部分可观测条件下基于实时全形状估计的可变形物体操作
Deformable Object Manipulation under Partial Observability via Real-Time Full-Shape Estimation
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中文总结 AI 辅助
针对部分可观测下可变形物体操作难题，提出轻量级条件循环变分自编码器cRVAE，仅凭角节点观测实时估计全形状，并集成于滚动时域控制，在绳索和织物上精度媲美XPBD且速度大幅提升，已部署于Unitree Go2机器人。
中文摘要 AI 辅助
操作可变形物体（DOs）具有挑战性，因为其状态空间高维、动力学欠驱动且存在部分可观测性。本文提出cRVAE，一种轻量级条件循环变分自编码器，在推理时仅从部分角节点观测中估计完整的DO状态。所得模型被用作滚动时域最优控制框架中的前向模型，用于障碍感知的协作式DO操作。在绳索和织物的仿真中，cRVAE仅利用可用的角节点测量值即可估计完整的DO状态，其精度与参数已识别的XPBD模型相当。推理时，它不使用任何物理参数作为模型输入，也不进行在线参数识别。此外，在绳索上前向传播每次运行速度约快350倍，在织物上快1500倍以上，使得基于时域的规划保持在100毫秒的控制预算内，而XPBD在短时域内就已超出该预算。从角点感知进行全形状估计并达到循环速度，使该模型可部署于硬件，我们在一台Unitree Go2机器人上进行了演示。
英文摘要
Manipulating deformable objects (DOs) is challenging due to their high-dimensional state space, underactuated dynamics, and partial observability. In this paper, we propose cRVAE, a lightweight conditional recurrent variational autoencoder that estimates the full DO state from only partial corner-node observations during inference. The resulting model is used as the forward model in a receding-horizon optimal control framework for obstacle-aware collaborative DO manipulation. In simulation on rope and fabric, cRVAE estimates the full DO state from the available corner-node measurements alone, matching the accuracy of a parameter-identified XPBD model. At inference it uses no physical parameters as model inputs and performs no online parameter identification. It also runs approximately 350 times faster on the rope and over 1500 times faster on the fabric per forward pass, keeping horizon-based planning within the 100 ms control budget where XPBD exceeds it already at short horizons. Full-shape estimation from corner sensing at in-loop speed is what makes the model deployable on hardware, which we demonstrate on a Unitree Go2 robot.
