2,769 research outputs found
Human Like Adaptation of Force and Impedance in Stable and Unstable Tasks
Abstract—This paper presents a novel human-like learning con-troller to interact with unknown environments. Strictly derived from the minimization of instability, motion error, and effort, the controller compensates for the disturbance in the environment in interaction tasks by adapting feedforward force and impedance. In contrast with conventional learning controllers, the new controller can deal with unstable situations that are typical of tool use and gradually acquire a desired stability margin. Simulations show that this controller is a good model of human motor adaptation. Robotic implementations further demonstrate its capabilities to optimally adapt interaction with dynamic environments and humans in joint torque controlled robots and variable impedance actuators, with-out requiring interaction force sensing. Index Terms—Feedforward force, human motor control, impedance, robotic control. I
Trajectory Deformations from Physical Human-Robot Interaction
Robots are finding new applications where physical interaction with a human
is necessary: manufacturing, healthcare, and social tasks. Accordingly, the
field of physical human-robot interaction (pHRI) has leveraged impedance
control approaches, which support compliant interactions between human and
robot. However, a limitation of traditional impedance control is that---despite
provisions for the human to modify the robot's current trajectory---the human
cannot affect the robot's future desired trajectory through pHRI. In this
paper, we present an algorithm for physically interactive trajectory
deformations which, when combined with impedance control, allows the human to
modulate both the actual and desired trajectories of the robot. Unlike related
works, our method explicitly deforms the future desired trajectory based on
forces applied during pHRI, but does not require constant human guidance. We
present our approach and verify that this method is compatible with traditional
impedance control. Next, we use constrained optimization to derive the
deformation shape. Finally, we describe an algorithm for real time
implementation, and perform simulations to test the arbitration parameters.
Experimental results demonstrate reduction in the human's effort and
improvement in the movement quality when compared to pHRI with impedance
control alone
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