-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathindex.html
More file actions
785 lines (734 loc) · 44.1 KB
/
index.html
File metadata and controls
785 lines (734 loc) · 44.1 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
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
<!DOCTYPE HTML>
<!--
Helios by HTML5 UP
html5up.net | @ajlkn
Free for personal and commercial use under the CCA 3.0 license (html5up.net/license)
-->
<html>
<head>
<title>CoPerception</title>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1, user-scalable=no" />
<link rel="stylesheet" href="assets/css/main.css" />
<noscript>
<link rel="stylesheet" href="assets/css/noscript.css" />
</noscript>
</head>
<body class="no-sidebar is-preload">
<div id="page-wrapper">
<!-- Header -->
<div id="header">
<!-- Inner -->
<div class="inner">
<header>
<h1><a href="index.html" id="logo">CoPerception: Collaborative Perception and Learning</a></h1>
<br />
<h2><a href="https://www.icra2023.org/" id="logo">ICRA 2023 workshop (South Gallery Room 25, Friday 2nd June 2023; Zoom recording will be available)</a></h2>
</header>
</div>
<!-- Nav -->
<nav id="nav">
<ul>
<li><a href="#abstract">Abstract</a></li>
<li><a href="#speakers">Invited Speakers</a></li>
<li><a href="#submission">Submission</a></li>
<li><a href="#topics">Topics</a></li>
<li><a href="#program">Program</a></li>
<li><a href="#organizers">Organizers</a></li>
</ul>
</nav>
</div>
<!-- Main -->
<div class="wrapper style1" id="abstract">
<div class="container">
<article id="main" class="special">
<header>
<h2>Abstract</h2>
</header>
<!-- Slideshow container -->
<p>
Perception, which involves organization, identification, and interpretation of sensory streams, has been a long-standing problem in robotics, and has been rapidly promoted by modern deep learning techniques.
Traditional research in this field generally lies in single-robot scenarios, such as object detection, tracking, and semantic/panoptic segmentation. However, single-robot perception suffers from long-range and
occlusion issues due to the limited sensing capability and dense traffic situations, and the imperfect perception could severely degrade the later planning and control modules.
</p>
<p>
Collaborative perception has been proposed to fundamentally solve the aforementioned problem, yet it is still faced with challenges including lack of real-world dataset,
extra computational burden, high communication bandwidth, and subpar performance in adversarial scenarios. To tackle these challenging issues and to promote more research in collaborative perception and learning,
this workshop aims to stimulate discussion on techniques that will enable better multi-agent autonomous systems with an emphasis on robust collaborative perception and learning methods,
perception-based multi-robot planning and control, cooperative and competitive multi-agent systems, and safety-critical connected autonomous driving.
</p>
<p>
In line with the ICRA 2023 Making Robots for Humans theme, this workshop will provide a venue for academics and industry practitioners to create a vision for connected robots to promote the safety and intelligence for humans.
The half-day workshop will feature presentations by distinguished speakers as well as interactive activities in the form of poster sessions and panel discussions.
