-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathindex.html
More file actions
705 lines (661 loc) · 34.9 KB
/
index.html
File metadata and controls
705 lines (661 loc) · 34.9 KB
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
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<!-- Primary Meta Tags -->
<!-- TODO: Replace with your paper title and author names -->
<meta name="title" content="VO-DP: Semantic-Geometric Adaptive Diffusion Policy for Vision-Only Robotic Manipulation">
<!-- TODO: Write a compelling 150-160 character description of your research -->
<meta name="description" content="Semantic-Geometric Adaptive Diffusion Policy for Vision-Only Robotic Manipulation">
<!-- TODO: Add 5-10 relevant keywords for your research area -->
<meta name="keywords" content="Representation Learning; Imitation Learning; Perception for Grasping and Manipulation">
<!-- TODO: List all authors -->
<meta name="author" content="Zehao Ni, Yonghao He, Lingfeng Qian, Jilei Mao, Fa Fu, Wei Sui, Hu Su, Junran Peng, Zhipeng Wang, Bin He">
<meta name="robots" content="index, follow">
<meta name="language" content="English">
<!-- Open Graph / Facebook -->
<meta property="og:type" content="article">
<!-- TODO: Replace with your institution or lab name -->
<meta property="og:site_name" content="D-Robotics">
<!-- TODO: Same as paper title above -->
<meta property="og:title" content="VO-DP: Semantic-Geometric Adaptive Diffusion Policy for Vision-Only Robotic Manipulation">
<!-- TODO: Same as description above -->
<meta property="og:description" content="Semantic-Geometric Adaptive Diffusion Policy for Vision-Only Robotic Manipulation">
<!-- TODO: Replace with your actual website URL -->
<meta property="og:url" content="https://d-robotics-ai-lab.github.io/vodp/">
<!-- TODO: Create a 1200x630px preview image and update path -->
<meta property="og:image" content="https://YOUR_DOMAIN.com/static/images/social_preview.png">
<meta property="og:image:width" content="1200">
<meta property="og:image:height" content="630">
<meta property="og:image:alt" content="VO-DP - Research Preview">
<meta property="article:published_time" content="2025-09-01T00:00:00.000Z">
<meta property="article:author" content="Zehao Ni">
<meta property="article:section" content="Research">
<meta property="article:tag" content="Vision-Only">
<meta property="article:tag" content="Representation Learning">
<meta property="article:tag" content="Imitation Learning">
<meta property="article:tag" content="Perception for Grasping and Manipulation">
<!-- Twitter -->
<meta name="twitter:card" content="summary_large_image">
<!-- TODO: Replace with your lab/institution Twitter handle -->
<meta name="twitter:site" content="@YOUR_TWITTER_HANDLE">
<!-- TODO: Replace with first author's Twitter handle -->
<meta name="twitter:creator" content="@AUTHOR_TWITTER_HANDLE">
<!-- TODO: Same as paper title above -->
<meta name="twitter:title" content="PAPER_TITLE">
<!-- TODO: Same as description above -->
<meta name="twitter:description" content="BRIEF_DESCRIPTION_OF_YOUR_RESEARCH_CONTRIBUTION_AND_FINDINGS">
<!-- TODO: Same as social preview image above -->
<meta name="twitter:image" content="https://YOUR_DOMAIN.com/static/images/social_preview.png">
<meta name="twitter:image:alt" content="PAPER_TITLE - Research Preview">
<!-- Academic/Research Specific -->
<meta name="citation_title" content="VO-DP: Semantic-Geometric Adaptive Diffusion Policy for Vision-Only Robotic Manipulation">
<meta name="citation_author" content="Ni, Zehao">
<meta name="citation_author" content="Yonghao, He">
<meta name="citation_author" content="Lingfeng, Qian">
<meta name="citation_author" content="Jilei, Mao">
<meta name="citation_author" content="Fa, Fu">
<meta name="citation_author" content="Wei, Sui">
<meta name="citation_author" content="Hu, Su">
<meta name="citation_author" content="Junran, Peng">
