Sling2Sim2Real：用于无损弹弓策略学习的一次性弹性系统识别
Sling2Sim2Real: One-Shot Elastic System Identification for Non-Destructive Slingshot Policy Learning
- School of Computing, Korea Advanced Institute of Science and Technology（韩国科学技术院计算机学院）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
针对弹性物体操纵中校准现实与模拟弹性行为的挑战，提出Sling2Sim2Real框架；通过多起点Real2Sim系统识别方法及模拟策略学习等，从单次无损交互识别弹性参数，实现准确策略学习与稳健泛化，减少现实交互量。
中文摘要 AI 辅助
弹性物体操纵（EOM）涉及高维、非线性和弹性变形。弹性物体多样的变形特性极大地扩展了相关状态空间，对于弹弓操纵等任务，需要大量探索来学习准确的操纵策略。虽然模拟与现实世界试验相比能实现大规模且安全的探索，但校准现实世界与模拟之间的弹性行为仍具挑战。为此提出Sling2Sim2Real框架，它能从单次无损交互中识别弹性参数并在模拟中进行策略学习。该框架分两个阶段，一是利用参数协方差估计弹性属性的多起点Real2Sim系统识别方法，二是基于模拟的策略学习及使用校准模拟器的零射击Sim2Real转移。通过在弹弓操纵任务上的实验表明，该框架能实现准确的策略学习和稳健泛化，同时显著减少所需的现实世界交互量。
英文摘要
Elastic object manipulation (EOM) involves highdimensional, nonlinear, and elastic deformations. The diverse deformation properties of elastic objects substantially expand the relevant state space, requiring extensive exploration to learn accurate manipulation policies for tasks such as slingshot manipulation. While simulation enables large-scale and safe exploration compared to costly and potentially destructive real-world trials (e.g., repeated projectile launches), accurately calibrating elastic behavior between the real world and simulation remains challenging since elastic properties are largely indistinguishable from visual observations alone. To address these challenges, we propose Sling2Sim2Real, a one-shot Real2Sim2Real framework that identifies elastic parameters from a single non-destructive interaction and enables policy learning in simulation. The framework consists of two stages: 1) a multi-start Real2Sim system identification method that exploits parameter covariance to estimate elastic properties, and 2) simulation-based policy learning followed by zero-shot Sim2Real transfer using the calibrated simulator. We evaluate Sling2Sim2Real on a slingshot manipulation task using a Franka Emika Panda arm and elastic bands with diverse physical properties across varying target distances. Experimental results demonstrate that Sling2Sim2Real achieves accurate policy learning and robust generalization while significantly reducing the amount of required real-world interaction.
