公司简介
计算学习和运动控制实验室的研究重点是用于感觉运动控制和学习的神经计算领域。神经计算试图将生物学知识与物理学和工程学知识结合起来，以便对复杂系统中的信息处理形成更基本和更正式的理解。一方面，目标是通过研究生物行为和信息处理的原理来发现新技术，认识到即使是简单的生物系统也能实现远优于人工系统的感觉运动能力和计算自组织。另一方面，生物信息处理的形式化将同样有助于我们对神经系统组织的认识的进步，并随后有助于医学和临床诊断和治疗的新方法的发展，包括神经假体装置。我们研究的一部分是关于神经网络、统计学习和机器学习中的学习，因为学习和自组织的能力似乎是自治系统最重要的先决条件之一。该研究项目的另一部分侧重于如何产生运动，特别是在具有身体、四肢和眼睛的类人系统中。本研究涉及控制理论、非线性控制、非线性动力学、优化理论和强化学习等领域。在第三个研究分支中，我们通过测量他们在特殊设计的行为任务中的运动来调查人类的表现，也通过使用神经成像技术测量他们的大脑活动。这种研究与运动控制的计算神经科学密切相关，它包括大脑如何组织感觉运动协调的抽象功能模型。实验室研究的第四部分强调对实际的类人机器人和受生物启发的机器人的研究。 With this work, we are first interested in testing our learning and control theories with real physical systems in order to evaluate the robustness of our research results. Another challenge arises due to the scalability of our methods towards complex robot: our most advanced robot (similar to the picture above) requires the nonlinear control of 30 physical degrees of freedom that need to be coordinated with visual, tactile, and acoustic perception. When attempting to synthesize behavior with such a machine, the shortcomings of state-of-the-art learning and control theories can be discovered and addressed in subsequent research. Finally, we also use humanoid robots for direct comparisons in behavioral experiments in which the robot is treated like a regular human subject. The Computational Learning and Motor Control Lab is also part of the Computational Neuroscience and Humanoid Robotics Department, located at the ATR Laboratories in Japan. Members of the lab have ample opportunities to visit the Japanese research facilities for short or extended times, and it is in the Japanese lab that we have access to the most advanced humanoid robotic hardware, as shown in the two humanoid robot pictures above.
