arXiv 2112.067522021-12-13cs.ROcs.AI

通过预测进行适应：多感觉主动推理扭矩控制

Adaptation through prediction: multisensory active inference torque control

Delft University of Technology（代尔夫特理工大学）；Radboud University（拉德堡德大学）

Cristian Meo, Giovanni Franzese, Corrado Pezzato, Max Spahn, Pablo Lanillos

备注：: arXiv admin note: text overlap with arXiv:2103.04412

摘要：

对于不确定环境中的机器人系统来说，适应外部和内部变化是主要的。在这里，我们提出了一种新的多传感器主动推理扭矩控制器的工业武器，显示如何预测可以用来解决适应。我们的控制器受到预测大脑假设的启发，通过结合低维和高维传感器输入的学习和多模态集成（例如，原始图像），同时简化架构。我们在7DoF Franka Panda机器人手臂上对我们的模型进行了系统的评估，将其行为与以前的主动推理基线和经典控制器进行比较，定性和定量地分析自适应能力和控制精度。结果表明，改进的控制精度在目标导向达到高噪声抑制由于多模态滤波，和适应性的动态惯性变化，弹性约束和人为干扰，而不需要重新学习的模型，也没有参数重新调整。

英文摘要：

Adaptation to external and internal changes is major for robotic systems in uncertain environments. Here we present a novel multisensory active inference torque controller for industrial arms that shows how prediction can be used to resolve adaptation. Our controller, inspired by the predictive brain hypothesis, improves the capabilities of current active inference approaches by incorporating learning and multimodal integration of low and high-dimensional sensor inputs (e.g., raw images) while simplifying the architecture. We performed a systematic evaluation of our model on a 7DoF Franka Emika Panda robot arm by comparing its behavior with previous active inference baselines and classic controllers, analyzing both qualitatively and quantitatively adaptation capabilities and control accuracy. Results showed improved control accuracy in goal-directed reaching with high noise rejection due to multimodal filtering, and adaptability to dynamical inertial changes, elasticity constraints and human disturbances without the need to relearn the model nor parameter retuning.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

摘要：

英文摘要：

相关论文