6 accepted papers
Compared to rigid hands, underactuated compliant hands offer greater adaptability to object shapes, provide stable grasps, and are often more cost-effective. However, they introduce uncertainties in hand-object interactions due to their inherent compliance and lack of precise finger proprioception a
Tactile sensing is a necessary capability for a robotic hand to perform fine manipulations and interact with the environment. Optical sensors are a promising solution for high-resolution contact estimation. Nevertheless, they are usually not easy to fabricate and require individual calibration in or
Simulating tactile perception could potentially leverage the learning capabilities of robotic systems in manipulation tasks. However, the reality gap of simulators for high-resolution tactile sensors remains large. Models trained on simulated data often fail in zero-shot inference and require fine-t…
Tendon-based underactuated hands are intended to be simple, compliant and affordable. Often, they are 3D printed and do not include tactile sensors. Hence, performing in-hand object recognition with direct touch sensing is not feasible. Adding tactile sensors can complicate the hardware and introduc…
Object insertion is primarily studied using rigid robotic hands. However, these may have difficulties overcoming spatial uncertainties originating from an uncertain initial grasp. Compliant hands, on the other hand, can cope with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:x
Throwing objects by a robot extends its reach and has many industrial applications. While analytical models can provide efficient performance, they require accurate estimation of system parameters. Reinforcement Learning (RL) algorithms can provide an accurate throwing policy without prior knowledge