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Simultaneously encoding movement and sEMG-based stiffness for robotic skill learning
Transferring human stiffness regulation strategies to robots enables them to effectively and efficiently acquire adaptive impedance control policies to deal with uncertainties during the accomplishment of physical contact tasks in an unstructured environment. In this work, we develop such a physical human-robot interaction (pHRI) system which allows robots to learn variable impedance skills from human demonstrations. Specifically, the biological signals, i.e., surface electromyography (sEMG) are utilized for the extraction of human arm stiffness features during the task demonstration. The estimated human arm stiffness is then mapped into a robot impedance controller. The dynamics of both movement and stiffness are simultaneously modeled by using a model combining the hidden semi-Markov model (HSMM) and the Gaussian mixture regression (GMR). More importantly, the correlation between the movement information and the stiffness information is encoded in a systematic manner. This approach enables capturing uncertainties over time and space and allows the robot to satisfy both position and stiffness requirements in a task with modulation of the impedance controller. The experimental study validated the proposed approach
Robust Cooperative Manipulation without Force/Torque Measurements: Control Design and Experiments
This paper presents two novel control methodologies for the cooperative
manipulation of an object by N robotic agents. Firstly, we design an adaptive
control protocol which employs quaternion feedback for the object orientation
to avoid potential representation singularities. Secondly, we propose a control
protocol that guarantees predefined transient and steady-state performance for
the object trajectory. Both methodologies are decentralized, since the agents
calculate their own signals without communicating with each other, as well as
robust to external disturbances and model uncertainties. Moreover, we consider
that the grasping points are rigid, and avoid the need for force/torque
measurements. Load distribution is also included via a grasp matrix
pseudo-inverse to account for potential differences in the agents' power
capabilities. Finally, simulation and experimental results with two robotic
arms verify the theoretical findings
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
Continuous critic learning for robot control in physical human-robot interaction
In this paper, optimal impedance adaptation is investigated for interaction control in constrained motion. The external environment is modeled as a linear system with parameter matrices completely unknown and continuous critic learning is adopted for interaction control. The desired impedance is obtained which leads to an optimal realization of the trajectory tracking and force regulation. As no particular system information is required in the whole process, the proposed interaction control provides a feasible solution to a large number of applications. The validity of the proposed method is verified through simulation studies
Deep Model Predictive Variable Impedance Control
The capability to adapt compliance by varying muscle stiffness is crucial for
dexterous manipulation skills in humans. Incorporating compliance in robot
motor control is crucial to performing real-world force interaction tasks with
human-level dexterity. This work presents a Deep Model Predictive Variable
Impedance Controller for compliant robotic manipulation which combines Variable
Impedance Control with Model Predictive Control (MPC). A generalized Cartesian
impedance model of a robot manipulator is learned using an exploration strategy
maximizing the information gain. This model is used within an MPC framework to
adapt the impedance parameters of a low-level variable impedance controller to
achieve the desired compliance behavior for different manipulation tasks
without any retraining or finetuning. The deep Model Predictive Variable
Impedance Control approach is evaluated using a Franka Emika Panda robotic
manipulator operating on different manipulation tasks in simulations and real
experiments. The proposed approach was compared with model-free and model-based
reinforcement approaches in variable impedance control for transferability
between tasks and performance.Comment: Preprint submitted to the journal of robotics and autonomous system
Bayesian estimation of human impedance and motion intention for human-robot collaboration
This article proposes a Bayesian method to acquire the estimation of human impedance and motion intention in a human-robot collaborative task. Combining with the prior knowledge of human stiffness, estimated stiffness obeying Gaussian distribution is obtained by Bayesian estimation, and human motion intention can be also estimated. An adaptive impedance control strategy is employed to track a target impedance model and neural networks are used to compensate for uncertainties in robotic dynamics. Comparative simulation results are carried out to verify the effectiveness of estimation method and emphasize the advantages of the proposed control strategy. The experiment, performed on Baxter robot platform, illustrates a good system performance
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