cs.RO · 2026-08-26 · No. 96

Robotics, 2026-08-26.

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

01 — The papers

4 entries
  1. 01

    NeuralParker: A Reinforcement Learning Planner for Irregular Parking Environments

    Zihan Wang, Bai Huang, Yang Guan, Xiao Li, Haoyu Xu, Naizheng Wang, Shengbo Eben Li

    cs.RO · cs.LG

    Automated parking commonly assumes marked slots and short approach maneuvers. Delivery and service vehicles, however, may need to reach an operator-specified pose in an irregular bounded environment from a distant start. Existing learning-based parking planners often rely on local observations, which can restrict long-range route reasoning. To address this problem, we present NeuralParker, a reinforcement learning-based hybrid planner for...

    arxiv.org/abs/2608.24485 · PDF

  2. 02

    PonderPounce: A Pretrained MLLM as an Episode Context Engine for Robot Control

    Suhwan Choi, Jaeyoon Jung, Sungkyung Kim, Yunsung Lee, Youngjae Yu

    cs.RO · cs.AI

    Multimodal large language models (MLLMs) can integrate long visual histories, reason under partial observability, and infer behavior from a few examples. Yet vision-language-action (VLA) models generally inherit pretrained representations without using this contextual capacity as episode memory. Memory-dependent policies address this gap through purpose-built history mechanisms. PonderPounce instead reuses an MLLM's native causal context as...

    arxiv.org/abs/2608.24115 · PDF

  3. 03

    Hierarchical Skill Retrieval for Data-Efficient Adaptation of Vision-Language-Action Models

    Haoran Hao, Shahram Najam Syed, Jeff Schneider, Jeffrey Ichnowski

    cs.RO · cs.AI · cs.LG

    While Vision-Language-Action (VLA) models pretrained on large-scale robot datasets provide a strong foundation for robot manipulation, their performance can degrade when adapted to new tasks with limited task-specific demonstrations. Retrieval offers a practical way to reuse existing demonstrations for data-efficient adaptation, but existing methods often rely on visual similarity, state-action representations, or task-level language...

    arxiv.org/abs/2608.24042 · PDF

  4. 04

    Design-to-Plan: A Large Language Model-Based Multi-Agent Framework for Manufacturing Process Planning from 3D CAD Models and 2D Engineering Drawings

    Muhammad Tayyab Khan, Lequn Chen, Wenhe Feng, Seung Ki Moon

    cs.RO · cs.AI

    Manufacturing process planning transforms heterogeneous design information into coherent manufacturing decisions. However, existing approaches focus on isolated subtasks, such as feature recognition, drawing interpretation, or tool selection, and struggle to support the full reasoning chain from design artifacts to process plans. This is critical when planning must interpret 3D CAD models, 2D engineering drawings, materials, and...

    arxiv.org/abs/2608.24039 · PDF

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