ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics

Physical simulators have been widely used in robot planning and control. Among them, differentiable simulators are particularly favored, as they can be incorporated into gradient-based optimization algorithms that are efficient in solving inverse problems such as optimal control and motion planning. Simulating deformable objects is, however, more challenging compared to rigid body dynamics. The underlying physical laws of deformable objects are more complex, and the resulting systems have orders of magnitude more degrees of freedom and therefore they are significantly more computationally expensive to simulate. Computing gradients with respect to physical design or controller parameters is typically even more computationally challenging. In this paper, we propose a real-time, differentiable hybrid Lagrangian-Eulerian physical simulator for deformable objects, ChainQueen, based on the Moving Least Squares Material Point Method (MLS-MPM). MLS-MPM can simulate deformable objects including contact and can be seamlessly incorporated into inference, control and co-design systems. We demonstrate that our simulator achieves high precision in both forward simulation and backward gradient computation. We have successfully employed it in a diverse set of control tasks for soft robots, including problems with nearly 3,000 decision variables.Comment: In submission to ICRA 2019. Supplemental Video: https://www.youtube.com/watch?v=4IWD4iGIsB4 Project Page: https://github.com/yuanming-hu/ChainQuee

Constraint-aware coordinated construction of generic structures

This paper presents a constraint-aware decentralized approach to construction with teams of robots. We present an extension to existing work on a distributed controller for robotic construction of simple structures. Our previous work described a set of adaptive algorithms for constructing truss structures given a target geometry using continuous and graph-based equal-mass partitioning [1], [2]. Using this work as a foundation, we present an algorithm which performs construction tasks and conforms to physical constraints while considering those constraints to parallelize tasks. This is accomplished by defining a mass function which reflects the priority of part placement and prevents physically impossible states. This mass function generates a set of pointmasses in ℝn, and we present a novel algorithm for finding a locally optimal, equal-mass, convex tessellation of such a set.Boeing CompanyNational Science Foundation (U.S.).National Science Foundation (U.S.). Office of Emerging Frontiers in Research and Innovation (Grant #0735953)United States. Army Research Office. Multidisciplinary University Research Initiative. Swarms of Autonomous Robots and Mobile Sensors Project (Grant number N0014-09-1051)United States. Army Research Office. Multidisciplinary University Research Initiative. Scalable (Grant number 544252

Safe Local Navigation for Visually Impaired Users With a Time-of-Flight and Haptic Feedback Device

This paper presents ALVU (Array of Lidars and Vibrotactile Units), a contactless, intuitive, hands-free, and discreet wearable device that allows visually impaired users to detect low- and high-hanging obstacles, as well as physical boundaries in their immediate environment. The solution allows for safe local navigation in both confined and open spaces by enabling the user to distinguish free space from obstacles. The device presented is composed of two parts: a sensor belt and a haptic strap. The sensor belt is an array of time-of-flight distance sensors worn around the front of a user's waist, and the pulses of infrared light provide reliable and accurate measurements of the distances between the user and surrounding obstacles or surfaces. The haptic strap communicates the measured distances through an array of vibratory motors worn around the user's upper abdomen, providing haptic feedback. The linear vibration motors are combined with a point-loaded pretensioned applicator to transmit isolated vibrations to the user. We validated the device's capability in an extensive user study entailing 162 trials with 12 blind users. Users wearing the device successfully walked through hallways, avoided obstacles, and detected staircases.Andrea Bocelli FoundationNational Science Foundation (U.S.) (Grant NSF IIS1226883

Antisense-induced exon skipping for duplications in Duchenne muscular dystrophy

<p>Abstract</p> <p>Background</p> <p>Antisense-mediated exon skipping is currently one of the most promising therapeutic approaches for Duchenne muscular dystrophy (DMD). Using antisense oligonucleotides (AONs) targeting specific exons the DMD reading frame is restored and partially functional dystrophins are produced. Following proof of concept in cultured muscle cells from patients with various deletions and point mutations, we now focus on single and multiple exon duplications. These mutations are in principle ideal targets for this approach since the specific skipping of duplicated exons would generate original, full-length transcripts.</p> <p>Methods</p> <p>Cultured muscle cells from DMD patients carrying duplications were transfected with AONs targeting the duplicated exons, and the dystrophin RNA and protein were analyzed.</p> <p>Results</p> <p>For two brothers with an exon 44 duplication, skipping was, even at suboptimal transfection conditions, so efficient that both exons 44 were skipped, thus generating, once more, an out-of-frame transcript. In such cases, one may resort to multi-exon skipping to restore the reading frame, as is shown here by inducing skipping of exon 43 and both exons 44. By contrast, in cells from a patient with an exon 45 duplication we were able to induce single exon 45 skipping, which allowed restoration of wild type dystrophin. The correction of a larger duplication (involving exons 52 to 62), by combinations of AONs targeting the outer exons, appeared problematic due to inefficient skipping and mistargeting of original instead of duplicated exons.</p> <p>Conclusion</p> <p>The correction of DMD duplications by exon skipping depends on the specific exons targeted. Its options vary from the ideal one, restoring for the first time the true, wild type dystrophin, to requiring more 'classical' skipping strategies, while the correction of multi-exon deletions may need the design of tailored approaches.</p

Pedestrian detection in far-infrared daytime images using a hierarchical codebook of SURF

