Opportunities and Challenges in Soft Robotics
Bibliographic record
Abstract
Soft Robotics has emerged as a new and rapidly evolving interdisciplinary research area. This technology can provide a wide range of opportunities to create machines with unprecedented mechanical functionalities, as well as robots that are intrinsically safe to interact with human beings. However, the potential of this technology has not been fully realized as it is still a significant challenge to design, model and control such robots. This special issue, building on a workshop co-organized by the guest editors at the 2019 IEEE International Conference on Robotics and Automation in Montreal, Canada, focuses on recent advancements in soft robotics. The set of accepted papers highlights the opportunities and critical challenges of this field. Successfully realized soft robotics technologies could have a major impact on numerous industries and human activities (1900166, 1900171). Indeed, soft robotics offers the potential to be much more conformable and adaptable through novel sensing (1900080, 1900171, 1900178, 2000002; see Figure 1 A,B) and actuation mechanisms (1900177, 1900163; see Figure 1 C,D). As a result, these robots will be able to demonstrate significantly higher dexterity and manipulation capabilities than their traditional rigid counterparts. For example, grippers/gloves with embedded soft sensors that can empower service robots to manipulate a broad range of objects (1900080; see Figure 1 A) or enable computational proprioception and task identification (2000002; see Figure 1 B). Bio-inspired soft robots can also significantly benefit search and rescue and exploratory operations as they can potentially negotiate across much more complicated terrestrial and aquatic terrains with soft bodies (1900183, 1900154, 1900186; see Figure 1 E,F). Furthermore, soft robotic technologies could be used to create highly functional magnetically controlled devices, which can potentially change minimally-invasive surgeries and targeted drug delivery (1900086; see Figure 1 G). This special issue of Advanced Intelligent Systems is aimed at both roboticists and material scientists. Based on a rigorous peer-review process, we have selected a set of papers that illustrate the inherent interdisciplinary nature of, and the diverse approaches being adopted within current soft robotics research. We hope that the Special Issue will stimulate researchers currently working in soft robotics, and also encourage other researchers to engage this emerging and challenging area. Hamid Marvi received his B.Sc. from Iran University of Science and Technology in 2004, M.Sc. degree from Clemson University in 2009 and Ph.D. in mechanical engineering from Georgia Tech in 2013. He was a postdoctoral fellow at Georgia Tech and then at Carnegie Mellon University till August 2015. Since then, he has been an Assistant Professor of Mechanical and Aerospace Engineering at Arizona State University. His research aims to study fundamental physics behind interactions of biological and robotic systems with their surrounding solid, granular, and fluidic environments. Guo Zhan Lum received his B.Eng. from Nanyang Technological University in 2010. He went on to pursue his postgraduate studies under the dual Ph.D. program of Nanyang Technological University and Carnegie Mellon University. He received his M.Sc. degree from Carnegie Mellon University in 2015, and dual Ph.D. degrees in 2016. From 2016 to 2017, he was a post-doctoral researcher at the Max Planck Institute for Intelligent Systems. He is now an Assistant Professor at Nanyang Technological University and his research interests include soft robots, miniature robots and biomedical devices. Ian Walker received the B.Sc. from the University of Hull, England, in 1983 and the M.S. and Ph.D. from the University of Texas at Austin in 1985 and 1989, respectively. He was an Assistant and Associate Professor at Rice University from 1989 to 1997. Since 1997, he has been with the Department of Electrical and Computer Engineering at Clemson University, where he is a full Professor. Professor Walker's research centers on robotics, particularly novel continuous backbone “continuum” and soft robots.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".