</p>
</article>
</div>
</div>
<div id="speakers"></div>
<div class="container speakers" id="speakers2">
<article id="main" class="special">
<header>
<h2>Invited Speakers</h2>
</header>
</article>
</div>
<section class="carousel">
<div class="reel">
<article>
<a href="https://web.stanford.edu/~schwager/" class="image featured"><img
src="images/mac.jpg" alt="" /></a>
<header>
<h3>
<a href="https://web.stanford.edu/~schwager/"></a>Mac Schwager</a>
</h3>
<h3>
<a href="https://web.stanford.edu/~schwager/"></a>Stanford</a>
</h3>
</header>
<p>Multi-robot systems, distributed estimation</p>
</article>
<article>
<a href="https://engineering.nyu.edu/faculty/giuseppe-loianno"
class="image featured"><img src="images/giuseppe.jpg" alt="" /></a>
<header>
<h3>
<a href="https://engineering.nyu.edu/faculty/giuseppe-loianno"></a>Giuseppe Loianno
</h3>
<h3>
<a href="https://engineering.nyu.edu/faculty/giuseppe-loianno"></a>NYU</a>
</h3>
</header>
<p>Multi-robot perception, swarm robotics</p>
</article>
<article>
<a href="http://nicsefc.ee.tsinghua.edu.cn/" class="image featured"><img
src="images/yu.jpg" alt="" /></a>
<header>
<h3>
<a href="http://nicsefc.ee.tsinghua.edu.cn/"></a>Yu
Wang</a>
</h3>
<h3>
<a href="http://nicsefc.ee.tsinghua.edu.cn/"></a>Tsinghua University</a>
</h3>
</header>
<p>Multi-agent exploration, efficient DL</p>
</article>
<article>
<a href="https://www.cs.utexas.edu/~pstone/" class="image featured"><img
src="images/peter.png" alt="" /></a>
<header>
<h3>
<a href="https://www.cs.utexas.edu/~pstone/"></a>Peter Stone</a>
</h3>
<h3>
<a href="https://www.cs.utexas.edu/~pstone/"></a>UT Austin</a>
</h3>
</header>
<p>Machine learning, multiagent systems, and robotics</p>
</article>
<article>
<a href="http://feimiao.org/" class="image featured"><img src="images/fei.jpg"
alt="" /></a>
<header>
<h3>
<a href="http://feimiao.org/"></a>Fei Miao</a>
</h3>
<h3>
<a href="http://feimiao.org/"></a>UConn</a>
</h3>
</header>
<p>Connected and autonomous vehicles (CAVs)</p>
</article>
<article>
<a href="https://boleizhou.github.io"
class="image featured"><img src="images/bolei.jpeg" alt="" /></a>
<header>
<h3>
<a
href="https://boleizhou.github.io"></a>Bolei Zhou
</a>
</h3>
<h3>
<a
href="https://www.proroklab.org/"></a>UCLA
</a>
</h3>
</header>
<p>Interpretable human-AI interaction</p>
</article>
</div>
</section>
<div class="wrapper style1 organizers" id="speakers1">
<section class="container special">
<article id="main" class="special">
<header>
<h2>Invited Speakers</h2>
<p></p>
</header>
</article>
<div class="row" style="margin-left: 0px;">
<article>
<a href="https://web.stanford.edu/~schwager/" class="image featured"><img
src="images/mac.jpg" alt="" /></a>
<header>
<h3>
<a href="https://web.stanford.edu/~schwager/"></a>Mac Schwager</a>
</h3>
<h3>
<a href="https://web.stanford.edu/~schwager/"></a>Stanford</a>
</h3>
</header>
<p>Multi-robot systems, distributed estimation</p>
</article>
<article>
<a href="https://engineering.nyu.edu/faculty/giuseppe-loianno"
class="image featured"><img src="images/giuseppe.jpg" alt="" /></a>
<header>
<h3>
<a href="https://engineering.nyu.edu/faculty/giuseppe-loianno"></a>Giuseppe Loianno
</a>
</h3>
<h3>
<a href="https://engineering.nyu.edu/faculty/giuseppe-loianno"></a>NYU
</a>
</h3>
</header>
<p>Multi-robot perception, swarm robotics</p>
</article>
<article>
<a href="http://nicsefc.ee.tsinghua.edu.cn/" class="image featured"><img
src="images/yu.jpg" alt="" /></a>
<header>
<h3>
<a href="http://nicsefc.ee.tsinghua.edu.cn/"></a>Yu
Wang</a>
</h3>
<h3>
<a href="http://nicsefc.ee.tsinghua.edu.cn/"></a>Tsinghua University
</a>
</h3>
</header>
<p>Multi-agent exploration, efficient DL</p>
</article>
<article>
<a href="https://www.cs.utexas.edu/~pstone/" class="image featured"><img
src="images/peter.png" alt="" /></a>
