Learning task error models for manipulation

Precise kinematic forward models are important for robots to successfully perform dexterous grasping and manipulation tasks, especially when visual servoing is rendered infeasible due to occlusions. A lot of research has been conducted to estimate geometric and non-geometric parameters of kinematic chains to minimize reconstruction errors. However, kinematic chains can include non-linearities, e. g. due to cable stretch and motor-side encoders, that result in significantly different errors for different parts of the state space. Previous work either does not consider such non-linearities or proposes to estimate non-geometric parameters of carefully engineered models that are robot specific. We propose a data-driven approach that learns task error models that account for such unmodeled non-linearities. We argue that in the context of grasping and manipulation, it is sufficient to achieve high accuracy in the task relevant state space. We identify this relevant state space using previously executed joint configurations and learn error corrections for those. Therefore, our system is developed to generate subsequent executions that are similar to previous ones. The experiments show that our method successfully captures the non-linearities in the head kinematic chain (due to a counterbalancing spring) and the arm kinematic chains (due to cable stretch) of the considered experimental platform, see Fig. 1. The feasibility of the presented error learning approach has also been evaluated in independent DARPA ARM-S testing contributing to successfully complete 67 out of 72 grasping and manipulation tasks.

Authors

• Peter Pastor
• Mrinal Kalakrishnan
• Jonathan Binney
• Jonathan Kelly
• Ludovic Righetti
• Gaurav S. Sukhatme
• Stefan Schaal

Keywords

• Kinematics
• Joints
• Cameras
• Robots
• Training
• Computational modeling
• Grasping
• Errors In Task
• State Space
• Geometric Parameters
• Forward Model
• Experimental Platform
• Manipulation Tasks
• Joint Configuration
• Error Learning
• Kinematic Chain
• Visual Servoing
• Systematic Errors
• Nonlinear Programming
• Gaussian Process
• Kriging
• Viewing Angle
• Calibration Procedure
• Joint Angles
• System Overview
• Joint Space
• End-effector Pose
• Left Camera
• Pose Error
• Parameters Of The Robot
• Base Frame
• Pose Estimation
• Gaussian Process Regression Model
• Inverse Kinematics
• Robot Manipulator
• Test Configuration