Interactive Imitation Learning of Bimanual Movement Primitives

Abstract

Performing bimanual tasks with dual robotic setups can drastically increase the impact on industrial and daily life applications. However, performing a bimanual task brings many challenges, such as synchronization and coordination of the single-arm policies. This article proposes the safe, interactive movement primitives learning (SIMPLe) algorithm, to teach and correct single or dual arm impedance policies directly from human kinesthetic demonstrations. Moreover, it proposes a novel graph encoding of the policy based on Gaussian process regression where the single-arm motion is guaranteed to converge close to the trajectory and then toward the demonstrated goal. Regulation of the robot stiffness according to the epistemic uncertainty of the policy allows for easily reshaping the motion with human feedback and/or adapting to external perturbations. We tested the SIMPLe algorithm on a real dual-arm setup where the teacher gave separate single-arm demonstrations and then successfully synchronized them only using kinesthetic feedback or where the original bimanual demonstration was locally reshaped to pick a box at a different height.

Research on LFD System of Humanoid Dual-Arm Robot

“…Similar to GMM, Gaussian regression models can also generate a trajectory curve based on existing teaching data [27,28]. However, Gaussian regression models have a higher computational cost [29,30]. To address this issue, Schneider [31] proposed local Gaussian regression, which incorporates some custom constraints into the skill teaching data.…”

A Practical Roadmap to Learning from Demonstration for Robotic Manipulators in Manufacturing

“…Bi-manual tasks involve the coordinated use of both robot arms to manipulate objects or perform activities that require dual-handed dexterity [27,41,64,[77][78][79]. Teaching a robot to perform bi-manual tasks through LfD should emphasize synchronization and coordination between the robot's multiple arms.…”