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1. Encoder-less Robot Control for Space Operations with Continuum Robots

   Frazaelle, C.G.; Walker, I.D.; AlAttar, A.; Kormushev, P. (March 2021, IEEE Aerospace Conference)

   null (Ed.)

   Continuum robots have strong potential for application in Space environments. However, their modeling is challenging in comparison with traditional rigid-link robots. The Kinematic-Model-Free (KMF) robot control method has been shown to be extremely effective in permitting a rigid-link robot to learn approximations of local kinematics and dynamics (“kinodynamics”) at various points in the robot’s task space. These approximations enable the robot to follow various trajectories and even adapt to changes in the robot’s kinematic structure. In this paper, we present the adaptation of the KMF method to a three-section, nine degrees-of-freedom continuum manipulator for both planar and spatial task spaces. Using only an external 3D camera, we show that the KMF method allows the continuum robot to converge to various desired set points in the robot’s task space, avoiding the complexities inherent in solving this problem using traditional inverse kinematics. The success of the method shows that a continuum robot can “learn” enough information from an external camera to reach and track desired points and trajectories, without needing knowledge of exact shape or position of the robot. We similarly apply the method in a simulated example of a continuum robot performing an inspection task on board the ISS. 

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2. A survey of communicating robot learning during human-robot interaction

   https://doi.org/10.1177/02783649241281369  

   Habibian, Soheil; Alvarez_Valdivia, Antonio; Blumenschein, Laura_H; Losey, Dylan_P (October 2024, The International Journal of Robotics Research)

   For robots to seamlessly interact with humans, we first need to make sure that humans and robots understand one another. Diverse algorithms have been developed to enable robots to learn from humans (i.e., transferring information from humans to robots). In parallel, visual, haptic, and auditory communication interfaces have been designed to convey the robot’s internal state to the human (i.e., transferring information from robots to humans). Prior research often separates these two directions of information transfer, and focuses primarily on either learning algorithms or communication interfaces. By contrast, in this survey we take an interdisciplinary approach to identify common themes and emerging trends that close the loop between learning and communication. Specifically, we survey state-of-the-art methods and outcomes for communicating a robot’s learning back to the human teacher during human-robot interaction. This discussion connects human-in-the-loop learning methods and explainable robot learning with multimodal feedback systems and measures of human-robot interaction. We find that—when learning and communication are developed together—the resulting closed-loop system can lead to improved human teaching, increased human trust, and human-robot co-adaptation. The paper includes a perspective on several of the interdisciplinary research themes and open questions that could advance how future robots communicate their learning to everyday operators. Finally, we implement a selection of the reviewed methods in a case study where participants kinesthetically teach a robot arm. This case study documents and tests an integrated approach for learning in ways that can be communicated, conveying this learning across multimodal interfaces, and measuring the resulting changes in human and robot behavior. 

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3. Kinematics and Stiffness Modeling of Soft Robot With a Concentric Backbone

   https://doi.org/10.1115/1.4055860  

   Xiao, Qingyu; Musa, Mishek; Godage, Isuru S.; Su, Hao; Chen, Yue (October 2023, Journal of Mechanisms and Robotics)

   Abstract Soft robots can undergo large elastic deformations and adapt to complex shapes. However, they lack the structural strength to withstand external loads due to the intrinsic compliance of fabrication materials (silicone or rubber). In this paper, we present a novel stiffness modulation approach that controls the robot’s stiffness on-demand without permanently affecting the intrinsic compliance of the elastomeric body. Inspired by concentric tube robots, this approach uses a Nitinol tube as the backbone, which can be slid in and out of the soft robot body to achieve robot pose or stiffness modulation. To validate the proposed idea, we fabricated a tendon-driven concentric tube (TDCT) soft robot and developed the model based on Cosserat rod theory. The model is validated in different scenarios by varying the joint-space tendon input and task-space external contact force. Experimental results indicate that the model is capable of estimating the shape of the TDCT soft robot with an average root-mean-square error (RMSE) of 0.90 (0.56% of total length) mm and average tip error of 1.49 (0.93% of total length) mm. Simulation studies demonstrate that the Nitinol backbone insertion can enhance the kinematic workspace and reduce the compliance of the TDCT soft robot by 57.7%. Two case studies (object manipulation and soft laparoscopic photodynamic therapy) are presented to demonstrate the potential application of the proposed design. 

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4. Exploring Consequential Robot Sound: Should We Make Robots Quiet and Kawaii-et?

   https://doi.org/10.1109/IROS51168.2021.9636365  

   Zhang, Brian J.; Peterson, Knut; Sanchez, Christopher A.; Fitter, Naomi T. (September 2021, 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   All robots create consequential sound—sound produced as a result of the robot’s mechanisms—yet little work has explored how sound impacts human-robot interaction. Recent work shows that the sound of different robot mechanisms affects perceived competence, trust, human-likeness, and discomfort. However, the physical sound characteristics responsible for these perceptions have not been clearly identified. In this paper, we aim to explore key characteristics of robot sound that might influence perceptions. A pilot study from our past work showed that quieter and higher-pitched robots may be perceived as more competent and less discomforting. To better understand how variance in these attributes affects perception, we performed audio manipulations on two sets of industrial robot arm videos within a series of four new studies presented in this paper. Results confirmed that quieter robots were perceived as less discomforting. In addition, higher-pitched robots were perceived as more energetic, happy, warm, and competent. Despite the robot’s industrial purpose and appearance, participants seemed to prefer more "cute" (or "kawaii") sound profiles, which could have implications for the design of more acceptable and fulfilling sound profiles for human-robot interactions with practical collaborative robots. 

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5. Fabrication, Modeling, and Control of Plush Robots

   Bern, James M; Kumagai, Grace; Coros, Stelian (September 2017, Proceedings of the International Conference on Intelligent Robots and Systems)

   Abstract — We present a class of tendon-actuated soft robots, which promise to be low-cost and accessible to non-experts. The fabrication techniques we introduce are largely based on traditional techniques for fabricating plush toys, and so we term the robots created using our approach “plush robots.” A plush robot moves by driving internal winches that pull in (or let out) tendons routed through its skin. We provide a forward simulation model for predicting a plush robot’s deformation behavior given some contractions of its internal winches. We also leverage this forward model for use in an interactive control scheme, in which the user provides a target pose for the robot, and optimal contractions of the robot’s winches are automatically computed in real-time. We fabricate two examples to demonstrate the use of our system, and also discuss the design challenges inherent to plush robots. 

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