<header>
<h3>
<a href="https://www.cs.utexas.edu/~pstone/"></a>Peter Stone</a>
</h3>
<h3>
<a href="https://www.cs.utexas.edu/~pstone/"></a>UT Austin</a>
</h3>
</header>
<p>Machine learning, multiagent systems, and robotics</p>
</article>
<article>
<a href="http://feimiao.org/" class="image featured"><img src="images/fei.jpg"
alt="" /></a>
<header>
<h3>
<a href="http://feimiao.org/"></a>Fei Miao</a>
</h3>
<h3>
<a href="http://feimiao.org/"></a>UConn</a>
</h3>
</header>
<p>Connected and autonomous vehicles (CAVs)</p>
</article>
<article>
<a href="https://boleizhou.github.io"
class="image featured"><img src="images/bolei.jpeg" alt="" /></a>
<header>
<h3>
<a
href="https://boleizhou.github.io"></a>Bolei Zhou
</a>
</h3>
<h3>
<a
href="https://www.proroklab.org/"></a>UCLA
</a>
</h3>
</header>
<p>Interpretable human-AI interaction</p>
</article>
</div>
</section>
</div>
<div class="wrapper style1" id="submission">
<div class="container" style="max-width: 800px;">
<article id="main" class="special">
<header>
<h2>Extended Abstract Submission
</h2>
</header>
<p>
We invite researchers working on related topics to submit abstracts or extended abstracts (no
longer than 4 pages in ICRA paper format, including references; you may add appendix after references) that can contribute to this
workshop. The accepted extended abstracts will be publicly available on this workshop website until the end of ICRA'23.
</p>
<p>Note: we DO allow previously published papers to be presented in this workshop,
because the accepted extended abstracts in this workshop will NOT be published in the ICRA'23 proceeding.
</p>
<p>
Desired Works could:
</p>
<ul>
<li>identify novel collaborative perception algorithms for outdoor or indoor robotics,</li>
<li>discuss multi-agent systems in the context of applications in autonomous driving, human-robot
interaction, or unmanned aerial vehicles,</li>
<li>demonstrate multi-robot communication efficiency,</li>
<li>describe novel perception-based multi-robot planning methods such as collaborative visual navigation or exploration,</li>
<li>review and benchmark various methods proposed by different communities (e.g., robotics, computer vision,
transportation) with the ultimate goal to enhance the mutual understanding of challenges and
opportunities related to this workshop.</li>
</ul>
<br />
<h3 style="text-align: center;">Important Dates</h3>
<br />
<div
style="width: 100%; display: flex; flex-direction: column; justify-content: center; align-items: center;">
<del>
<b>Extended Abstract Submission (send to coperception.icra2023@gmail.com):</b>
<p> May 7, 2023, 11:59PM PDT.
</p>
<b>Extended Abstract Acceptance:</b>
<p> May 14, 2023, 11:59PM PDT.
</p>
<b>Final Version Submission:</b>
<p> May 21, 2023, 11:59PM PDT.
</p>
</del>
<br />
</div>
<br />
<h3 style="text-align: center;">Best Paper Awards (Sponsored by IEEE RAS TC for Computer & Robot Vision)</h3>
<br />
<div
style="width: 100%; display: flex; flex-direction: column; justify-content: center; align-items: center;">
<b>First Prize</b>
<p> $150
</p>
<b>Second Prize</b>
<p> $100
</p>
<b>Third Prize</b>
<p> $50
</p>
</div>
<!-- <br />
<h3 style="text-align: center;">Important Dates</h3>
<br /> -->
<!-- <div
style="width: 100%; display: flex; flex-direction: column; justify-content: center; align-items: center;"> -->
<!-- <del>
<b>Extended Abstract Submission (submit via <a href="https://forms.gle/WghcECDqFm4VowSA9">this Google Form</a>):</b>
<p> August 31, 2021, 5PM eastern
time.
</p>
<b>Extended Abstract Acceptance:</b>
<p> September 07, 2021, 5PM eastern
time.
</p>
<b>Final Version Submission:</b>
<p> September 21, 2021, 5PM eastern time.
</p>
<br />
</del> -->
<!-- <b>Special Issue (Full Manuscript, see below) Submission:</b>
<p> September 31, 2021,
5PM
eastern time.
