About this Research Topic
An increasing number of robots are made of soft materials. Because they are inherently safe to interact with, soft robots have been widely used in human-robot interactions. Moreover, due to their flexibility, they adapt to their environment, requiring less precision in certain tasks like grasp planning. Nonetheless, soft robotic state estimation has its unique challenges. In general, soft robots are underactuated and have infinitely many degrees of freedom. They undergo large deformations and hence display a highly nonlinear hyperelastic behavior. Furthermore, because many applications involve contact, we require state estimation algorithms to scale well and perform robustly in contact-rich interactions with humans or environments. To balance precision and timing requirements, novel proprioceptive sensing and modeling are needed to estimate a soft robot’s state efficiently and effectively. Real-time state estimation also paves the way towards closed-loop control of soft robots.
This Research Topic aims to exchange new knowledge, discover inspiring trends, and advance integrated software and hardware techniques for soft robot sensing and the estimation of their internal states, such as geometric shape and properties, strain- and stress-related quantities in quasi-static settings, and velocities and accelerations in dynamic settings. Our goal is to connect communities and stimulate collaborative research. Desired articles could (1) identify novel state estimation theories and algorithms for robots with parts made of soft materials, (2) discuss state estimation in the context of applications in soft robotics, human-robot interaction, or wearable devices, (3) demonstrate real-time state estimation with limited precision for closed-loop or accurate state estimation for open-loop control, (4) describe novel integrated hard- and software prototypes of proprioceptive soft robots, (5) review and benchmark various methods proposed by different communities (e.g., robotics, HCI, HRI) with the ultimate goal to enhance the mutual understanding of challenges and opportunities related to this Research Topics.
· High-resolution soft body proprioception
· Contact force/torque estimation
· Joint proprioception and tactile sensing
· Real-time state estimation for soft bodies
· New mechanisms or principles for soft body state estimation
· Simulation and differentiable simulation for soft body state estimation
· Learning-based tactile sensing
· Learning-based proprioception
· Self-calibration for soft robot state estimation
· Soft robot co-design for state estimation and closed-loop control
· Perspectives on challenges and open questions
· Benchmarking experiments for comparing sensing techniques and state estimation algorithms
Moritz Bächer is a Research Scientist and group leader at Disney Research. His core expertise is the development of differentiable simulators to tackle complex design, control, and characterization problems in (soft) robotics, digital fabrication, and robotic construction. All other Topic Editors declare no competing interests with the theme of this Research Topic.
Keywords: Soft Body State Modelling and Estimation, Soft Sensor Design, Shape and Tactile Sensing, Machine Learning for Soft Robotics, Soft Robot Simulation
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