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Rus, D. & Tolley, M. T. Design, fabrication and control of soft robots. Nature 521, 467–475, https://doi.org/10.1038/nature14543 (2015). Colonoscope training model, kyoto kagaku co., ltd, fushimi-ku kyoto, japan, https://www.kyotokagaku.com/products/detail01/m40.html (Accessed: 22-03-2019).

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BALLU is a robot that never falls down. It is a fact that BALLU, by design, cannot damage its surrounding environment or itself, unlike other heavier robots. However, interestingly, BALLU has its own counterpart to a conventional biped’s “fall” state, which is called “sink down” state. In this state, the robot has sunk down as shown in Figure 3A. BALLU’s body is slightly heavier than the net buoyancy that the balloons can exert. Hence, without any control, it sinks down until reaching equilibrium between the buoyancy, the GRFs, and the straightening force from the compressed knee joints. In this state, it is difficult to conduct any meaningful motion. In the sink down state, the knee joints are close to the joint limits and it is hard to make the leg swing without dragging its foot, despite most of the body parts still floating. Therefore, it is necessary for BALLU to manage its state with properly coordinated walking motions and avoid sinking down. 3.1.1 Underactuation Manfredi, L., Yue, L., Zhang, J. & Cuschieri, A. A 4 DOFs variable stiffness soft module. In IEEE RoboSoft, https://doi.org/10.1109/ROBOSOFT.2018.8404903 (2018). Alcaide, J. O., Pearson, L. & Rentschler, M. E. Design, modeling and control of a sma-actuated biomimetic robot with novel functional skin. In 2017 IEEE International Conference on Robotics and Automation (ICRA), 4338–4345, https://doi.org/10.1109/ICRA.2017.7989500 (2017). verifyErrors }}{{ message }}{{ /verifyErrors }}{{ One class of robots most commonly used in our daily lives is service robots. In particular, a central application is social robots that interact with humans and provide information. For instance, LG Electronics’ CLOi at an airport ( Incheon Airport, 2019) leads passengers to find a route inside the airport and informs them of their flight schedule. SoftBank’s Pepper at a library ( Roanoke County Public Library, 2018) helps the visitors find books. As a similar example, LinkedIn makes the use of Double Robotics’ telepresence robot to telecommute ( Double Robotics, 2015). These robots are more economical than human employees in providing an intuitive and easy interface to information. As a result, more service providers are seeking to introduce robots to their business ( Aymerich-Franch and Ferrer, 2020; Fukawa, 2020).Omnidirectional compliance on cross-linked actuator coordination enables simultaneous multi-functions of soft modular robots Manfredi, L., Capoccia, E., Ciuti, G. et al. A Soft Pneumatic Inchworm Double balloon (SPID) for colonoscopy. Ahn, M. S., and Hong, D. (2020). Dynamic, robust locomotion for a non-anthropomorphic biped. In 2020 17th International Conference on Ubiquitous Robots (UR) (New Jersey, IEEE), 185–191. doi:10.1109/ur49135.2020.9144959

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Alazmani, A., Hood, A., Jayne, D., Neville, A. & Culmer, P. Quantitative assessment of colorectal morphology: Implications for robotic colonoscopy. Medical Engineering & Physics 38, 148–154, https://doi.org/10.1016/j.medengphy.2015.11.018 (2016). Rohmer, E., Singh, S. P. N., and Freese, M. (2013). Coppeliasim (formerly v-rep): a versatile and scalable robot simulation framework. In Proc. of the International Conference on Intelligent Robots and Systems (IROS). Www.coppeliarobotics.com.The hyperparameters are chosen via grid search to have the least test loss. The list of searched hyperparameters and their candidates are the number of hidden layers h l ∈ 2 0 , 2 1 , ⋯ , 2 7, hidden units h u ∈ 1,2,3,4, and the learning rate α ∈ 0.0001 , 0.0002 , 0.0005 , 0.001 , ⋯ 0.05 , 0.01. As a result, a network with three hidden layers with eight hidden units, respectively, and a learning rate of 0.002 were used. The network is trained for 100 epochs with early stopping. For each set of hyperparameters, they are mostly stopped in less than 50 epochs. Dataset was randomly split so that 70% of data is used for training, 15% for validation, 15% for testing, and the training and validation set is shuffled at every epoch. The basic concept of SPID involves the design of the double balloon and the SPA. Each part was constructed and tested before the assembly to avoid any issue in the final functional outcomes. A colonic phantom was designed and constructed to test the locomotion performance based on the size and configuration of the average human colon anatomy. The following sections report on the design details of each component. Balloon for anchorage

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Jang, H. J. Training in endoscopy: Colonoscopy. Clinical endoscopy 50, 322, https://doi.org/10.5946/ce.2017.077 (2017).The original contributions presented in the study are included in the article material, further inquiries can be directed to the corresponding authors. Author Contributions

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Incheon Airport (2019). [Incheon Airport] 2019 Incheon International Airport Promotional Video. [YouTube video]. [Accessed Jun. 19, 2021]. Haga M, Uchida M (2017) Propulsion modeling of caudal fin driving system on balloon fish robot. Artif Life Robot 22:10–16 Robotics and Mechanisms Laboratory (RoMeLa), Department of Mechanical and Aerospace Engineering, University of California, Los Angeles (UCLA), Los Angeles, CA, United States Zhang, X., Zeng, Y., and Hong, D. (2016). A novel and efficient compact propulsion mechanism with high response speed for robotic applications. In 2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI). 314–320. New Jersey, IEEE. doi:10.1109/URAI.2016.7734050 We trained a planar data-driven walking controller with artificial neural networks using the definition of the state and a set of expert data.It is well established that one of the main challenges for a robotic colonoscopy consists of the ability of the device to negotiate the acute angled splenic flexure at the junction between the transverse and the descending colon. This flexure has an angle ranging between 40° to 50° and a diameter of 30 mm after CO 2 inflation of the colon 24. A neural network is trained as a function approximator for each g. Both networks individually consist of a multilayer perceptron with ELU nonlinear activation functions and ADAM optimizer with MSE (Minimum Square Error) loss and a constant learning rate.