</p>
</div> -->
<!-- <br />
<h3 style="text-align: center;">Associated Special Issue</h3>
<br />
<p>The organizers are currently guest editing a special issue on <a
href="https://www.frontiersin.org/research-topics/19049/soft-robot-state-estimation"
style="font-weight: bold;">the same research topic </a>. The potential authors would
also be invited to participate in this workshop. Authors of the selected abstracts would be
invited to submit the full version of their works to this special issue.</p> -->
</article>
</div>
</div>
<div class="wrapper style1" id="topics">
<div class="container">
<article id="main" class="special">
<header>
<h2>Topics of Interest
</h2>
</header>
<div style="display:flex; justify-content: center;">
<ul>
<li>Collaborative perception (detection, segmentation, tracking, motion forecasting, etc.)</li>
<li>Communication-efficient collaborative perception</li>
<li>Robust collaborative perception (latency / pose errors)</li>
<li>Collaborative embodied AI</li>
<li>Representation learning in multi-agent systems</li>
<li>Adversarial learning in multi-agent perception</li>
<li>Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I)</li>
<li>Connected and autonomous vehicles (CAVs)</li>
<li>Intelligent transportation systems</li>
<li>Smart cities</li>
<li>Multi-robot systems and swarm systems</li>
<li>Multi-robot exploration and mapping</li>
<li>Distributed optimization</li>
<li>Efficient large-scale collaborative learning</li>
<li>Edge AI and federated learning</li>
<li>Cooperative and competitive multi-agent systems</li>
<li>Simulation for multi-agent learning</li>
<li>Dataset and benchmarking for collaborative perception and learning</li>
</ul>
</div>
</article>
</div>
</div>
<div class="wrapper style1" id="program">
<div class="container">
<article id="main" class="special">
<header>
<h2>Program (Friday 2nd June 2023)
</h2>
</header>
<div style="width: 100%; display: flex; justify-content: center;">
<table class="tg">
<thead>
<tr>
<th class="tg-a0ej"><span style="background-color:#F2F2F2"><b>Time in London</b></th>
<th class="tg-a0ej"><span style="background-color:#F2F2F2"><b>Talk in South Gallery Room 25</b></th>
</tr>
</thead>
<tbody>
<tr>
<td class="tg-lboi">08:45 – 08:50 </td>
<td class="tg-rpart">Welcome / Introductions </td>
</tr>
<tr>
<td class="tg-lboi">08:50 – 09:15 </td>
<td class="tg-rpart"><a style="font-weight: bold;">Distributed Models and Representations for Robot Collective Intelligence, Giuseppe Loianno</a>
<ul>
<li>Abstract: In this talk, I will discuss recent techniques to address fast distributed multi-vehicle perception, planning, and control problems to increase situational of each member of the team as well as robustness, adaptation, and resilience with respect to robots’ and/or sensors’ failures and interaction with humans with limited of absence of communication. Specifically, by combining learning-based and physics-based techniques it is possible to achieve collaborative and shared autonomy tasks that address a wide range of problems such as search and rescue, monitoring, construction, and transportation.</li>
</ul>
</td>
</tr>
<tr>
<td class="tg-lboi">09:15 – 09:40 </td>
<td class="tg-rpart"><a style="font-weight: bold;">Learning and Control for Safety, Efficiency, and Resiliency of Cyber-Physical Systems, Fei Miao</a>
<ul>
<li>Abstract: The rapid evolution of ubiquitous sensing, communication, and computation
technologies has contributed to the revolution of cyber-physical systems (CPS). Learning-based
methodologies are integrated to the control of physical systems and provide tremendous
opportunities for AI-enabled CPS. However, existing networked CPS decision-making
frameworks lack understanding of the tridirectional relationship among communication, learning
