Variable admittance controller for physical human robot interaction

Wai, Hninn; Hlaing, Wai; Myo, Aung Kyaw; Sin, Thant

doi:10.1109/iecon.2017.8216494

Cited by 3 publications

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The aim of the proposed methodology is to achieve a prescribed (known) human-robot dynamics given by an available task model. In [15] a variable admittance controller is proposed. It infers human intentions by monitoring the variation of the interaction force magnitude, accordingly tuning the admittance controller damping.…”

Section: Non-learning-based Control Methodologiesmentioning

confidence: 99%

Model-Based Reinforcement Learning Variable Impedance Control for Human-Robot Collaboration

Roveda

Maskani

Franceschi

et al. 2020

J Intell Robot Syst

131

View full text Add to dashboard Cite

Industry 4.0 is taking human-robot collaboration at the center of the production environment. Collaborative robots enhance productivity and flexibility while reducing human's fatigue and the risk of injuries, exploiting advanced control methodologies. However, there is a lack of real-time model-based controllers accounting for the complex human-robot interaction dynamics. With this aim, this paper proposes a Model-Based Reinforcement Learning (MBRL) variable impedance controller to assist human operators in collaborative tasks. More in details, an ensemble of Artificial Neural Networks (ANNs) is used to learn a human-robot interaction dynamic model, capturing uncertainties. Such a learned model is kept updated during collaborative tasks execution. In addition, the learned model is used by a Model Predictive Controller (MPC) with Cross-Entropy Method (CEM). The aim of the MPC+CEM is to online optimize the stiffness and damping impedance control parameters minimizing the human effort (i.e, minimizing the human-robot interaction forces). The proposed approach has been validated through an experimental procedure. A lifting task has been considered as the reference validation application (weight of the manipulated part: 10 kg unknown to the robot controller). A KUKA LBR iiwa 14 R820 has been used as a test platform. Qualitative performance (i.e, questionnaire on perceived collaboration) have been evaluated. Achieved results have been compared with previous developed offline model-free optimized controllers and with the robot manual guidance controller. The proposed MBRL variable impedance controller shows improved humanrobot collaboration. The proposed controller is capable to actively assist the human in the target task, compensating for the unknown part weight. The human-robot interaction dynamic model has been trained with a few initial experiments (30 initial experiments). In addition, the possibility to keep the learning of the human-robot interaction dynamics active allows accounting for the adaptation of human motor system.

show abstract

Section: Non-learning-based Control Methodologiesmentioning

confidence: 99%

Model-Based Reinforcement Learning Variable Impedance Control for Human-Robot Collaboration

Roveda

Maskani

Franceschi

et al. 2020

J Intell Robot Syst

131

View full text Add to dashboard Cite

show abstract

Adaptive Admittance Control for Physical Human-Robot Interaction based on Imitation and Reinforcement Learning

Guo,

Yao,

et al. 2023

2023 29th International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

View full text Add to dashboard Cite

An Admittance Parameter Optimization Method Based on Reinforcement Learning for Robot Force Control

Hu,

Liu,

Ren

et al. 2024

Actuators

View full text Add to dashboard Cite

When a robot performs tasks such as assembly or human–robot interaction, it is inevitable for it to collide with the unknown environment, resulting in potential safety hazards. In order to improve the compliance of robots to cope with unknown environments and enhance their intelligence in contact force-sensitive tasks, this paper proposes an improved admittance force control method, which combines classical adaptive control and machine learning methods to make them use their respective advantages in different stages of training and, ultimately, achieve better performance. In addition, this paper proposes an improved Deep Deterministic Policy Gradient (DDPG)-based optimizer, which is combined with the Gaussian process (GP) model to optimize the admittance parameters. In order to verify the feasibility of the algorithm, simulations and experiments are carried out in MATLAB and on a UR10e robot, respectively. The experimental results show that the algorithm improves the convergence speed by 33% in comparison to the general model-free learning method, and has better control performance and robustness. Finally, the adjustment time required by the algorithm is 44% shorter than that of classical adaptive admittance control.

show abstract

Variable admittance controller for physical human robot interaction

Cited by 3 publications

References 22 publications

Model-Based Reinforcement Learning Variable Impedance Control for Human-Robot Collaboration

Model-Based Reinforcement Learning Variable Impedance Control for Human-Robot Collaboration

Adaptive Admittance Control for Physical Human-Robot Interaction based on Imitation and Reinforcement Learning

An Admittance Parameter Optimization Method Based on Reinforcement Learning for Robot Force Control

Contact Info

Product

Resources

About