79 research outputs found

    Self-supervised online learning of basic object push affordances

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    Continuous learning of object affordances in a cognitive robot is a challenging problem, the solution to which arguably requires a developmental approach. In this paper, we describe scenarios where robotic systems interact with household objects by pushing them using robot arms while observing the scene with cameras, and which must incrementally learn, without external supervision, both the effect classes that emerge from these interactions as well as a discriminative model for predicting them from object properties. We formalize the scenario as a multi-view learning problem where data co-occur over two separate data views over time, and we present an online learning framework that uses a self-supervised form of learning vector quantization to build the discriminative model. In various experiments, we demonstrate the effectiveness of this approach in comparison with related supervised methods using data from experiments performed using two different robotic platforms

    Self-Supervised Traversability Prediction by Learning to Reconstruct Safe Terrain

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    Navigating off-road with a fast autonomous vehicle depends on a robust perception system that differentiates traversable from non-traversable terrain. Typically, this depends on a semantic understanding which is based on supervised learning from images annotated by a human expert. This requires a significant investment in human time, assumes correct expert classification, and small details can lead to misclassification. To address these challenges, we propose a method for predicting high- and low-risk terrains from only past vehicle experience in a self-supervised fashion. First, we develop a tool that projects the vehicle trajectory into the front camera image. Second, occlusions in the 3D representation of the terrain are filtered out. Third, an autoencoder trained on masked vehicle trajectory regions identifies low- and high-risk terrains based on the reconstruction error. We evaluated our approach with two models and different bottleneck sizes with two different training and testing sites with a fourwheeled off-road vehicle. Comparison with two independent test sets of semantic labels from similar terrain as training sites demonstrates the ability to separate the ground as low-risk and the vegetation as high-risk with 81.1% and 85.1% accuracy

    Rover Relocalization for Mars Sample Return by Virtual Template Synthesis and Matching

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    We consider the problem of rover relocalization in the context of the notional Mars Sample Return campaign. In this campaign, a rover (R1) needs to be capable of autonomously navigating and localizing itself within an area of approximately 50 x 50 m using reference images collected years earlier by another rover (R0). We propose a visual localizer that exhibits robustness to the relatively barren terrain that we expect to find in relevant areas, and to large lighting and viewpoint differences between R0 and R1. The localizer synthesizes partial renderings of a mesh built from reference R0 images and matches those to R1 images. We evaluate our method on a dataset totaling 2160 images covering the range of expected environmental conditions (terrain, lighting, approach angle). Experimental results show the effectiveness of our approach. This work informs the Mars Sample Return campaign on the choice of a site where Perseverance (R0) will place a set of sample tubes for future retrieval by another rover (R1).Comment: To appear in IEEE Robotics and Automation Letters (RA-L) and IEEE International Conference on Robotics and Automation (ICRA 2021

    Segmentation of the mouse fourth deep lumbrical muscle connectome reveals concentric organization of motor units

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    Connectomic analysis of the nervous system aims to discover and establish principles that underpin normal and abnormal neural connectivity and function. Here we performed image analysis of motor unit connectivity in the fourth deep lumbrical muscle (4DL) of mice, using transgenic expression of fluorescent protein in motor neurones as a morphological reporter. We developed a method that accelerated segmentation of confocal image projections of 4DL motor units, by applying high resolution (63×, 1.4 NA objective) imaging or deconvolution only where either proved necessary, in order to resolve axon crossings that produced ambiguities in the correct assignment of axon terminals to identified motor units imaged at lower optical resolution (40×, 1.3 NA). The 4DL muscles contained between 4 and 9 motor units and motor unit sizes ranged in distribution from 3 to 111 motor nerve terminals per unit. Several structural properties of the motor units were consistent with those reported in other muscles, including suboptimal wiring length and distribution of motor unit size. Surprisingly, however, small motor units were confined to a region of the muscle near the nerve entry point, whereas their larger counterparts were progressively more widely dispersed, suggesting a previously unrecognised form of segregated motor innervation in this muscle. We also found small but significant differences in variance of motor endplate length in motor units, which correlated weakly with their motor unit size. Thus, our connectomic analysis has revealed a pattern of concentric innervation that may perhaps also exist in other, cylindrical muscles that have not previously been thought to show segregated motor unit organisation. This organisation may be the outcome of competition during postnatal development based on intrinsic neuronal differences in synaptic size or synaptic strength that generates a territorial hierarchy in motor unit size and disposition

    Some reminiscences on studies of age-dependent and activity-dependent degeneration of sensory and motor endings in mammalian skeletal muscle

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    I present here an overview of research on the biology of neuromuscular sensory and motor endings that was inspired and influenced partly by my educational experience in the Department of Zoology at the University of Durham, from 1971 to 1974. I allude briefly to neuromuscular synaptic structure and function in dystrophic mice, influences of activity on synapse elimination in development and regeneration, and activity-dependent protection and degeneration of neuromuscular junctions in Wld(S) mice
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