and control. It remains challenging to leverage the communication capability for the learning and
control methodology design of CPS, to improve the safety, efficiency, and robustness of the
system. In the talk, we will present our research contributions on learning and control with
information sharing for networked CPS. We design the first uncertainty quantification method for
collaborative perception of connected autonomous vehicles (CAVs) and show the accuracy
improvement and uncertainty reduction performance of our method. To utilize the information
shared among agents, we then develop a safe and scalable deep multi-agent reinforcement
learning (MARL) algorithms to improve system safety and efficiency. We validate the benefits of
communication in MARL especially for CAVs under challenging mixed traffic scenarios. To
motivate agents to communicate and coordinate, we design a novel stable and efficient Shapley
value-based reward reallocation scheme for MARL. Finally, considering the complicated system
dynamics and state information uncertainties from sensors and learning-based perception of
networked CPS, we present our contribution to robust MARL methods, including formal analysis
on the solution concept of MARL under state uncertainties and state perturbations.</li>
</ul>
</td>
</tr>
<tr>
<td class="tg-lboi">09:40 – 10:05 </td>
<td class="tg-rpart"><a style="font-weight: bold;">MetaDriverse: Simulating Digital Twins of Real-World Traffic Scenarios for AI Safety, Bolei Zhou</a>
<ul>
<li>Abstract: Autonomous driving (AD) powered by AI is an emerging technology that revolutionizes mobility and transportation. However, it remains difficult to ensure the AI safety when driving in a wide range of complex real-world situations. To tackle this, driving simulation platform becomes a stepping stone for evaluating AD systems. In this case, diverse and realistic traffic scenarios that reflect the real-world complexity are crucial for evaluating the AI safety in simulation. I will introduce our effort of building the MetaDrivese platform to manage and simulate more than 1 million different traffic scenarios. This platform can import HD maps and replay real-world vehicle trajectories as well as learning to generate novel ones. It can substantially improve the realism and diversity of the traffic scenarios in simulation as well as thorougly evaluating the decision-making and AI safety of AD systems. Recent progress of MetaDriverse platform is available at https://metadriverse.github.io/.</li>
</ul>
</td>
</tr>
<tr>
<td class="tg-lboi">10:05 – 10:35 </td>
<td class="tg-rpart"><a style="font-weight: bold;">Panel Discussion</a>
<ul>
<li>If you have any topic related to coperception you’d like to propose to discuss, please send your proposed question directly to <a href="mailto:coperception.icra2023@gmail.com">coperception.icra2023@gmail.com</a>.</li>
</ul>
</td>
</tr>
<tr>
<td class="tg-lboi">10:45 – 11:10 </td>
<td class="tg-rpart"><a style="font-weight: bold;">Coopernaut: End-to-End Driving with Cooperative Perception for Networked Vehicles, Peter Stone</a>
<ul>
<li>Abstract: Optical sensors and learning algorithms for autonomous vehicles have dramatically advanced in the past few years. Nonetheless, the
reliability of today's autonomous vehicles is hindered by the limited line-of-sight sensing capability and the brittleness of data-driven
methods in handling extreme situations. With recent developments of telecommunication technologies, cooperative perception with
vehicle-to-vehicle communications has become a promising paradigm to enhance autonomous driving in dangerous or emergency situations. We
introduce COOPERNAUT, an end-to-end learning model that uses cross-vehicle perception for vision-based cooperative driving. Our model
