Adaptive Robotic Tool-Tip Control Learning Considering Online Changes in Grasping State

Abstract

Various robotic tool manipulation methods have been developed so far. However, to our knowledge, none of them have taken into account the fact that the grasping state such as grasping position and tool angle can change at any time during the tool manipulation. In addition, there are few studies that can handle deformable tools. In this study, we develop a method for estimating the position of a tool-tip, controlling the tool-tip, and handling online adaptation to changes in the relationship between the body and the tool, using a neural network including parametric bias. We demonstrate the effectiveness of our method for online change in grasping state and for deformable tools, in experiments using two different types of robots: axis-driven robot PR2 and tendon-driven robot MusashiLarm.

Accepted at IEEE Robotics and Automation Letters

References in corpus (5)

• How to select and use tools? : Active Perception of Target Objects Using Multimodal Deep Learning
• Motion Generation Using Bilateral Control-Based Imitation Learning with Autoregressive Learning
• Long-time Self-body Image Acquisition and its Application to the Control of Musculoskeletal Structures
• Object Recognition, Dynamic Contact Simulation, Detection, and Control of the Flexible Musculoskeletal Hand Using a Recurrent Neural Network with Parametric Bias
• Tool Shape Optimization through Backpropagation of Neural Network