu and Zifeng Gao and Tianxing Chen and Wenxun Dai and Ziwei Wang and Yansong Tang},
journal={arXiv preprint arXiv:2406.01586},
year={2024}
}</code></pre>
</div>
</section>
<footer class="footer">
<div class="container">
<div class="content has-text-centered">
<p>Page template borrowed from <a href="https://nerfies.github.io/"><span class="dnerf">Nerfies</span></a> and <a href="https://robot-parkour.github.io/"><span class="dnerf">Robot-Parkour</span></a>.</p>
</div>
</div>
</footer>