encodes LiDAR information into compact point-based representations that can be transmitted as messages between vehicles via realistic wireless
channels. To evaluate our model, we develop AUTOCASTSIM, a network-augmented driving simulation framework with example
accident-prone scenarios. Our experiments on AUTOCASTSIM suggest that our cooperative perception driving models lead to a 40% improvement in
average success rate over egocentric driving models in these challenging driving situations and a 5x smaller bandwidth requirement than prior work V2VNet.</li>
</ul>
</td>
</tr>
<tr>
<td class="tg-lboi">11:10 – 11:35 </td>
<td class="tg-rpart"><a style="font-weight: bold;">Applications of Distributed Optimization in Multi-robot Systems, Mac Schwager</a>
<ul>
<li>Abstract: Distributed optimization is are an expressive and powerful optimization paradigm that allows for principled translation of single robot tasks to the multi-robot domain. This talk centers on how teams of connected robots can leverage distributed optimization algorithms to perform collaborative estimation and learning.</li>
</ul>
</td>
</tr>
<tr>
<td class="tg-lboi">11:35 – 12:00 </td>
<td class="tg-rpart"><a style="font-weight: bold;">Collaborative multi-robot exploration: fundamental technology and applications, Yu Wang</a>
<ul>
<li>Abstract: With the advancement of individual agent capabilities, collaboration between multiple agents has become possible. Compared to single-agent intelligence, multi-agent collaborative intelligence has a larger perception range of the environment, stronger action capabilities, and can further improve system efficiency. However, multi-agent systems face challenges such as resource limitations in communication, perception, data, and computation. To address these challenges, research needs to be conducted in communication systems, computation systems, collaborative perception and decision-making algorithms. This report focuses on the collaborative multi-robot exploration under resource-limited conditions and showcases the research team's achievements in collaborative mapping and exploration systems in unknown environments with communication limitations, as well as adaptive multi-task decision-making algorithms.</li>
</ul>
</td>
</tr>
<tr>
<td class="tg-lboi">12:00 – 12:30 </td>
<td class="tg-rpart"><a style="font-weight: bold;"> Presentations of Workshop Papers</a>
<ul>
<li> Jan Blumenkamp, Qingbiao Li, Binyu Wang, Zhe Liu, and Amanda Prorok (University of Cambridge). <i>See What the Robot Can’t See: Learning Cooperative Perception for Visual Navigation</i>. <a href="https://drive.google.com/file/d/1Aa20H4rLBJJM8o9hTE9Ka3c5lRDUSMDu/view?usp=share_link">[PDF]</a> </li>
</ul>
<ul>
<li> Nathaniel Moore Glaser and Zsolt Kira (Georgia Tech). <i>We Need to Talk: Identifying and Overcoming Communication-Critical Scenarios for Self-Driving</i>. <a href="https://drive.google.com/file/d/1BEA5NZfWHHTbqhUWqj7_1bPnZTsIbApW/view?usp=share_link">[PDF]</a> </li>
</ul>
<ul>
<li> Arash Asgharivaskasi and Nikolay Atanasov (UCSD). <i>Distributed Optimization with Consensus Constraint for Multi-Robot Semantic Octree Mapping</i>. <a href="https://drive.google.com/file/d/1_NtKcbPY0qJE2Cj-mCbZHy4IVcpzr0dt/view?usp=share_link">[PDF]</a> </li>
</ul>
<ul>
<li> Giuliano Albanese, Arka Mitra, Jan-Nico Zaech, Yupeng Zhao, Ajad Chhatkuli, and Luc Van Gool (ETH Zurich and KU Leuven). <i>Optimizing Long-Term Player Tracking and Identification in NAO Robot Soccer by fusing Game-state and External Video</i>. <a href="https://drive.google.com/file/d/15eFCqXz_JG7c2OkwE-PCwGJnvYYQXtU7/view?usp=share_link">[PDF]</a> </li>
</ul>
<ul>
