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  1. null (Ed.)
    There has been great progress in soft robot design, manufacture, and control in recent years, and soft robots are a tool of choice for safe and robust handling of objects in conditions of uncertainty. Still, dexterous in-hand manipulation using soft robots remains a challenge. This paper introduces foam robot hands actuated by tendons sewn through a fabric glove. The flexibility of tendon actuation allows for high competence in utilizing deformation for robust in-hand manipulation. We discuss manufacturing, control, and design optimization for foam robots and demonstrate robust grasping and in-hand manipulation on a variety of different physical hand prototypes. 
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  2. null (Ed.)
    Grasp planning and motion synthesis for dexterous manipulation tasks are traditionally done given a pre-existing kinematic model for the robotic hand. In this paper, we introduce a framework for automatically designing hand topologies best suited for manipulation tasks given high-level objectives as input. Our pipeline is capable of building custom hand designs around specific manipulation tasks based on high-level user input. Our framework comprises of a sequence of trajectory optimizations chained together to translate a sequence of objective poses into an optimized hand mechanism along with a physically feasible motion plan involving both the constructed hand and the object. We demonstrate the feasibility of this approach by synthesizing a series of hand designs optimized to perform specified in-hand manipulation tasks of varying difficulty. We extend our original pipeline 32 to accommodate the construction of hands suitable for multiple distinct manipulation tasks as well as provide an in depth discussion of the effects of each non-trivial optimization term. 
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  3. null (Ed.)
    In spite of substantial progress, robust and dexterous in-hand manipulation remains a robotics grand challenge. Recent research has shown that optimization of robot hand morphology for specific tasks can result in custom hand designs that are low-cost, easy to maintain, and highly capable. However, the resulting manipulation strategies may not be very robust or generalizable in real-world situations. This paper shows that robustness can be improved dramatically by optimizing controls instead of contact force / trajectories and by considering uncertainty explicitly during the optimization process. We present a evolutionary algorithm based pipeline for co-optimizing hand morphology and control strategy over families of problems and initial states in order to achieve robust in-hand manipulation. We demonstrate that this approach produces robust results which utilize all surfaces of the hand and surprising dynamic motions. We showcase the advantage of optimizing joint limit values to create robust designs. Furthermore, we demonstrate that our approach is complementary to trajectory optimization based approaches and can be utilized to improve robustness of such results as well as to create custom hand designs from scratch. Results are shown for repositioning and reorienting diverse objects relative to the palm of the hand. 
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  4. The recent ubiquity of high-framerate (120 fps and higher) handheld cameras creates the opportunity to study human grasping at a greater level of detail than normal speed cameras allow. We first collected 91 slow-motion interactions with objects in a convenience store setting. We then annotated the actions through the lenses of various existing manipulation taxonomies. We found manipulation, particularly the process of forming a grasp, is complicated and proceeds quickly. Our dataset shows that there are many ways that people deal with clutter in order to form a strong grasp of an object. It also reveals several errors and how people recover from them. Though annotating motions in detail is time-consuming, the annotation systems we used nevertheless leave out important aspects of understanding manipulation actions, such as how the environment is functioning as a “finger” of sorts, how different parts of the hand can be involved in different grasping tasks, and high-level intent. 
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  5. 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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