One of the main challenges in intelligent vehicles concerns pedestrian detection for driving assistance. Recent experiments have showed that state-of-the-art descriptors provide better performances on the far-infrared (FIR) spectrum than on the visible one, even in daytime conditions, for pedestrian classification. In this paper, we propose a pedestrian detector with on-board FIR camera. Our main contribution is the exploitation of the specific characteristics of FIR images to design a fast, scale-invariant and robust pedestrian detector. Our system consists of three modules, each based on speeded-up robust feature (SURF) matching. The first module allows generating regions-of-interest (ROI), since in FIR images of the pedestrian shapes may vary in large scales, but heads appear usually as light regions. ROI are detected with a high recall rate with the hierarchical codebook of SURF features located in head regions. The second module consists of pedestrian full-body classification by using SVM. This module allows one to enhance the precision with low computational cost. In the third module, we combine the mean shift algorithm with inter-frame scale-invariant SURF feature tracking to enhance the robustness of our system. The experimental evaluation shows that our system outperforms, in the FIR domain, the state-of-the-art Haar-like Adaboost-cascade, histogram of oriented gradients (HOG)/linear SVM (linSVM) and MultiFtrpedestrian detectors, trained on the FIR images

Robotic load balancing for mobility-on-demand systems

In this paper we develop methods for maximizing the throughput of a mobility-on-demand urban transportation system. We consider a finite group of shared vehicles, located at a set of stations. Users arrive at the stations, pickup vehicles, and drive (or are driven) to their destination station where they drop-off the vehicle. When some origins and destinations are more popular than others, the system will inevitably become out of balance: vehicles will build up at some stations, and become depleted at others. We propose a robotic solution to this rebalancing problem that involves empty robotic vehicles autonomously driving between stations. Specifically, we utilize a fluid model for the customers and vehicles in the system. Then, we develop a rebalancing policy that lets every station reach an equilibrium in which there are excess vehicles and no waiting customers and that minimizes the number of robotic vehicles performing rebalancing trips. We show that the optimal rebalancing policy can be found as the solution to a linear program. We use this solution to develop a real-time rebalancing policy which can operate in highly variable environments. Finally, we verify policy performance in a simulated mobility-on-demand environment and in hardware experiments.Singapore-MIT Alliance for Research and Technology CenterUnited States. Office of Naval Research (Grant N000140911051)National Science Foundation (U.S.) (Grant EFRI0735953

BxDF material acquisition, representation, and rendering for VR and design

Photorealistic and physically-based rendering of real-world environments with high fidelity materials is important to a range of applications, including special effects, architectural modelling, cultural heritage, computer games, automotive design, and virtual reality (VR). Our perception of the world depends on lighting and surface material characteristics, which determine how the light is reflected, scattered, and absorbed. In order to reproduce appearance, we must therefore understand all the ways objects interact with light, and the acquisition and representation of materials has thus been an important part of computer graphics from early days. Nevertheless, no material model nor acquisition setup is without limitations in terms of the variety of materials represented, and different approaches vary widely in terms of compatibility and ease of use. In this course, we describe the state of the art in material appearance acquisition and modelling, ranging from mathematical BSDFs to data-driven capture and representation of anisotropic materials, and volumetric/thread models for patterned fabrics. We further address the problem of material appearance constancy across different rendering platforms. We present two case studies in architectural and interior design. The first study demonstrates Yulio, a new platform for the creation, delivery, and visualization of acquired material models and reverse engineered cloth models in immersive VR experiences. The second study shows an end-to-end process of capture and data-driven BSDF representation using the physically-based Radiance system for lighting simulation and rendering

Targeted Skipping of Human Dystrophin Exons in Transgenic Mouse Model Systemically for Antisense Drug Development

Antisense therapy has recently been demonstrated with great potential for targeted exon skipping and restoration of dystrophin production in cultured muscle cells and in muscles of Duchenne Muscular Dystrophy (DMD) patients. Therapeutic values of exon skipping critically depend on efficacy of the drugs, antisense oligomers (AOs). However, no animal model has been established to test AO targeting human dystrophin exon in vivo systemically. In this study, we applied Vivo-Morpholino to the hDMD/mdx mouse, a transgenic model carrying the full-length human dystrophin gene with mdx background, and achieved for the first time more than 70% efficiency of targeted human dystrophin exon skipping in vivo systemically. We also established a GFP-reporter myoblast culture to screen AOs targeting human dystrophin exon 50. Antisense efficiency for most AOs is consistent between the reporter cells, human myoblasts and in the hDMD/mdx mice in vivo. However, variation in efficiency was also clearly observed. A combination of in vitro cell culture and a Vivo-Morpholino based evaluation in vivo systemically in the hDMD/mdx mice therefore may represent a prudent approach for selecting AO drug and to meet the regulatory requirement

Curb-intersection feature based Monte Carlo Localization on urban roads

One of the most prominent features on an urban road is the curb, which defines the boundary of a road surface. An intersection is a junction of two or more roads, appearing where no curb exists. The combination of curb and intersection features and their idiosyncrasies carry significant information about the urban road network that can be exploited to improve a vehicle's localization. This paper introduces a Monte Carlo Localization (MCL) method using the curb-intersection features on urban roads. We propose a novel idea of “Virtual LIDAR” to get the measurement models for these features. Under the MCL framework, above road observation is fused with odometry information, which is able to yield precise localization. We implement the system using a single tilted 2D LIDAR on our autonomous test bed and show robust performance in the presence of occlusion from other vehicles and pedestrians