Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching

This paper presents a robotic pick-and-place system that is capable of grasping and recognizing both known and novel objects in cluttered environments. The key new feature of the system is that it handles a wide range of object categories without needing any task-specific training data for novel objects. To achieve this, it first uses a category-agnostic affordance prediction algorithm to select and execute among four different grasping primitive behaviors. It then recognizes picked objects with a cross-domain image classification framework that matches observed images to product images. Since product images are readily available for a wide range of objects (e.g., from the web), the system works out-of-the-box for novel objects without requiring any additional training data. Exhaustive experimental results demonstrate that our multi-affordance grasping achieves high success rates for a wide variety of objects in clutter, and our recognition algorithm achieves high accuracy for both known and novel grasped objects. The approach was part of the MIT-Princeton Team system that took 1st place in the stowing task at the 2017 Amazon Robotics Challenge. All code, datasets, and pre-trained models are available online at http://arc.cs.princeton.eduComment: Project webpage: http://arc.cs.princeton.edu Summary video: https://youtu.be/6fG7zwGfIk

Hydrogenated amorphous silicon films with significantly improved stability

NRC publication: Ye

Effects of seasonal changes on T-helper 1/ T-helper 2 immune balance and eczema onset in rats

Objective: To explore the impact of seasonal changes on the T-helper 1 (Th1)/T-helper 2 (Th2) immune balance of the body in order to elucidate the internal immunological mechanisms underlying seasonal eczema. Methods: Eighty 4-week-old male Sprague–Dawley rats were divided into 5 season groups kept in corresponding season simulation environments, and subdivided into the model group and the control group. The Eczema Area and Severity Index (EASI) and scratching frequency were evaluated. The serum levels of immunoglobulin E (IgE), interleukin-2 (IL-2), interleukin-12 (IL-12), and interferon-gamma (IFN-γ), interleukin-4 (IL-4), interleukin-25 (IL-25), and interleukin-31 (IL-31), and melatonin (MT), as well as the MT receptor (MTR) levels in the spleen, were detected by enzyme-linked immunosorbent assay analysis. The mRNA expression levels of T-bet and GATA3 were detected by quantitative real-time polymerase chain reaction. Results: EASI scores and the scratching frequency of the model group were higher in the long summer than in the other 4 seasons. Meanwhile, the serum levels of IgE and the Th2 cytokines IL-4, IL-25, and IL-31, as well as GATA3 mRNA expression levels, were high during the long summer in both groups. However, the serum levels of the Th1 cytokines IL-2, IL-12, and IFN-γ, as well as MT, MTR, and T-bet mRNA levels, were lower during the long summer. In all 5 seasonal groups (spring, summer, long summer, autumn, and winter), the levels of all immune factors, especially IL-4 and IL-31, were higher in the model group than those in the control group, while the concentrations of MT and MTR were lower. Conclusion: Under long light, hot, and humid conditions in the long summer, the body is more likely to suffer from Th2-dominated immune imbalance. This is the internal mechanism behind the high incidence and severity of eczema during the long summer. MT and MTR play a key role in the seasonal onset of eczema

Add-on sirolimus for the treatment of mild or moderate systemic lupus erythematosus via T lymphocyte subsets balance

Objective The efficacy of sirolimus in treating severe or refractory systemic lupus erythematosus (SLE) has been confirmed by small-scale clinical trials. However, few studies focused on mild or moderate SLE. Therefore, in this study we elucidated clinical efficacy of add-on sirolimus in patients with mild or moderate SLE.Methods Data of 17 consecutive patients with SLE were retrospectively collected. SLE Disease Activity Index-2000 (SLEDAI-2K), clinical manifestation, laboratory data and peripheral T lymphocyte subsets with cytokines were collected before and 6 months after sirolimus add-on treatment. T cell subsets were detected by flow cytometry and cytokines were determined by multiplex bead-based flow fluorescent immunoassay simultaneously. Twenty healthy controls matched with age and sex were also included in our study.Results (1) The numbers of peripheral blood lymphocytes, T cells, T helper (Th) cells, regulatory T (Treg) cells, Th1 cells, Th2 cells and Treg/Th17 ratios in patients with SLE were significantly lower, while the numbers of Th17 cells were evidently higher than those of healthy control (p<0.05). (2) After 6 months of sirolimus add-on treatment, urinary protein, pancytopenia, immunological indicators and SLEDAI-2K in patients with SLE were distinctively improved compared with those before sirolimus treatment (p<0.05). (3) The numbers of peripheral blood lymphocytes, T cells, Th cells, Treg cells, Th2 cells and the ratios of Treg/Th17 in patients with SLE after treatment were clearly higher than those before (p<0.05). (4) The levels of plasma interleukin (IL)-5, IL-6 and IL-10 in patients with SLE decreased notably, conversely the IL-4 levels increased remarkably compared with pretreatment (p<0.05).Conclusions (1) Patients with SLE presented imbalanced T cell subsets, especially the decreased ratio of Treg/Th17. (2) Sirolimus add-on treatment ameliorated clinical involvement, serological abnormalities and disease activity without adverse reactions in patients with SLE. (3) The multi-target therapy facilitates the enhanced numbers of Treg cells, Treg/Th17 imbalance and anti-inflammatory cytokines, simultaneously, reducing inflammatory cytokines

Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching

This paper presents a robotic pick-and-place system that is capable of grasping and recognizing both known and novel objects in cluttered environments. The key new feature of the system is that it handles a wide range of object categories without needing any task-specific training data for novel objects. To achieve this, it first uses a category-agnostic affordance prediction algorithm to select and execute among four different grasping primitive behaviors. It then recognizes picked objects with a cross-domain image classification framework that matches observed images to product images. Since product images are readily available for a wide range of objects (e.g., from the web), the system works out-of-the-box for novel objects without requiring any additional training data. Exhaustive experimental results demonstrate that our multi-affordance grasping achieves high success rates for a wide variety of objects in clutter, and our recognition algorithm achieves high accuracy for both known and novel grasped objects. The approach was part of the MIT-Princeton Team system that took 1st place in the stowing task at the 2017 Amazon Robotics Challenge. All code, datasets, and pre-trained models are available online at http://arc.cs.princeton.edu