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新论文 · v1arXiv:2611.15443

SPARC: RoboCup Standard Platform for Autonomous Rescue Competition with Initial Datasets, Software, and Simulation Environments

Yuezhong Wang, Bichi Zhang, Zhongli Yin, Pingcong Li, Raimund Edlinger, Sören Schwertfeger

原文摘要

The RoboCup Standard Platform for Autonomous Rescue Competition (SPARC) is a new competition under development of the RoboCup Rescue Robot League that uses the Unitree A2-W as a common robot platform. SPARC aims to make research on artificial intelligence for autonomous rescue robots accessible to a broader research community, including groups without expertise in developing and maintaining custom robot hardware. By providing a capable standard platform and established benchmark environments, researchers can focus on autonomous navigation, perception, manipulation, and embodied AI for challenging disaster-response scenarios. This paper introduces the SPARC competition, its benchmark environment, and the Unitree A2-W standard platform. Furthermore, we present the first open SPARC datasets collected during the IEEE ICRA 2026 Legged Robot Challenges and RoboCup Rescue 2026, release an open-source software stack for autonomous navigation, and provide initial simulation assets representing RoboCup Rescue test environments. Together, these resources aim to lower the barrier to entry for autonomous rescue robotics and facilitate reproducible research and collaborative development on a common hardware platform.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15101

CFGComposer: Compositional Audio-Text-to-Motion Generation via Copula Score Correction

Pengyang Shi, Jinlu Zhang, Chunze Lin, Yizhou Wang, Li Yi

原文摘要

Generating whole-body gestures that are simultaneously rhythmically synchronized with speech audio and semantically aligned with text descriptions is a fundamental challenge in embodied AI and social robotics. We tackle this Audio-Text-to-Motion (AT2M) problem by composing two independently-trained diffusion models at inference time and refine bias in joint inference by modeling the copula score $s_I$. A rigorous theoretical analysis reveals that naive epsilon blending corresponds to a Product-of-Experts formulation that omits the copula score $s_I$: the cross-modal interaction between audio and text that neither marginal model can capture. We propose CFGComposer: a jointly-trained AT2M diffusion model that estimates $s_I$ via a CFG analogy and injects it as an $ε$-space correction. Experiments on BEAT demonstrate best-in-class semantic fidelity (SRGR $0.9916$, FID $27.18$) with GT-level beat alignment (BA $0.1826$ vs. GT $0.1743$), and 76-83% win rate in blind pairwise human evaluation against all prior methods. Physical deployment on a Unitree G1 humanoid confirms direct sim-to-real transfer.

cs.SD · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04424

ReflexArc: Bottom-Up Integration of Instantaneous Tactile Reflexes into Whole-Body Humanoid Control

Mingyu Wang, Si Li, Ziqing Chen, He Wang, Li Yi

原文摘要

Humanoid robots operating in cluttered environments must react to unexpected contacts before the first impact peak, which arrives within tens of milliseconds. Learned whole-body policies running at 50 Hz introduce a policy-cycle delay and may suppress a protective withdrawal as a tracking error. To address these challenges, we propose ARC (Ascending Reflex Control), a bottom-up framework that integrates instantaneous tactile reflexes with learned whole-body control. ARC consists of two components: an instantaneous reflex pathway and bottom-up reflex integration. The reflex pathway detects hazardous contacts from full-body tactile sensing, solves a whole-body withdrawal pose, and delivers it directly to the motors within 10 ms. Bottom-up reflex integration combines reflex-in-the-loop policy learning, reflex-transparent policy alignment, and reflex-informed policy adaptation to sustain the withdrawal, maintain balance, and complete longer-horizon recovery. We deploy ARC on a Unitree G1 and evaluate three categories of hazardous contact. Compared with an otherwise identical reflex delayed by 20 ms, ARC reduces the first force peak by 34.1% and reduces the contact impulse from 0.2632 N s to 0.1225 N s (a 2.15x ratio). We further demonstrate protected motion tracking, teleoperation, and vision-free obstacle traversal guided by foot tactile sensing. Project page: https://minusmodulo.github.io/reflexarc-website/

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.26015

SemantiScene-Edit: Expert-Synthesized Supervision and Dual-Path Source Conditioning for Instruction-Based Video Editing

Xinyu Chen, Song Wu, Dingbao Shao, Jiahang Li, Jiang Lin, Yuhang Liu, Duo Li, Jiaxin Hu 等 15 位作者

原文摘要

Instruction-guided video editing requires executing a requested change while preserving the source video's appearance and temporal structure. We present SemantiScene-Edit, a video editor that couples a multimodal encoder with a pretrained video diffusion transformer. The encoder jointly processes source frames and the editing instruction, and learnable queries convey their semantic context to the generator through a trainable connector and cross-attention. A complementary visual pathway concatenates source-video latents with noisy target latents before patch embedding, supplying dense spatial and temporal information for content preservation. We train the model with flow matching and a three-stage image--video curriculum comprising semantic alignment, joint adaptation, and high-resolution refinement. To support training, we construct over 1.2 million instruction-conditioned video-editing records covering object addition, removal, replacement, and attribute modification. We also construct a 100-case benchmark for evaluating instruction execution, source preservation, temporal consistency, and visual quality. Comparative experiments show that SemantiScene-Edit effectively follows editing instructions while retaining source content. At inference, the model requires only a source video and a natural-language instruction.

cs.AI · cs.GR

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arXiv ↗PDF ↗