5 accepted papers
Learning from Demonstration (LfD) for contact-rich tasks faces a fundamental challenge: arbitrating between tracking a demonstrated trajectory and reproducing an interaction force. This paper introduces a novel one-shot LfD framework that resolves this conflict by leveraging the operator's grip forc…
Precise control of soft pneumatic actuators is impeded by significant nonlinearities, particularly large internal volume variations during actuation—a factor often overlooked in conventional modeling. This paper proposes an adaptive robust control (ARC) framework designed for high-performance, energ
Precise control of soft pneumatic actuators is impeded by significant nonlinearities, particularly large internal volume variations during actuation---a factor often overlooked in conventional modeling. This paper proposes an adaptive robust control (ARC) framework designed for high-performance, ene…
This study presents a design method for pneumatic soft actuators, guaranteeing precise actuator kinematics. Unlike conventional soft actuator designs, which rely on trial-and-error or heuristic modeling, our approach allows direct inverse derivation of uninflated actuator geometry from the desired p
The significance of physical human-robot interaction (pHRI) with collaborative robots in the industry is growing steadily. Within this domain, an admittance controller is crucial for enabling robots to follow or assist human intentions. However, a persistent challenge with admittance controllers is…