ForwardDLO：无约束可变形线性物体的基于模型的双臂形状匹配
ForwardDLO: Model-Based Bimanual Shape Matching of Unconstrained Deformable Linear Objects
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中文总结 AI 辅助
针对无固定可变形线性物体的双臂形状控制，提出基于循环潜在动力学的ForwardDLO模型，预测每段位移，实现高效批量评估，在真实预测和模拟布线任务中显著优于基线。
中文摘要 AI 辅助
绳索、电缆及其他可变形线性物体出现在从解缠到电缆布线和缝合等任务中，但控制其形状仍然是机器人操作中的一个挑战。我们研究了一般设置下的基于模型形状控制：物体无固定地放置在支撑表面上，两只手臂可以沿其长度任意位置抓取和移动它。由于每只手臂选择抓取点、方向和幅度，联合动作空间在组合上很大，动力学模型的每次预测成本限制了规划器可以搜索的范围。我们提出了ForwardDLO，一种用于这种无固定双臂设置的循环潜在动力学模型，该模型在每一步基于观察到的绳索状态预测每个段的位移。我们的模型达到了与更昂贵的基线相当的精度，同时不包含显式的段到段操作，这使得候选动作的批量评估变得廉价。在真实绳索运动的开环预测中，它达到了我们评估的学习模型中的最低误差，比最强基线低13%。在固定的时间预算内，它评分的候选动作数量是精度相当模型的8到22倍，同时在真实世界形状匹配中与它们相当；在30Hz控制率的模拟布线任务中，这种吞吐量转化为98%的任务成功率，而基线在其自身预算下最多只有30%。我们发布了模型、代码以及包含242万模拟和14107个真实绳索转换的数据集，网址为https://this URL。
英文摘要
Ropes, cables, and other deformable linear objects appear in tasks from untangling to cable routing and suturing, yet controlling their shape remains a challenge in robot manipulation. We study model-based shape control in a general setting: the object lies unfixated on a support surface and two arms may grasp and move it anywhere along its length. Because each arm chooses a grasp point, direction, and magnitude, the joint action space is combinatorially large, and the dynamics model's per-prediction cost bounds how much of it a planner can search. We present ForwardDLO, a recurrent latent dynamics model for this unfixated bimanual setting that predicts per-segment displacements grounded in the observed rope state at every step. Our model reaches accuracy comparable to more expensive baselines while containing no explicit segment-to-segment operations, which makes batched evaluation of candidate actions cheap. On open-loop prediction of real rope motion it reaches the lowest error of the learned models we evaluate, 13% below the strongest baseline. Within a fixed time budget it scores 8 to 22 times more candidate actions than models of comparable accuracy while matching them in real-world shape matching; and on a simulated routing task at a 30Hz control rate, this throughput converts into 98% task success versus at most 30% for the baselines at their own budgets. We release the model, code, and a dataset of 2.42 million simulated and 14,107 real rope transitions at https://anonymous.4open.science/r/ForwardDLO/
发表机构
- Technical University of Darmstadt（达姆施塔特工业大学）
- Honda Research Institute Europe GmbH（本田欧洲研究院有限公司）
- Universidade Estadual de Campinas (UNICAMP)（坎皮纳斯州立大学）
- German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）
- Robotics Institute Germany (RIG)（德国机器人研究所）
- Centre for Cognitive Science（认知科学中心）
机构由 AI 辅助整理，请以论文原文为准。