<li> Rui Song, Lingjuan Lyu, Wei Jiang, Andreas Festag, and Alois Knoll (Fraunhofer Institute for Transportation and Infrastructure Systems IVI, TUM, Sony AI, DFKI, and Technische Hochschule Ingolstadt). <i>V2X-Boosted Federated Learning for Cooperative Intelligent Transportation Systems with Contextual Client Selection</i>. <a href="https://drive.google.com/file/d/1-OQAeNryd7-lksCTuWnhhyydpFSnw5On/view?usp=share_link">[PDF]</a> </li>
</ul>
<ul>
<li> Chelsea Zou, Kishan Chandan, Yan Ding, and Shiqi Zhang (Binghamton University). <i>ARDIE: AR, Dialogue, and Eye Gaze Policies for Human-Robot Collaboration</i>. <a href="https://drive.google.com/file/d/1A7flsqhYkSfTk-VogiTUukor61SG8YzL/view?usp=share_link">[PDF]</a> </li>
</ul>
<ul>
<li> Sebin Lee, Woobin Im, and Sung-Eui Yoon (KAIST). <i>Multi-Resolution Distillation for Self-Supervised Monocular Depth</i>. <a href="https://drive.google.com/file/d/1-us7q2o_7Rh1cTSnsviyi3653H7apmsZ/view?usp=share_link">[PDF]</a> </li>
</ul>
</td>
</tr>
</tbody>
</table>
</div>
</article>
</div>
</div>
<div class="wrapper style1 organizers" id="organizers">
<section class="container special">
<header>
<h2>Organizers</h2>
<p></p>
</header>
<div class="row" style="margin-left: 0px;">
<article class="col-3 col-12-mobile special">
<a href="https://engineering.nyu.edu/faculty/chen-feng" class="image featured"><img
src="images/Chen Feng.jpeg" alt="" /></a>
<header>
<h3><a href="https://engineering.nyu.edu/faculty/chen-feng">Chen Feng</a></h3>
<h3><a href="https://engineering.nyu.edu/faculty/chen-feng">NYU</a></h3>
</header>
<p style="text-align: center;">
cfeng@nyu.edu
</p>
</article>
<article class="col-3 col-12-mobile special">
<a href="https://mediabrain.sjtu.edu.cn/sihengc/" class="image featured"><img
src="images/siheng.jpeg" alt="" /></a>
<header>
<h3><a href="https://mediabrain.sjtu.edu.cn/sihengc/">Siheng Chen</a></h3>
<h3><a href="https://mediabrain.sjtu.edu.cn/sihengc/">SJTU</a></h3>
</header>
<p style="text-align: center;">
sihengc@sjtu.edu.cn
</p>
</article>
<article class="col-3 col-12-mobile special">
<a href="https://samueli.ucla.edu/people/jiaqi-ma/" class="image featured"><img
src="images/jiaqi.jpeg" alt="" /></a>
<header>
<h3><a href="https://samueli.ucla.edu/people/jiaqi-ma/">Jiaqi Ma</a></h3>
<h3><a href="https://samueli.ucla.edu/people/jiaqi-ma/">UCLA</a>
</h3>
</header>
<p style="text-align: center;">
jiaqima@ucla.edu
</p>
</article>
<!-- <article class="col-3 col-12-mobile special">
<a href="https://www.baecher.info/" class="image featured"><img src="images/Moritz Bächer.jpeg"
alt="" /></a>
<header>
<h3><a href="https://www.baecher.info/">Moritz Bächer</a></h3>
<h3><a href="https://www.baecher.info/">Disney Research</a></h3>
</header>
<p style="text-align: center;">
moritz.baecher@disney.com
</p>
</article> -->
</div>
</section>
<section class="container special">
<header>
<h2>Student Organizers</h2>
<p></p>
</header>
<div class="row" style="margin-left: 0px;">
<article class="col-3 col-12-mobile special">
<a href="https://roboticsyimingli.github.io/" class="image featured"><img
src="images/yiming.jpeg" alt="" /></a>
<header>
<h3><a href="https://roboticsyimingli.github.io/">Yiming Li</a></h3>
<h3><a href="https://roboticsyimingli.github.io/">NYU</a></h3>
</header>
<p style="text-align: center;">
yimingli@nyu.edu
</p>
</article>
<article class="col-3 col-12-mobile special">
<a href="https://scholar.google.com/citations?hl=en&user=XBbwb78AAAAJ" class="image featured"><img
src="images/yue.jpg" alt="" /></a>
<header>
<h3><a href="https://scholar.google.com/citations?hl=en&user=XBbwb78AAAAJ">Yue Hu</a></h3>
<h3><a href="https://scholar.google.com/citations?hl=en&user=XBbwb78AAAAJ">SJTU</a></h3>
</header>
<p style="text-align: center;">
phyllis1sjtu@outlook.com
</p>
</article>
<article class="col-3 col-12-mobile special">
<a href="https://derrickxunu.github.io/" class="image featured"><img
src="images/runsheng.jpg" alt="" /></a>