<meta name="citation_author" content="Zhipeng, Wang">
<meta name="citation_author" content="Bin, He">
<meta name="citation_publication_date" content="2025">
<meta name="citation_conference_title" content="Arxiv">
<meta name="citation_pdf_url" content="https://YOUR_DOMAIN.com/static/pdfs/paper.pdf">
<!-- Additional SEO -->
<meta name="theme-color" content="#2563eb">
<meta name="msapplication-TileColor" content="#2563eb">
<meta name="apple-mobile-web-app-capable" content="yes">
<meta name="apple-mobile-web-app-status-bar-style" content="default">
<!-- Preconnect for performance -->
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link rel="preconnect" href="https://ajax.googleapis.com">
<link rel="preconnect" href="https://documentcloud.adobe.com">
<link rel="preconnect" href="https://cdn.jsdelivr.net">
<!-- TODO: Replace with your paper title and authors -->
<title>VO-DP</title>
<!-- Favicon and App Icons -->
<link rel="icon" type="image/x-icon" href="static/images/favicon.png">
<link rel="apple-touch-icon" href="static/images/favicon.png">
<!-- Critical CSS - Load synchronously -->
<link rel="stylesheet" href="static/css/bulma.min.css">
<link rel="stylesheet" href="static/css/index.css">
<!-- Non-critical CSS - Load asynchronously -->
<link rel="preload" href="static/css/bulma-carousel.min.css" as="style" onload="this.onload=null;this.rel='stylesheet'">
<link rel="preload" href="static/css/bulma-slider.min.css" as="style" onload="this.onload=null;this.rel='stylesheet'">
<link rel="preload" href="static/css/fontawesome.all.min.css" as="style" onload="this.onload=null;this.rel='stylesheet'">
<link rel="preload" href="https://cdn.jsdelivr.net/gh/jpswalsh/academicons@1/css/academicons.min.css" as="style" onload="this.onload=null;this.rel='stylesheet'">
<!-- Fallback for browsers that don't support preload -->
<noscript>
<link rel="stylesheet" href="static/css/bulma-carousel.min.css">
<link rel="stylesheet" href="static/css/bulma-slider.min.css">
<link rel="stylesheet" href="static/css/fontawesome.all.min.css">
<link rel="stylesheet" href="https://cdn.jsdelivr.net/gh/jpswalsh/academicons@1/css/academicons.min.css">
</noscript>
<!-- Fonts - Optimized loading -->
<link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700;800&display=swap" rel="stylesheet">
<!-- Defer non-critical JavaScript -->
<script defer src="https://ajax.googleapis.com/ajax/libs/jquery/3.5.1/jquery.min.js"></script>
<script defer src="https://documentcloud.adobe.com/view-sdk/main.js"></script>
<script defer src="static/js/fontawesome.all.min.js"></script>
<script defer src="static/js/bulma-carousel.min.js"></script>
<script defer src="static/js/bulma-slider.min.js"></script>
<script defer src="static/js/index.js"></script>
<!-- Structured Data for Academic Papers -->
<script type="application/ld+json">
{
"@context": "https://schema.org",
"@type": "ScholarlyArticle",
"headline": "PAPER_TITLE",
"description": "BRIEF_DESCRIPTION_OF_YOUR_RESEARCH_CONTRIBUTION_AND_FINDINGS",
"author": [
{
"@type": "Person",
"name": "FIRST_AUTHOR_NAME",
"affiliation": {
"@type": "Organization",
"name": "INSTITUTION_NAME"
}
},
{
"@type": "Person",
"name": "SECOND_AUTHOR_NAME",
"affiliation": {
"@type": "Organization",
"name": "INSTITUTION_NAME"
}
}
],
"datePublished": "2024-01-01",
"publisher": {
"@type": "Organization",
"name": "CONFERENCE_OR_JOURNAL_NAME"
},
"url": "https://YOUR_DOMAIN.com/YOUR_PROJECT_PAGE",
"image": "https://YOUR_DOMAIN.com/static/images/social_preview.png",
"keywords": ["KEYWORD1", "KEYWORD2", "KEYWORD3", "machine learning", "computer vision"],
"abstract": "FULL_ABSTRACT_TEXT_HERE",
"citation": "BIBTEX_CITATION_HERE",
"isAccessibleForFree": true,
"license": "https://creativecommons.org/licenses/by/4.0/",
"mainEntity": {
"@type": "WebPage",
"@id": "https://YOUR_DOMAIN.com/YOUR_PROJECT_PAGE"
},
"about": [
