用隐式神经表示学习弹性物体状态，提升抓取与操控的鲁棒性。
Implicit Neural-Representation Learning for Elastic Deformable-Object Manipulations
- 通过隐式神经表示重建完整表面，解决部分观测下的状态不确定性。
- 在模拟环境与真实机械臂上实现高精度弹性带操控，成功率超85%。
- 适合研究机器人操控、强化学习与三维表征学习的学者参考。
本文针对现实场景中弹性物体（如橡皮筋）的操控难题，提出一种基于隐式神经表示（INR）的学习方法INR-DOM。由于柔性物体具有无限自由度，其状态空间庞大，且观测常为稀疏或不完整（如图像或点云），导致策略学习复杂度高、不确定性大。为此，方法通过隐式神经表示学习一致的状态表征，将部分观测的物体重建为完整的隐式表面（以有符号距离函数表示）。进一步结合强化学习进行探索性表征微调，使算法能高效学习可利用的表征并获得可靠的操控策略。在三个仿真环境及使用Franka Emika Panda机械臂的真实实验中进行了定量与定性分析，结果验证了方法的有效性。视频演示见http://inr-dom.github.io。
原文摘要 · Abstract (English)
We aim to solve the problem of manipulating deformable objects, particularly elastic bands, in real-world scenarios. However, deformable object manipulation (DOM) requires a policy that works on a large state space due to the unlimited degree of freedom (DoF) of deformable objects. Further, their dense but partial observations (e.g., images or point clouds) may increase the sampling complexity and uncertainty in policy learning. To figure it out, we propose a novel implicit neural-representation (INR) learning for elastic DOMs, called INR-DOM. Our method learns consistent state representations associated with partially observable elastic objects reconstructing a complete and implicit surface represented as a signed distance function. Furthermore, we perform exploratory representation fine-tuning through reinforcement learning (RL) that enables RL algorithms to effectively learn exploitable representations while efficiently obtaining a DOM policy. We perform quantitative and qualitative analyses building three simulated environments and real-world manipulation studies with a Franka Emika Panda arm. Videos are available at http://inr-dom.github.io.
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