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Impedance learning for robots interacting with unknown environments
In this paper, impedance learning is investigated for robots interacting with unknown environments. A twoloop control framework is employed and adaptive control is developed for the inner-loop position control. The environments are described as time-varying systems with unknown parameters in the state-space form. The gradient-following scheme and betterment scheme are employed to obtain a desired impedance model, subject to unknown environments. The desired interaction performance is achieved in the sense that a defined cost function is minimized. Simulation and experiment studies are carried out to verify the validity of the proposed method
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
Impedance adaptation for optimal robot–environment interaction
In this paper, impedance adaptation is investigated for robots interacting with unknown environments. Impedance control is employed for the physical interaction between robots and environments, subject to unknown and uncertain environments dynamics. The unknown environments are described as linear systems with unknown dynamics, based on which the desired impedance model is obtained. A cost function that measures the tracking error and interaction force is defined, and the critical impedance parameters are found to minimize it. Without requiring the information of the environments dynamics, the proposed impedance adaptation is feasible in a large number of applications where robots physically interact with unknown environments. The validity of the proposed method is verified through simulation studies
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
Force, impedance and trajectory learning for contact tooling and haptic identification
Humans can skilfully use tools and interact with the environment by adapting their movement trajectory, contact force, and impedance. Motivated by the human versatility, we develop here a robot controller that concurrently adapts feedforward force, impedance, and reference trajectory when interacting with an unknown environment. In particular, the robot's reference trajectory is adapted to limit the interaction force and maintain it at a desired level, while feedforward force and impedance adaptation compensates for the interaction with the environment. An analysis of the interaction dynamics using Lyapunov theory yields the conditions for convergence of the closed-loop interaction mediated by this controller. Simulations exhibit adaptive properties similar to human motor adaptation. The implementation of this controller for typical interaction tasks including drilling, cutting, and haptic exploration shows that this controller can outperform conventional controllers in contact tooling
Reference adaptation for robots in physical interactions with unknown environments
In this paper, we propose a method of reference adaptation for robots in physical interactions with unknown environments. A cost function is constructed to describe the interaction performance, which combines trajectory tracking error and interaction force between the robot and the environment. It is minimized by the proposed reference adaptation based on trajectory parametrization and iterative learning. An adaptive impedance control is developed to make the robot be governed by the target impedance model. Simulation and experiment studies are conducted to verify the effectiveness of the proposed method
A Self-Tuning Impedance-based Interaction Planner for Robotic Haptic Exploration
This paper presents a novel interaction planning method that exploits
impedance tuning techniques in response to environmental uncertainties and
unpredictable conditions using haptic information only. The proposed algorithm
plans the robot's trajectory based on the haptic interaction with the
environment and adapts planning strategies as needed. Two approaches are
considered: Exploration and Bouncing strategies. The Exploration strategy takes
the actual motion of the robot into account in planning, while the Bouncing
strategy exploits the forces and the motion vector of the robot. Moreover,
self-tuning impedance is performed according to the planned trajectory to
ensure compliant contact and low contact forces. In order to show the
performance of the proposed methodology, two experiments with a
torque-controller robotic arm are carried out. The first considers a maze
exploration without obstacles, whereas the second includes obstacles. The
proposed method performance is analyzed and compared against previously
proposed solutions in both cases. Experimental results demonstrate that: i) the
robot can successfully plan its trajectory autonomously in the most feasible
direction according to the interaction with the environment, and ii) a
compliant interaction with an unknown environment despite the uncertainties is
achieved. Finally, a scalability demonstration is carried out to show the
potential of the proposed method under multiple scenarios.Comment: 8 pages, 9 figures, accepted for IEEE Robotics and Automation Letters
(RA-L) and IEEE/RSJ International Conference on Intelligent Robots and
Systems 202
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