<header>
<h3><a href="https://derrickxunu.github.io/">Runsheng Xu</a></h3>
<h3><a href="https://derrickxunu.github.io/">UCLA</a>
</h3>
</header>
<p style="text-align: center;">
rxx3386@ucla.edu
</p>
</article>
<!-- <article class="col-3 col-12-mobile special">
<a href="https://www.baecher.info/" class="image featured"><img src="images/Moritz Bächer.jpeg"
alt="" /></a>
<header>
<h3><a href="https://www.baecher.info/">Moritz Bächer</a></h3>
<h3><a href="https://www.baecher.info/">Disney Research</a></h3>
</header>
<p style="text-align: center;">
moritz.baecher@disney.com
</p>
</article> -->
</div>
</section>
</div>
<div class="wrapper style1" id="topics">
<div class="container">
<article id="main" class="special">
<header>
<h2>Acknowledgement
</h2>
</header>
<div style="display:flex; justify-content: center;">
<ul>
<li>IEEE RAS TC for Computer & Robot Vision</li>
<li>IEEE RAS TC on Multi-Robot Systems</li>
<li>IEEE RAS Autonomous Ground Vehicles and Intelligent Transportation Systems TC</li>
</ul>
</div>
</article>
</div>
</div>
</div>
<!-- Footer -->
<div id="footer">
<div class="container">
<div class="row">
<div class="col-12">
<!-- Copyright -->
<div class="copyright">
<div style="height: 100px; position: relative;">
<div style="width: 150px; margin-left: auto; margin-right: auto;">
<script type="text/javascript" src="//rf.revolvermaps.com/0/0/8.js?i=5re4zbga5f3&m=0&c=ff0000&cr1=ffffff&f=arial&l=33" async="async"></script>
</div>
</div>
<div>
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/"><img
alt="Creative Commons License" style="border-width:0"
src="https://i.creativecommons.org/l/by/4.0/88x31.png" /></a><br />This work is
licensed under a <a rel="license"
href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0
International License</a>.
</div>
<div>
Powered by @html5up. Presented by <a href="https://eveshi.com">@Eve Shi</a>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
<!-- Scripts -->
<script src="assets/js/jquery.min.js"></script>
<script src="assets/js/jquery.dropotron.min.js"></script>
<script src="assets/js/jquery.scrolly.min.js"></script>
<script src="assets/js/jquery.scrollex.min.js"></script>
<script src="assets/js/browser.min.js"></script>
<script src="assets/js/breakpoints.min.js"></script>
<script src="assets/js/util.js"></script>
<script src="assets/js/main.js"></script>
<script>
var slideIndex = 0;
showSlides();
var slides, dots;
function showSlides() {
var i;
slides = document.getElementsByClassName("mySlides");
dots = document.getElementsByClassName("dot");
for (i = 0; i < slides.length; i++) {
slides[i].style.display = "none";
}
slideIndex++;
if (slideIndex > slides.length) { slideIndex = 1 }
for (i = 0; i < dots.length; i++) {
dots[i].className = dots[i].className.replace(" active", "");
}
slides[slideIndex - 1].style.display = "block";
dots[slideIndex - 1].className += " active";
setTimeout(showSlides, 2500); // Change image every 2.5 seconds
}
function plusSlides(position) {
slideIndex += position;
if (slideIndex > slides.length) { slideIndex = 1 }
else if (slideIndex < 1) { slideIndex = slides.length }
for (i = 0; i < slides.length; i++) {
slides[i].style.display = "none";
}
for (i = 0; i < dots.length; i++) {
dots[i].className = dots[i].className.replace(" active", "");
}
slides[slideIndex - 1].style.display = "block";
dots[slideIndex - 1].className += " active";
}
function currentSlide(index) {
if (index > slides.length) { index = 1 }
else if (index < 1) { index = slides.length }
for (i = 0; i < slides.length; i++) {
slides[i].style.display = "none";
}
for (i = 0; i < dots.length; i++) {
dots[i].className = dots[i].className.replace(" active", "");
}
slides[index - 1].style.display = "block";
dots[index - 1].className += " active";
}
</script>
</body>
</html>