Sensor-based control, real-time motion planning, and reinforcement learning for industrial robots

Abstract

Embedding multiple sensors — force/torque, vision, and distance — in the feedback loops of motion controllers has enabled new robot applications. For instance, safe human-robot interaction and many assembly tasks that could not be automated before. As important as these real-time control features is the ability to plan robot motions deterministically and in real-time. To enable spontaneous changes from sensor-guided robot motion control (e.g., force/torque or visual servo control) to trajectory-following motion control, an algorithmic framework is explained that lets us compute robot motions deterministically within less than one millisecond. The resulting class of on-line trajectory generation algorithms serves as an intermediate layer between low-level motion control and high-level sensor-based motion planning. Online motion generation from arbitrary states is an essential feature for autonomous hybrid switched motion control systems. Building upon this framework and with the goal of significantly reducing the amount of resources needed for programing industrial and service robots, reinforcement learning offers a yet unused potential that will be introduced as well. Samples and use-cases — including manipulation and human-robot interaction tasks — will accompany the talk in order to provide a comprehensible insight into these interesting and relevant fields of robotics.