{
"@type": "Thing",
"name": "RESEARCH_AREA_1"
},
{
"@type": "Thing",
"name": "RESEARCH_AREA_2"
}
]
}
</script>
<!-- Website/Organization Structured Data -->
<script type="application/ld+json">
{
"@context": "https://schema.org",
"@type": "Organization",
"name": "INSTITUTION_OR_LAB_NAME",
"url": "https://YOUR_INSTITUTION_WEBSITE.com",
"logo": "https://YOUR_DOMAIN.com/static/images/favicon.ico",
"sameAs": [
"https://twitter.com/YOUR_TWITTER_HANDLE",
"https://github.com/YOUR_GITHUB_USERNAME"
]
}
</script>
</head>
<body>
<!-- Scroll to Top Button -->
<button class="scroll-to-top" onclick="scrollToTop()" title="Scroll to top" aria-label="Scroll to top">
<i class="fas fa-chevron-up"></i>
</button>
<!-- More Works Dropdown -->
<div class="more-works-container">
<button class="more-works-btn" onclick="toggleMoreWorks()" title="View More Works from Our Lab">
<i class="fas fa-flask"></i>
More Works
<i class="fas fa-chevron-down dropdown-arrow"></i>
</button>
<div class="more-works-dropdown" id="moreWorksDropdown">
<div class="dropdown-header">
<h4>More Works from Our Lab</h4>
<button class="close-btn" onclick="toggleMoreWorks()">
<i class="fas fa-times"></i>
</button>
</div>
<div class="works-list">
<!-- TODO: Replace with your lab's related works -->
<a href="https://arxiv.org/abs/2412.14680" class="work-item" target="_blank">
<div class="work-info">
<!-- TODO: Replace with actual paper title -->
<h5>A Light-Weight Framework for Open-Set Object Detection with Decoupled Feature Alignment in Joint Space</h5>
<!-- TODO: Replace with brief description -->
<!-- <p>A lightweight framework that enhances real-time performance for robotic systems by using an MLP adaptor to
decouple and align visual and text features in a joint space.</p> -->
<!-- TODO: Replace with venue and year -->
<span class="work-venue">2025 IEEE International Conference on Robotics and Automation (ICRA)</span>
</div>
<i class="fas fa-external-link-alt"></i>
</a>
<!-- TODO: Add more related works or remove extra items -->
<a href="https://openaccess.thecvf.com/content/CVPR2025W/MEIS/papers/Yu_Efficient_Task-specific_Conditional_Diffusion_Policies_Shortcut_Model_Acceleration_and_SO3_CVPRW_2025_paper.pdf" class="work-item" target="_blank">
<div class="work-info">
<h5>Efficient Task-Specific Conditional Diffusion Policies: Shortcut Model Acceleration and SO(3) Optimization</h5>
<!-- <p>A Classifier-Free Shortcut Diffusion Policy (CF-SDP) that significantly accelerates robot action generation
by integrating shortcut-based sampling and SO(3) manifold optimization. </p> -->
<span class="work-venue">Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2025</span>
</div>
<i class="fas fa-external-link-alt"></i>
</a>
<!-- <a href="https://arxiv.org/abs/PAPER_ID_3" class="work-item" target="_blank">
<div class="work-info">
<h5>Paper Title 3</h5>
<p>Brief description of the work and its main contribution.</p>
<span class="work-venue">Conference/Journal 2023</span>
</div>
<i class="fas fa-external-link-alt"></i>
</a> -->
</div>
</div>
</div>
<main id="main-content">
<section class="hero">
<div class="hero-body" style="padding-bottom: 0px">
<div class="container is-max-desktop">
<div class="columns is-centered">
<div class="column has-text-centered">
<!-- TODO: Replace with your paper title -->
<h1 class="title is-1 publication-title" style="font-size: 34px;">
<font color="#eb4e27">VO-DP</font>: Semantic-Geometric Adaptive
<font color="#eb4e27">D</font>iffusion <font color="#eb4e27">P</font>olicy for
<font color="#eb4e27">V</font>ision-<font color="#eb4e27">O</font>nly Robotic Manipulation
</h1>
<div class="is-size-5 publication-authors">
<!-- TODO: Replace with your paper authors and their personal links -->
<span class="author-block">
<a onclick="return false">Zehao Ni</a><sup>1,2,5,6*</sup>,
</span>
<span class="author-block">
