论文标题
部分可观测时空混沌系统的无模型预测
Real Robot Challenge 2021: Cartesian Position Control with Triangle Grasp and Trajectory Interpolation
论文作者
论文摘要
我们为2021年真正的机器人挑战赛提供了亚军方法。我们基于2020年真正的机器人挑战中使用的先前方法。为了解决依次目标的任务，我们专注于实现近乎最佳轨迹的两个方面：抓握稳定性和控制器的性能。在RRC 2021模拟的挑战中，我们的方法依赖于手工设计的捏合掌握，并结合了轨迹插值，以在运动过程中提高稳定性，以进行快速目标。在第1阶段中，我们观察到恢复到三角形的抓握，与轨迹插值结合使用时，可能会提供更稳定的掌握，这可能是由于SIM2REAL间隙所致。可以在https://youtu.be/dloueoarwrm上获得我们方法的视频演示。该代码可在https://github.com/madan96/benchmark-rrc上公开获取。
We present our runner-up approach for the Real Robot Challenge 2021. We build upon our previous approach used in Real Robot Challenge 2020. To solve the task of sequential goal-reaching we focus on two aspects to achieving near-optimal trajectory: Grasp stability and Controller performance. In the RRC 2021 simulated challenge, our method relied on a hand-designed Pinch grasp combined with Trajectory Interpolation for better stability during the motion for fast goal-reaching. In Stage 1, we observe reverting to a Triangular grasp to provide a more stable grasp when combined with Trajectory Interpolation, possibly due to the sim2real gap. The video demonstration for our approach is available at https://youtu.be/dlOueoaRWrM. The code is publicly available at https://github.com/madan96/benchmark-rrc.
