Learning Motion Predictors for Smart Wheelchair using Autoregressive Sparse Gaussian Process

Constructing a smart wheelchair on a commercially available powered wheelchair (PWC) platform avoids a host of seating, mechanical design and reliability issues but requires methods of predicting and controlling the motion of a device never intended for robotics. Analog joystick inputs are subject to black-box transformations which may produce intuitive and adaptable motion control for human operators, but complicate robotic control approaches; furthermore, installation of standard axle mounted odometers on a commercial PWC is difficult. In this work, we present an integrated hardware and software system for predicting the motion of a commercial PWC platform that does not require any physical or electronic modification of the chair beyond plugging into an industry standard auxiliary input port. This system uses an RGB-D camera and an Arduino interface board to capture motion data, including visual odometry and joystick signals, via ROS communication. Future motion is predicted using an autoregressive sparse Gaussian process model. We evaluate the proposed system on real-world short-term path prediction experiments. Experimental results demonstrate the system's efficacy when compared to a baseline neural network model.Comment: The paper has been accepted to the International Conference on Robotics and Automation (ICRA2018

Nitric oxide-induced lipophagic defects contribute to testosterone deficiency in rats with spinal cord injury

IntroductionMales with acute spinal cord injury (SCI) frequently exhibit testosterone deficiency and reproductive dysfunction. While such incidence rates are high in chronic patients, the underlying mechanisms remain elusive.Methods and resultsHerein, we generated a rat SCI model, which recapitulated complications in human males, including low testosterone levels and spermatogenic disorders. Proteomics analyses showed that the differentially expressed proteins were mostly enriched in lipid metabolism and steroid metabolism and biosynthesis. In SCI rats, we observed that testicular nitric oxide (NO) levels were elevated and lipid droplet-autophagosome co-localization in testicular interstitial cells was decreased. We hypothesized that NO impaired lipophagy in Leydig cells (LCs) to disrupt testosterone biosynthesis and spermatogenesis. As postulated, exogenous NO donor (S-nitroso-N-acetylpenicillamine (SNAP)) treatment markedly raised NO levels and disturbed lipophagy via the AMPK/mTOR/ULK1 pathway, and ultimately impaired testosterone production in mouse LCs. However, such alterations were not fully observed when cells were treated with an endogenous NO donor (L-arginine), suggesting that mouse LCs were devoid of an endogenous NO-production system. Alternatively, activated (M1) macrophages were predominant NO sources, as inducible NO synthase inhibition attenuated lipophagic defects and testosterone insufficiency in LCs in a macrophage-LC co-culture system. In scavenging NO (2-4-carboxyphenyl-4,4,5,5-tetramethylimidazoline-1-oxyl-3-oxide (CPTIO)) we effectively restored lipophagy and testosterone levels both in vitro and in vivo, and importantly, spermatogenesis in vivo. Autophagy activation by LYN-1604 also promoted lipid degradation and testosterone synthesis.DiscussionIn summary, we showed that NO-disrupted-lipophagy caused testosterone deficiency following SCI, and NO clearance or autophagy activation could be effective in preventing reproductive dysfunction in males with SCI

TempCLR: Reconstructing Hands via Time-Coherent Contrastive Learning

We introduce TempCLR, a new time-coherent contrastive learning approach for the structured regression task of 3D hand reconstruction. Unlike previous time-contrastive methods for hand pose estimation, our framework considers temporal consistency in its augmentation scheme, and accounts for the differences of hand poses along the temporal direction. Our data-driven method leverages unlabelled videos and a standard CNN, without relying on synthetic data, pseudo-labels, or specialized architectures. Our approach improves the performance of fully-supervised hand reconstruction methods by 15.9% and 7.6% in PA-V2V on the HO-3D and FreiHAND datasets respectively, thus establishing new state-of-the-art performance. Finally, we demonstrate that our approach produces smoother hand reconstructions through time, and is more robust to heavy occlusions compared to the previous state-of-the-art which we show quantitatively and qualitatively

Learning To Disambiguate Strongly Interacting Hands via Probabilistic Per-Pixel Part Segmentation

In natural conversation and interaction, our hands often overlap or are in contact with each other. Due to the homogeneous appearance of hands, this makes estimating the 3D pose of interacting hands from images difficult. In this paper we demonstrate that self-similarity, and the resulting ambiguities in assigning pixel observations to the respective hands and their parts, is a major cause of the final 3D pose error. Motivated by this insight, we propose DIGIT, a novel method for estimating the 3D poses of two interacting hands from a single monocular image. The method consists of two interwoven branches that process the input imagery into a per-pixel semantic part segmentation mask and a visual feature volume. In contrast to prior work, we do not decouple the segmentation from the pose estimation stage, but rather leverage the per-pixel probabilities directly in the downstream pose estimation task. To do so, the part probabilities are merged with the visual features and processed via fully-convolutional layers. We experimentally show that the proposed approach achieves new state-of-the-art performance on the InterHand2.6M dataset. We provide detailed ablation studies to demonstrate the efficacy of our method and to provide insights into how the modelling of pixel ownership affects 3D hand pose estimation