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In the design of a compliant admittance controller for physical human-robot interaction, it is necessary to ensure stable and effective cooperation. The stability of the admittance controller is mainly threatened by a stiff environment. Many methods that guarantee stability in arbitrary environments, impose conservative control gains that limit the(More)
This paper presents a method for variable admittance control in human-robot cooperation tasks, that combines a human-like decision making process and an adaptation algorithm. A Fuzzy Inference System is designed that relies on the measured velocity and the force applied by the operator to modify on-line the damping of the robot admittance, based on expert(More)
In this paper, a variable admittance controller based on reinforcement learning is proposed for human-robot co-manipulation tasks. Setting as the goal of the reinforcement learning algorithm the minimisation of the jerk throughout a point-to-point movement, the proposed controller can learn the appropriate damping for effective cooperation without any prior(More)
In this paper, two admittance based control schemes for a power assisted lifting device are presented. This device can be used to hoist a heavy object interactively for reducing the operator's burden. The proposed system integrates an admittance controller with an inner control loop that regulates the velocity of the object. The admittance is the outer loop(More)
This paper addresses the problem of providing feedback to the operator about the manipulator's performance during human-robot physical interaction. A method is proposed that implements virtual constraints in Cartesian admittance control in order to prevent the operator from guiding the manipulator to low-performance configurations. The constraints are(More)
In this paper the problem of variable admittance control in human-robot cooperation tasks is investigated, considering rotational motion of the robot's end-effector. A Fuzzy Model Reference Learning algorithm is used to determine online the appropriate virtual damping of the admittance controller with partial state representation of the system. The learning(More)