Human-Like Adaptation of Force and Impedance in Stable and Unstable Interactions

By Chenguang Yang, Ganesh Gowrishankar, Sami Haddadin, Sven Parusel, Alin Olimpiu Albu-Schäffer, Etienne Burdet. 2011

400 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cognitive Neuroscience, Control and Systems Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Wei He
2. Marcia Kilchenman O'Malley
3. Bruno Siciliano
4. Dylan P. Losey
5. Anca Dragan
6. Zhijun Li
7. Etienne Burdet

In Cognitive Neuroscience

1. Marcia Kilchenman O'Malley
2. Dylan P. Losey
3. Anca Dragan
4. Andrea V. Bajcsy
5. Etienne Burdet

In Control and Systems Engineering

1. Wei He
2. Chenguang Yang
3. Yiting Dong
4. Zhijun Li
5. Changyin Sun
6. Yiming Jiang
7. Rongxin Cui
8. Dylan P. Losey

Citations in the corpus

• Learning from Physical Human Corrections, One Feature at a Time (2018, background): “Traditionally, the human’s interactions are treated in one of three ways [9]: as disturbances to be rejected [5, 12, 23], as collisions to be detected and avoided [4], or as operator signals to be followed by switching into a compliant mode [8, 10, 14].”
• Robot Learning System Based on Adaptive Neural Control and Dynamic Movement Primitives (2019, background): “Generally, a model-based control performs better if the model is accurate enough [19].”
• Adaptive Neural Impedance Control of a Robotic Manipulator With Input Saturation (2016, background): “The main challenge in this field is to investigate the physical robot–environment interaction problem intensively, which has become popular on the development side [6], [7].”
• Adaptive Parameter Estimation and Control Design for Robot Manipulators With Finite-Time Convergence (2018, background): “Many well-performed robot controllers such as computed torque control [7], augmented proportional-derivative control [8], and impedance control [9] are model-based control and have been successfully implemented in a wide range of robot applications.”
• A Framework to Describe, Analyze and Generate Interactive Motor Behaviors (2012, methodology): “This extended nonlinear adaptive controller can be used to adapt force and mechanical impedance as demonstrated in [28,84].”
• Variable Impedance Control of Redundant Manipulators for Intuitive Human–Robot Physical Interaction (2015, methodology): “Inspired to these studies, a learning control technique is proposed in [11] to optimally adapt robot’s impedance during the interaction with dynamic environments and humans.”
• Collaborative manufacturing with physical human–robot interaction (2016, background): “This is the target of the research in [21] and [22]: a human-like learning controller is designed, to minimize motion error and effort, during interaction tasks.”