KARL用卡尔曼滤波提升机械臂抓取动态物体的稳定性和成功率。
KARL: Kalman-Filter Assisted Reinforcement Learner for Dynamic Object Tracking and Grasping
- 六阶段强化学习课程扩大运动范围，提升抓取能力。
- 卡尔曼滤波层维持6D姿态估计，即使目标短暂消失或快速移动。
- 支持重试机制，自动恢复执行失败，适合真实场景部署。
我们提出卡尔曼滤波辅助强化学习器（KARL），用于眼在手（EoH）系统中对动态物体进行跟踪与抓取，显著提升了系统在复杂真实环境中的能力。相较于此前最先进方法，KARL（1）引入新颖的六阶段强化学习课程，使系统运动范围翻倍，大幅提高抓取性能；（2）在感知与强化学习控制模块间集成鲁棒卡尔曼滤波层，即使目标物体暂时离开摄像头视野或发生快速不可预测运动，仍能保持不确定但连续的6D姿态估计；（3）引入重试机制，实现对不可避免策略执行失败的优雅恢复。仿真与真实实验的广泛评估从定性与定量角度验证了KARL的优势，实现了更高的抓取成功率和更快的机器人执行速度。源代码与补充材料将发布于：https://github.com/arc-l/karl。
原文摘要 · Abstract (English)
We present Kalman-filter Assisted Reinforcement Learner (KARL) for dynamic object tracking and grasping over eye-on-hand (EoH) systems, significantly expanding such systems capabilities in challenging, realistic environments. In comparison to the previous state-of-the-art, KARL (1) incorporates a novel six-stage RL curriculum that doubles the system's motion range, thereby greatly enhancing the system's grasping performance, (2) integrates a robust Kalman filter layer between the perception and reinforcement learning (RL) control modules, enabling the system to maintain an uncertain but continuous 6D pose estimate even when the target object temporarily exits the camera's field-of-view or undergoes rapid, unpredictable motion, and (3) introduces mechanisms to allow retries to gracefully recover from unavoidable policy execution failures. Extensive evaluations conducted in both simulation and real-world experiments qualitatively and quantitatively corroborate KARL's advantage over earlier systems, achieving higher grasp success rates and faster robot execution speed. Source code and supplementary materials for KARL will be made available at: https://github.com/arc-l/karl.
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