Design and Performance Analysis of a Skin-Stretcher Device for Urging Head Rotation

This paper introduces a novel skin-stretcher device for gently urging head rotation. The device pulls and/or pushes the skin on the user's neck by using servo motors. The user is induced to rotate his/her head based on the sensation caused by the local stretching of skin. This mechanism informs the user when and how much the head rotation is requested; however it does not force head rotation, i.e., it allows the user to ignore the stimuli and to maintain voluntary movements. We implemented a prototype device and analyzed the performance of the skin stretcher as a human-in-the-loop system. Experimental results define its fundamental characteristics, such as input-output gain, settling time, and other dynamic behaviors. Features are analyzed, for example, input-output gain is stable within the same installation condition, but various between users

X-CAD: Optimizing CAD Models with Extended Finite Elements

We propose a novel generic shape optimization method for CAD models based on the eXtended Finite Element Method (XFEM). Our method works directly on the intersection between the model and a regular simulation grid, without the need to mesh or remesh, thus removing a bottleneck of classical shape optimization strategies. This is made possible by a novel hierarchical integration scheme that accurately integrates finite element quantities with sub-element precision. For optimization, we efficiently compute analytical shape derivatives of the entire framework, from model intersection to integration rule generation and XFEM simulation. Moreover, we describe a differentiable projection of shape parameters onto a constraint manifold spanned by user-specified shape preservation, consistency, and manufacturability constraints. We demonstrate the utility of our approach by optimizing mass distribution, strength-to-weight ratio, and inverse elastic shape design objectives directly on parameterized 3D CAD models

The Role of Closed-Loop Hand Control in Handshaking Interactions

In this letter, we investigate the role of haptic feedback in human-robot handshaking by comparing different force controllers. The basic hypothesis is that in human handshaking force control there is a balance between an intrinsic (open-loop) and extrinsic (closed-loop) contributions. We use an underactuated anthropomorphic robotic hand, the Pisa/IIT hand, instrumented with a set of pressure sensors estimating the grip force applied by humans. In a first set of experiments, we ask subjects to mimic a given force profile applied by the robot hand, to understand how human perceive and are able to reproduce a handshaking force. Using the obtained results, we implement three different handshaking controllers, in which we varied the intrinsic and extrinsic contributions and in a second set of experiments, we ask participants to evaluate them in a user study. We show that a sensorimotor delay mimicking the reaction time of the central nervous system is beneficial for making interactions more human-like. Moreover, we demonstrate that humans exploit closed-loop control for handshaking. By varying the controller we show that we can change the perceived handshake quality and also influence personality traits attributed to the robot

Singularity-Aware Design Optimization for Multi-Degree-of-Freedom Spatial Linkages

We introduce a singularity-aware design optimization method for spatial multi-degree-of-freedom mechanical linkages. At the core of our approach is an adversarial sampling strategy, which actively detects singular configurations within the targeted operation range. The detection of singularities in both forward and inverse kinematics allows for two-way bijective mappings between input and output trajectories on our optimized designs, thus enabling robust control. We demonstrate our approach on a set of simulation examples and provide additional validation on physical prototypes.ISSN:2377-376

DOC: Differentiable Optimal Control for Retargeting Motions onto Legged Robots

Legged robots are designed to perform highly dynamic motions. However, it remains challenging for users to retarget expressive motions onto these complex systems. In this paper, we present a Differentiable Optimal Control (DOC) framework that facilitates the transfer of rich motions from either animals or animations onto these robots. Interfacing with either motion capture or animation data, we formulate retargeting objectives whose parameters make them agnostic to differences in proportions and numbers of degrees of freedom between input and robot. Optimizing these parameters over the manifold spanned by optimal state and control trajectories, we minimize the retargeting error. We demonstrate the utility and efficacy of our modeling by applying DOC to a Model-Predictive Control (MPC) formulation, showing retargeting results for a family of robots of varying proportions and mass distribution. With a hardware deployment, we further show that the retargeted motions are physically feasible, while MPC ensures that the robots retain their capability to react to unexpected disturbances.ISSN:0730-0301ISSN:1557-736

Optimal Design of Flexible-Link Mechanisms With Desired Load-Displacement Profiles

Robot mechanisms that exploit compliance can perform complex tasks under uncertainty using simple control strategies, but it remains difficult to design mechanisms with a desired embodied intelligence. In this paper, we propose an automated design technique that optimizes the desired load-displacement behavior of planar flexible-link mechanisms. To do so, we replace a subset of rigid with flexible links in an existing mechanism, and optimize their rest shape. We demonstrate the efficacy of our approach on a set of examples, including two fabricated prototypes, illustrating applications for grasping and locomotion tasks.ISSN:2377-376