<a href="https://github.com/YonghaoHe" target="_blank">Yonghao He</a><sup>1*†</sup>,
</span>
<span class="author-block">
<a onclick="return false">Lingfeng Qian</a><sup>1*</sup>,
</span>
<span class="author-block">
<a onclick="return false">Jilei Mao</a><sup>1</sup>,
</span>
<span class="author-block">
<a onclick="return false">Fa Fu</a><sup>1</sup>,
</span>
<span class="author-block">
<a onclick="return false">Wei Sui</a><sup>1</sup>,
</span>
<span class="author-block">
<a onclick="return false">Hu Su</a><sup>4</sup>,
</span>
<span class="author-block">
<a href="https://jrpeng.github.io/" target="_blank">Junran Peng</a><sup>3✉</sup>,
</span>
<span class="author-block">
<a href="https://robot.tongji.edu.cn/info/1256/2086.htm" target="_blank">Zhipeng Wang</a><sup>2,5,6</sup>,
</span>
<span class="author-block">
<a href="https://robot.tongji.edu.cn/info/1256/2083.htm" target="_blank">Bin He</a><sup>2,5,6✉</sup>
</span>
</div>
<div class="is-size-5 publication-authors" style="margin-bottom: 0px;">
<!-- TODO: Replace with your institution and conference/journal info -->
<span class="author-block" style="font-size: 14px;">
<sup>1</sup>D-Robotics<br>
<sup>2</sup>National Key Laboratory of Autonomous Intelligent Unmanned Systems<br>
<sup>3</sup>University of Science and Technology Beijing<br>
<sup>4</sup>State Key Laboratory of Multimodal Artificial Intelligence System (MAIS) Institute of Automation of Chinese Academy of Sciences<br>
<sup>5</sup>Frontiers Science Center for Intelligent Autonomous Systems<br>
<sup>6</sup>Shanghai Institute of Intelligent Science and Technology, Tongji University<br>
</span>
<!-- <span class="author-block">
ICRA 2026
</span> -->
<span class="author-block" style="font-size: 16px;">
<sup>*</sup>Equal Contribution <sup>*</sup>Project Leader <sup>✉</sup>Corresponding authors
</span>
<!-- TODO: Remove this line if no equal contribution -->
<!-- <span class="eql-cntrb"><small><sup>*</sup>Equal Contribution <sup>*</sup>Project Leader <sup>✉</sup>Corresponding authors</small></span> -->
</div>
<div class="column has-text-centered">
<div class="publication-links">
<!-- TODO: Update with your arXiv paper ID -->
<span class="link-block">
<a href="https://arxiv.org/abs/2510.15530" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="ai ai-arxiv"></i>
</span>
<span>arXiv</span>
</a>
</span>
<!-- TODO: Update with your arXiv paper ID -->
<span class="link-block">
<a href="https://arxiv.org/pdf/2510.15530" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-file-pdf"></i>
</span>
<span>PDF</span>
</a>
</span>
<!-- TODO: Add your supplementary material PDF or remove this section -->
<span class="link-block">
<a href="https://huggingface.co/datasets/D-Robotics/DRRM" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-database"></i>
</span>
<span>Dataset</span>
</a>
</span>
<!-- TODO: Replace with your GitHub repository URL -->
<span class="link-block">
<a href="https://github.com/D-Robotics-AI-Lab/DRRM" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fab fa-github"></i>
</span>
<span>Code</span>
</a>
</span>
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<!-- Teaser video-->
<section class="hero teaser">
<div class="container is-max-desktop">
<div class="hero-body">
<center>
<!-- static/images/vodp-all.jpg -->
<img src="static/images/compare.jpg"
alt="VO-DP" class="blend-img-background center-image"
style="max-width: 90%; height: auto;" loading="lazy">
</center>
<p>
VO-DP is a vision-only method for visuomotor
robotic manipulation: it takes single-view RGB images as
input, uses large vision models to extract semantic and geometric
features from observations, and provides high-quality
conditional inputs for the policy head. Experiments show
it matches point cloud-based DP3’s accuracy in simulation,
outperforms it significantly in real-world tasks, and notably
boosts vision-only method accuracy.
