cs.RO · 2026-08-18 · No. 88

Robotics, 2026-08-18.

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

    Don't Drop the BATON: Long-Horizon Robot Manipulation via Agentic Subtask Exploration and Transition-aware Memory

    Bingxin Xu, Yuzhang Shang, Emilio Ferrara

    cs.RO · cs.AI · cs.CV

    Long-horizon robot manipulation chains many contact-rich skills into one multi-stage task. Vision-language-action (VLA) models increasingly master the individual skills, yet the chain still fails: errors compound beyond the policy's ability to correct, and one subtask silently constrains the next. A promising recipe freezes the VLA and puts an LLM agent in charge: it plans in language, moves in free space with analytic primitives, invokes the...

    arxiv.org/abs/2608.16889 · PDF

  2. 02

    HAF: Adapting Generalist VLAs to Humanoid Whole-Body Loco-manipulation via Hierarchical Action Flow and Spectral Latent RL

    Langzhe Gu, Chengkai Hou, Meng Li, Xinhua Wang, Jiaming Liu, Xinyuan Lv, Bowei Zhang, Shuanghao Bai, Guangrun Li,...

    cs.RO · cs.AI

    Humanoid robots hold great promise as general-purpose agents in human-centered environments, yet generalist vision-language-action (VLA) foundation models are not readily applicable to humanoid whole-body loco-manipulation. The high dimensionality and interdependence of humanoid motions make it challenging for conventional single-stage VLA architectures to coordinate locomotion, waist posture, and dual-arm manipulation effectively. Moreover,...

    arxiv.org/abs/2608.16837 · PDF

  3. 03

    When State Becomes an Attack Surface: State-Semantic Injection in LLM-Driven Embodied Agents

    Jiawei Liu, Jiacheng Guo, Tian Zhang, Yiwei Xu, Juan Wang, Jinlin Fan, Bowen Xiao, Chi Guo, Keyan Guo, Hongxin Hu

    cs.RO · cs.AI

    Large Language Models (LLMs) have demonstrated capabilities in in-context learning, task decomposition, step-by-step reasoning, and code generation, driving their gradual evolution from text generation models into the core of agents capable of perceiving environments, invoking tools, and executing tasks. Traditional LLM Agents typically obtain information through webpages, documents, databases, or external tools and generate corresponding...

    arxiv.org/abs/2608.16806 · PDF

  4. 04

    Neurosymbolic Embodied Agents

    Mohammad Albinhassan, Yuming Feng, Alessandra Russo, Pranava Madhyastha

    cs.RO · cs.AI · cs.CL

    Language and vision-language models generate plausible embodied plans but do not guarantee executability, as their outputs can violate environment dynamics or act on incorrectly grounded entities. We present a neurosymbolic agent that factors long-horizon household tasks into task-directed visual exploration and constrained symbolic planning. In the first phase, a vision-language model and exploration harness acquire goal-relevant predicates...

    arxiv.org/abs/2608.16794 · PDF

  5. 05

    Orbit-Planner: Towards Latent World Models for On-Orbit Obstacle Avoidance of Satellite Agents

    Zhijian Li, Chao Ren, Peijin Wang, Xian Sun

    cs.RO · cs.AI

    Satellite agents for on-orbit navigation tasks need to predict collision risks using limited onboard observations. However, conventional planners often rely on predefined maps and fixed environmental assumptions, limiting their adaptability in dynamic on-orbit scenarios. In this paper, we propose Orbit-Planner, a two-stage latent world model for on-orbit obstacle avoidance. Orbit-Planner learns action-conditioned spacecraft dynamics to...

    arxiv.org/abs/2608.16651 · PDF

  6. 06

    NebulaVLA: A Dual-Frequency Vision-Language-Action Model With Guide Action for Robotic Manipulation

    Cong Zhao, Shuai Tian, Xu Zhang, Baocheng Ni, Xinguo Song, Xueying Sun, Shu Jiang, Shouchang Yang, Bo Tang, Jin...

    cs.RO · cs.AI

    Real-world deployment of Vision-Language-Action (VLA) models is often bottlenecked by efficiency-performance trade-offs, cross-embodiment generalization, and execution smoothness. We present NebulaVLA, an asynchronous dual-frequency architecture that decouples high-level semantic reasoning from low-level action control, optimizing computational resources and modularity. To bridge semantic gaps across heterogeneous robots, we introduce...

    arxiv.org/abs/2608.16503 · PDF

  7. 07

    HiPHI: A Large-Scale Benchmark for High-Precision Human Motion and Object-Interaction

    Jiahao Ji, Ji Ma, Runhan Zhang, Runyi Yu, Wenjia Wang, Weiheng Chi, Qianqian Peng, Weichao Yan, Yongfei Gu, Ye Tian,...

    cs.RO · cs.AI

    Humanoid intelligence requires learning over an extremely diverse space of whole-body motions and physically grounded interactions. However, existing embodied datasets remain fundamentally limited: internet-scale video data lack precise physical states and interaction grounding, while laboratory motion datasets provide high fidelity but only narrow behavioral coverage. This mismatch creates a critical bottleneck for scalable humanoid policy...

    arxiv.org/abs/2608.16222 · PDF

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