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标题: "TCAM for Autonomous Deformable Manipulation: The RMC2 Champion System for WBCD 2026 Track 4"
原文链接: "https://arxiv.org/abs/2608.10718"
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## Computer Science > Robotics

## Title:TCAM for Autonomous Deformable Manipulation: The RMC2 Champion System for WBCD 2026 Track 4

Authors:[Guangrui Shen](https://arxiv.org/search/cs?searchtype=author&query=Shen,+G), [Zhili He](https://arxiv.org/search/cs?searchtype=author&query=He,+Z), [Shigang Wang](https://arxiv.org/search/cs?searchtype=author&query=Wang,+S), [Yuanjun Sun](https://arxiv.org/search/cs?searchtype=author&query=Sun,+Y), [Qing Yu](https://arxiv.org/search/cs?searchtype=author&query=Yu,+Q)

[View PDF](https://arxiv.org/pdf/2608.10718) [HTML (experimental)](https://arxiv.org/html/2608.10718v1)

> Abstract:This technical report describes the RMC2 Team's champion solution for the WBCD 2026 Track 4: Deformable Manipulation Challenge. The task requires a robot to pick a single T-shirt from a stack, load it onto a printing pallet, align the collar with a target area, and smooth the printing region, a sequence that involves single-layer separation, deformable transport, precise placement, and contact-rich surface adjustment. The competition strongly incentivizes fully autonomous execution, motivating the development of an autonomous solution. We built a fully autonomous system around the TCAM (TermiBrain Causal Action Model) framework, with the design principle that hardware, perception, data, and learning should jointly reduce the physical interaction complexity the policy must handle. A custom 3D-printed gripper designed for single-layer fabric separation improves picking reliability on a dual-arm ARX X5 platform. A wrist-centric four-camera setup pairs upper fisheye cameras for task-level context with lower RGB cameras for close-range gripper-cloth contact observation. We combine portable UMI-style demonstrations with real-robot demonstrations collected on the deployable platform to provide both broad manipulation priors and deployment-specific dynamics. TCAM ties these components into a closed loop: each trajectory is analyzed to identify the physical factors contributing to its outcome, driving targeted data recollection and policy fine-tuning. The policy outputs 30-step end-effector delta-pose action chunks from a multi-view VLA backbone. In the final competition, our system loaded 25 T-shirts at an average of approximately 23 seconds per attempt, with 22 achieving the required surface smoothness, securing first place in Track 4.

| Subjects: | Robotics (cs.RO) |
| --- | --- |
| Cite as: | [arXiv:2608.10718](https://arxiv.org/abs/2608.10718) \[cs.RO\] |
|  | (or [arXiv:2608.10718v1](https://arxiv.org/abs/2608.10718v1) \[cs.RO\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.10718](https://doi.org/10.48550/arXiv.2608.10718) |

## Submission history

From: Guangrui Shen \[[view email](https://arxiv.org/show-email/09b89026/2608.10718)\]  
**\[v1\]** Tue, 11 Aug 2026 09:35:39 UTC (2,092 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.10718) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