</p>
</div>
</div>
</section>
<!-- End teaser video -->
<!-- Paper abstract -->
<section class="section hero is-light">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified" style="width: 100%; font-size: 16px;">
<!-- TODO: Replace with your paper abstract -->
<p>
In the context of imitation learning, visuomotor-based
diffusion policy learning is one of the main directions in
robotic manipulation. Most of these approaches rely on point
clouds as observation inputs and construct scene representations
through point clouds feature learning, which enables
them to achieve remarkable accuracy. However, the existing
literature lacks an in-depth exploration of vision-only solutions
that have significant potential. In this paper, we propose a
Vision-Only and single-view Diffusion Policy learning method
(VO-DP) that leverages pretrained visual foundation models
to achieve effective fusion of semantic and geometric features.
We utilize intermediate features from VGGT incorporating
semantic features from DINOv2 and geometric features from
Alternating Attention blocks. Features are fused via cross-attention
and spatially compressed with a CNN to form the
input to the policy head. Extensive experiments demonstrate
that VO-DP not only outperforms the vision-only baseline
DP significantly but also exhibits distinct performance trends
against the point cloud-based method DP3: in simulation tasks,
VO-DP achieves an average success rate of 64.6%—on par with
DP3 64.0% and far higher than DP 34.8%, while in real-world
tasks, it reaches 87.9%, outperforming both DP3 67.5% and
DP 11.2% by a notable margin. Further robustness evaluations
confirm that VO-DP remains highly stable under varying conditions
including color, size, background, and lighting. Lastly,
we open-source DRRM (D-Robotics Robotic Manipulation), a
training library for robotic manipulation. Built on Accelerate,
this library supports multi-machine and multi-GPU parallel
training, as well as mixed precision training (e.g., bf16, fp16).
It is compatible with visuomotor policies such as DP and DP3,
and also supports the RoboTwin simulator. VO-DP is integrated
into DRRM. We refer to the project page for the code and videos.
</div>
</div>
</div>
</div>
<div class="container is-max-desktop">
<video poster="" id="tree" autoplay muted controls loop height="100%" preload="metadata">
<source src="https://drobotics-ailab.tos-cn-beijing.volces.com/public/vodp/VO-DP-1080_h264.mp4" type="video/mp4">
</video>
</div>
</section>
<!-- End paper abstract -->
<!-- Method -->
<section class="section hero">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths" style="width: 100%">
<h2 class="title is-3">Method</h2>
<div class="content has-text-justified" style="font-size: 16px;">
<p>
Our method VO-DP has four core modules: 1) VGGT Encoder extracts semantic features from
patchified images via DINOv2 and generates geometric features through its AA network; 2) Semantic-Geometric Fuser
fuses per-frame geometric and semantic features using residual cross-attention and an FFN; 3) Spatial Compression module
reshapes fused features, downsamples them with a lightweight ResNet, and concatenates the compressed spatial features
with proprioceptive observations to form compact scenario representations; 4) Vision-Only Conditioned Action Generation
module employs a DDPM-based policy head to generate actions using the scenario representations.
</p>
<center>
<img src="static/images/framework_a.jpg" alt="VO-DP"
class="blend-img-background center-image" style="max-width: 90%; height: auto;" loading="lazy">
</center>
</div>
</div>
</div>
</div>
</section>
<!-- End Method -->
<!-- Effectiveness -->
<section class="section hero is-light">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths" style="width: 100%">
<h2 class="title is-3">Effectiveness</h2>
<div class="content has-text-justified" style="font-size: 16px;">
<p>
VO-DP achieves state-of-the-art (SOTA) performance
in both simulation and real-world environments,
while also demonstrating high data efficiency and strong generalization.
In simulation, it is rigorously evaluated on the challenging 14-task RoboTwin benchmark.
For real-world tasks such as pick-and-place and stacking, VO-DP is trained with only
200 demonstrations per task, showcasing exceptional data efficiency. More importantly,
the method exhibits remarkable generalization capabilities across significant variations,
maintaining robust performance in rigorous tests for size, appearance, illumination,
and background robustness, with success rates such as 65.0% on unseen object sizes and 83.3%
under dynamic lighting interference.
</p>
<center>
<img src="static/images/result.jpg" alt="VO-DP"
class="blend-img-background center-image" style="max-width: 90%; height: auto;" loading="lazy">
</center>
</div>
</div>
</div>
</section>
<!-- End Effectiveness -->
<!-- Simulation evaluation -->
<section class="section hero">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths" style="width: 100%">
<h2 class="title is-3">Geometry-Aware Tokens<br>Visualization on Robotwin</h2>
<div class="content has-text-justified" style="font-size: 16px;">
<p>
VO-DP demonstrates its effective spatial
representation in embodied scenarios through its geometry-aware tokens,
as evaluated on the challenging RoboTwin benchmark.
