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新论文 · v1arXiv:2611.15981

DexRemo: A Force-Sensing Hand Platform and Task Suite for Contact-Rich Removal Manipulation

Liman Wang, Hanyang Zhong, Sun Zhaole, Wenting Cao, Babar Jamil, Andy Tyrrell, Shan Luo, Jihong Zhu

原文摘要

Many manipulation tasks require separating adhered, embedded, contaminated, or mechanically constrained targets. We study this setting as removal manipulation, where progress depends on evolving contact states that are only partially observable from vision and kinematics. We introduce DexRemo, a three-finger robotic hand with high-force actuation, modular fingertips, and three-axis fingertip force sensing, together with a nine-scenario real-robot task suite spanning adhesive detachment, surface cleaning and self-cleaning, and occluded retrieval. To make multi-finger force observations easier to fuse, we express fingertip forces in a shared wrist-centered frame, giving force components consistent geometric meaning across fingers and time. Using an Action Chunking Transformer backbone, policies fuse multi-view vision, proprioception, fingertip pose, and force observations to predict closed-loop action chunks from demonstrations. Evaluation uses six core benchmark tasks for controlled fingertip-input and force-frame ablations, plus three extension scenarios for additional held-out coverage. Across these evaluations, the learned policies achieve consistent removal performance, and ablations show that wrist-frame forces, especially with fingertip pose, improve stability and generalization over local-frame force inputs and no-force baselines. Project website: https://dexremo.github.io

cs.RO · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15131

Duo: A Reciprocal World Model for Full-Duplex Interaction

Hengyuan Cao, Biao Gong, Guoshan Lu, Qiyuan Zhang, Lin Liu, Yiding Tian, Yilei Jiang, Yuxin Ma 等 20 位作者

原文摘要

In this report, we introduce the Reciprocal World Model (RWM) and present Duo v1, a full-duplex world model that continuously perceives audiovisual inputs while responding to users in real time. The RWM treats intelligence and physical reality as equally fundamental to what constitutes a world. Existing world models largely focus on simulating the latter, leaving reasoning and decision-making as external controls rather than intrinsic parts of the world itself. In RWM, we identify two equally fundamental capabilities: Intent, which provides the cognition underlying world interactions, and Realization, which generates coherent audio-visual behavior. To realize this formulation, Duo v1 introduces three key technical designs: (1) Leveraging autoregressive modeling for continuous interaction reasoning and participation decisions, rather than restricting it to instruction understanding for diffusion models. (2) Constructing chunk-specific conditions and synchronize tokens across Intent and Realization on a unified timeline. Module-specific attention masks enforce causal dependencies both within and across chunks, preventing access to future inputs. (3) Mitigating distribution shift in both modules through dual rollout distillation, training on autoregressively generated Intent tokens and synthesized audiovisual histories from Realization. Both modules continuously perceive real-time audio, video, and text, enabling full-duplex interaction with spontaneous conversations and interruptions. Beyond conversation, users can share real objects, reshape virtual scenes, guide assistants, or play audiovisual games without mouse or keyboard controls. The release of Duo v1 makes our first step, with the next version coming soon. We look forward to broader exploration of this emerging field.

cs.CV · cs.SD

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09118

Semantic Extraction Framework for Goal-Oriented V2X Communication Supporting Safe Intersection Passage

Haoran Yang, Shan Lu, Takaya Yamazato

原文摘要

This paper proposes a transmitter-side semantic extraction framework for goal-oriented Vehicle-to-Everything (V2X) communication supporting safe intersection passage. Instead of transmitting raw sensory data, roadside infrastructure extracts task-relevant semantic information from video and traffic-signal sequences and organizes them into compact semantic packets for low-latency driving-decision assistance. The intersection-passing problem is formulated as a set of traffic tasks under Japanese traffic regulations, and the proposed pipeline generates perception and intent semantics through object detection, object tracking, region-of-interest (ROI)-based semantic extraction, and recommended-action generation. The framework is implemented and evaluated on a self-constructed, CARLA-based single-vehicle dataset collected under clear weather conditions. Experimental results show high recommended-action accuracy within the tested task set, while the mean processing latency remains below the frame interval of the 15~fps input video.

eess.IV · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07939

WAM-Sim0.1: World Anchored Memory Meets Online RL for Consistent and Realistic Driving Simulation

Jiahao Cui, Jiaye Li, Wenhao Zhang, Yaoyi Li, Shan Luan, Quanhui Tang, Jianhua Han, Lin Cheng 等 10 位作者

原文摘要

Autoregressive driving world models systematically suffer from structural drift, identity drift, and quality decay over long rollout horizons due to transient conditioning signals that lack persistent state. We present WAM-Sim, a driving world model anchored by a persistent hierarchical memory and optimized via online flow-matching RL. WAM-Sim structures persistent context across three temporal and semantic scales: pose-aligned structural tokens encoding persistent road topology, read-only scene tokens supplying geolocated background appearance, and write-once instance tokens preserving individual traffic participant identities across occlusions and chunk boundaries. To restore visual sharpness and physical plausibility degraded by multi-scale conditioning, we introduce an online post-training framework that optimizes self-generated rollouts via flow-matching RL guided by hierarchy-aligned visual rewards and a vision-language model physical judge. The invariant memory empirically confines policy optimization within the anchored context, helping suppress semantic collapse and reward hacking, while online RL systematically enforces physical commonsense and fine-grained visual fidelity. On the nuScenes benchmark, WAM-Sim achieves a superior generation fidelity (FID=3.47, FVD=25.72) and strong downstream perception performance (24.16 mAP, 71.23 road mIoU). On minute-scale rollouts of self-collected data, WAM-Sim substantially mitigates structural and identity drift, preserving high scene similarity and subject identity coherence across extended horizons. Project page: https://fudan-generative-vision.github.io/WAM-Sim.

cs.CV

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arXiv ↗PDF ↗