cs.RO · 2026-09-23 · No. 122

Robotics, 2026-09-23.

7 new papers in cs.RO. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

7 entries
  1. 01

    MAVP: Map-Aware Visuomotor Policies for Mobile Manipulation

    Jinhe Tang, Ruixiao Dai, Weiming Zhi

    cs.RO · cs.AI · cs.CV · cs.LG

    Successful mobile manipulation requires coordinated base and arm motion while maintaining accurate spatial positioning. However, demonstration-trained policies can struggle to realise the intended base motion reliably, leading to spatial misalignment and subsequent manipulation failures. We present MAVP (Map-Aware Visuomotor Policies), a framework that improves execution reliability by predicting explicit base-pose targets and tracking them...

    arxiv.org/abs/2609.26378 · PDF

  2. 02

    TriWorldBench: A Tri-View Consistency Perspective on Embodied World Models

    Xuanyi Liu, Haofeng Wang, Ruiqi Li, Danni Yu, Rui Wan, Ruixu Zhang, Siyu Tao, Xue Yang, Shaofeng Zhang, Zicheng...

    cs.RO · cs.AI

    Embodied world models predict the outcomes of robot actions to support learning and planning. For robots equipped with head and wrist cameras, this requires complementary views: the head view captures the overall task, while wrist views reveal local gripper-object interactions. However, evaluating these views independently cannot determine whether they describe the same action and object state. We introduce TRIWORLDBENCH, a benchmark for...

    arxiv.org/abs/2609.26314 · PDF

  3. 03

    Skytopia: Monocular Drone Navigation with Action-Conditioned Latent World Models

    Yuhang Zhang, Rangya Zhang, Yujing Shang, Zhuoyuan Yu, Weiying Wang, Steven Yang, Qingsong Yan, Chao Yan, Mir Feroskhan

    cs.RO · cs.AI

    Monocular drone navigation requires reaching a goal in an unseen environment from a single forward-facing camera, which offers few cues for depth and scale. World models address this by modelling how observations evolve under actions, but they are built to be executed: the prediction is produced at deployment and fed back into action generation at every control step. We argue that what a policy needs from a world model is not the prediction...

    arxiv.org/abs/2609.26007 · PDF

  4. 04

    Destination Support Restoration for Finite-Set Multimodal Trajectory Prediction

    Fengrui Liu, Jiajun Peng, Duo Peng, Feng Liu

    cs.RO · cs.AI

    Robots operating around pedestrians often reason over a finite set of predicted human futures. Repeated online updates can concentrate this limited prediction budget on dominant destinations and leave plausible alternatives underrepresented or absent, removing those alternatives from the finite representation available to downstream decision making. We introduce Destination Support Restoration (DSR), a causal post-selection operator that...

    arxiv.org/abs/2609.25942 · PDF

  5. 05

    Beyond Reconstruction Error: Analytical and Data-Driven Action Tokenization for Autoregressive Vision-Language-Action Models

    Yuxin Yang, Gaohan He, Changxue Guan, Hangming Liu

    cs.RO · cs.LG

    Discrete action tokenization is central to autoregressive vision-language-action (VLA) models, yet action representations are often evaluated primarily through reconstruction fidelity. We ask which representation properties actually matter for closed-loop control by comparing fixed analytical, data-driven linear, and nonlinear neural representations under a unified tokenization interface. Across rate-distortion analysis, sequence-modeling...

    arxiv.org/abs/2609.25820 · PDF

  6. 06

    Teaching Reinforcement Learning and Humanoid Robotics to High-School Students: An Expert-Validated Curriculum Design on a Low-Cost Open Platform

    Yuanzhe Dong, Jie Cao, Shuman Wang

    cs.RO · cs.AI

    Lower cost open source robots and reinforcement learning (RL) simulation tools create new opportunities for precollege students to engage with contemporary robotics. However, translating a complete research workflow, spanning mechanical assembly, electrical setup, simulation, policy learning, system identification, and physical deployment, into a coherent course for novice learners remains challenging. We present an integrated robotics course...

    arxiv.org/abs/2609.25674 · PDF

  7. 07

    CODA: Depth-Aligned Scene Completion and Object Decomposition from a Single RGB-D Image

    Dongwon Son, Junhyek Han, Yoontae Cho, Minseok Lee, Hong-seok Choi, Jiwook Choi, Hyungjin Kim, Beomjoon Kim

    cs.RO · cs.CV · cs.LG

    Robots operating safely in cluttered everyday environments often need to infer scene geometry from partial observations. Methods that detect objects in 2D and reconstruct them independently struggle in such scenes: a missed object is never reconstructed, a merged detection can fuse two objects, and separately reconstructed meshes may overlap or fail to touch their supporting surfaces. We introduce CODA (Complete Once, Decompose Afterward), a...

    arxiv.org/abs/2609.25654 · PDF

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