It successfully addresses 14 bimanual tasks by comprehending semantic
intent and geometric structure from RGB image inputs.
The policy achieves high success rates by satisfying precise pose constraints
and maintaining collision-free trajectories across extensive test scenes.
</p>
<video poster="" id="tree" autoplay muted loop height="80%" preload="metadata">
<!-- TODO: Add your video file path here -->
<source src="https://drobotics-ailab.tos-cn-beijing.volces.com/public/vodp/vggt-vis_h264.mp4" type="video/mp4">
</video>
</div>
</div>
</div>
</div>
</section>
<!-- End Simulation evaluation -->
<!-- Real-world evaluation -->
<!-- <section class="section hero is-light">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths" style="width: 100%">
<h2 class="title is-3">Real-world tasks</h2>
<div class="content has-text-justified" style="font-size: 16px;">
<p>
VO-DP is a vision-only method for visuomotor
robotic manipulation: it takes single-view RGB images as
input, uses large vision models to extract semantic and geometric
features from observations, and provides high-quality
conditional inputs for the policy head. Experiments show
it matches point cloud-based DP3’s accuracy in simulation,
outperforms it significantly in real-world tasks, and notably
boosts vision-only method accuracy.
</p>
<p><img src="static/images/vodp-all.jpg" alt="VO-DP" class="blend-img-background center-image" style="max-width: 100%; height: auto;" loading="lazy"></p>
</div>
</div>
</div>
</div>
</section> -->
<!-- End Real-world evaluation -->
<!-- Robustness evaluation -->
<!-- <section class="section hero">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths" style="width: 100%">
<h2 class="title is-3">Robustness evaluation</h2>
<div class="content has-text-justified" style="font-size: 16px;">
<p>
VO-DP is a vision-only method for visuomotor
robotic manipulation: it takes single-view RGB images as
input, uses large vision models to extract semantic and geometric
features from observations, and provides high-quality
conditional inputs for the policy head. Experiments show
it matches point cloud-based DP3’s accuracy in simulation,
outperforms it significantly in real-world tasks, and notably
boosts vision-only method accuracy.
</p>
<div class="columns is-multiline">
<div class="column is-one-quarter">
<video poster="" class="grid-video" autoplay muted loop preload="metadata" style="width:100%; height:auto;" aria-label="VO-DP visualization 1">
<source src="https://drobotics-ailab.tos-cn-beijing.volces.com/public/vodp/exp/video_robust_light_h264.mp4" type="video/mp4">
</video>
</div>
<div class="column is-one-quarter">
<video poster="" class="grid-video" autoplay muted loop preload="metadata" style="width:100%; height:auto;" aria-label="VO-DP visualization 2">
<source src="https://drobotics-ailab.tos-cn-beijing.volces.com/public/vodp/exp/video_robust_bg_h264.mp4" type="video/mp4">
</video>
</div>
<div class="column is-one-quarter">
<video poster="" class="grid-video" autoplay muted loop preload="metadata" style="width:100%; height:auto;" aria-label="VO-DP visualization 3">
<source src="https://drobotics-ailab.tos-cn-beijing.volces.com/public/vodp/exp/video_robust_size_h264.mp4" type="video/mp4">
</video>
</div>
<div class="column is-one-quarter">
<video poster="" class="grid-video" autoplay muted loop preload="metadata" style="width:100%; height:auto;" aria-label="VO-DP visualization 4">
<source src="https://drobotics-ailab.tos-cn-beijing.volces.com/public/vodp/exp/video_robust_color_h264.mp4" type="video/mp4">
</video>
</div>
<div class="column is-one-quarter">
<video poster="" class="grid-video" autoplay muted loop preload="metadata" style="width:100%; height:auto;" aria-label="VO-DP visualization 5">
<source src="https://drobotics-ailab.tos-cn-beijing.volces.com/public/vodp/exp/video_robust_pi0.5_light_h264.mp4" type="video/mp4">
</video>
</div>
<div class="column is-one-quarter">
<video poster="" class="grid-video" autoplay muted loop preload="metadata" style="width:100%; height:auto;" aria-label="VO-DP visualization 6">
<source src="https://drobotics-ailab.tos-cn-beijing.volces.com/public/vodp/exp/video_robust_pi0.5_bg_h264.mp4" type="video/mp4">
</video>
</div>
<div class="column is-one-quarter">
<video poster="" class="grid-video" autoplay muted loop preload="metadata" style="width:100%; height:auto;" aria-label="VO-DP visualization 7">
<source src="https://drobotics-ailab.tos-cn-beijing.volces.com/public/vodp/exp/video_robust_pi0.5_size_h264.mp4" type="video/mp4">
</video>
</div>
<div class="column is-one-quarter">
<video poster="" class="grid-video" autoplay muted loop preload="metadata" style="width:100%; height:auto;" aria-label="VO-DP visualization 8">
<source src="https://drobotics-ailab.tos-cn-beijing.volces.com/public/vodp/exp/video_robust_pi0.5_color_h264.mp4" type="video/mp4">
</video>
</div>
</div>
</div>
</div>
</div>
</div>
</section> -->
<!-- End Robustness evaluation -->
<!-- Robustness evaluation -->
<section class="hero is-small">
<div class="hero-body">
<div class="container has-text-centered">
<h2 class="title is-3">Robustness evaluation</h2>
<div id="results-carousel" class="carousel results-carousel" style="padding-top: 0px; width: 80%;">
<div class="item item-video1">
<video poster="" id="video1" autoplay muted loop height="100%" preload="metadata">
<source src="https://drobotics-ailab.tos-cn-beijing.volces.com/public/vodp/exp/video_robust_light_h264.mp4" type="video/mp4">
</video>
</div>
<div class="item item-video2">
<!-- TODO: Add poster image for better preview -->
<video poster="" id="video2" autoplay muted loop height="100%" preload="metadata">
<!-- Your video file here -->
<source src="https://drobotics-ailab.tos-cn-beijing.volces.com/public/vodp/exp/video_robust_bg_h264.mp4" type="video/mp4">
</video>
</div>
<div class="item item-video3">
<!-- TODO: Add poster image for better preview -->
<video poster="" id="video3" autoplay muted loop height="100%" preload="metadata">
<!-- Your video file here -->
<source src="https://drobotics-ailab.tos-cn-beijing.volces.com/public/vodp/exp/video_robust_size_h264.mp4" type="video/mp4">
</video>
</div>
<div class="item item-video4">
<!-- TODO: Add poster image for better preview -->
<video poster="" id="video4" autoplay muted loop height="100%" preload="metadata">
<!-- Your video file here -->
<source src="https://drobotics-ailab.tos-cn-beijing.volces.com/public/vodp/exp/video_robust_color_h264.mp4" type="video/mp4">
</video>
</div>
</div>
</div>
</div>
</section>
<!-- End Robustness evaluation -->
<!--BibTex citation -->
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<div class="bibtex-header">
<h2 class="title">BibTeX</h2>
<button class="copy-bibtex-btn" onclick="copyBibTeX()" title="Copy BibTeX to clipboard">
<i class="fas fa-copy"></i>
<span class="copy-text">Copy</span>
</button>
</div>
<pre id="bibtex-code"><code>@article{ni2025vodp,
title={VO-DP: Semantic-Geometric Adaptive Diffusion Policy for Vision-Only Robotic Manipulation},
author={Zehao Ni and Yonghao He and Lingfeng Qian and Jilei Mao and Fa Fu and Wei Sui and Hu Su and Junran Peng and Zhipeng Wang and Bin He},
journal={arXiv preprint arXiv:2510.15530},
year={2025}
}</code></pre>
</div>
</section>
<!--End BibTex citation -->
<footer class="footer">
<div class="container">
<div class="columns is-centered">
<div class="column is-8">
<div class="content">
<p>
This page was built using the <a href="https://github.com/eliahuhorwitz/Academic-project-page-template" target="_blank">Academic Project Page Template</a> which was adopted from the <a href="https://nerfies.github.io" target="_blank">Nerfies</a> project page.
You are free to borrow the source code of this website, we just ask that you link back to this page in the footer. <br> This website is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/" target="_blank">Creative
Commons Attribution-ShareAlike 4.0 International License</a>.
</p>
</div>
</div>
</div>
</div>
</footer>
<!-- Statcounter tracking code -->
<!-- You can add a tracker to track page visits by creating an account at statcounter.com -->
<!-- End of Statcounter Code -->
</body>
</html>