Learning Dexterous Manipulation from Exemplar Object Trajectories and Pre-Grasps

Learning diverse dexterous manipulation behaviors with assorted objects remains an open grand challenge. While policy learning methods offer a powerful avenue to attack this problem, they require extensive per-task engineering and algorithmic tuning. This paper seeks to escape these constraints, by developing a Pre-Grasp informed Dexterous Manipulation (PGDM) framework that generates diverse dexterous manipulation behaviors, without any task-specific reasoning or hyper-parameter tuning. At the core of PGDM is a well known robotics construct, pre-grasps (i.e. the hand-pose preparing for object interaction). This simple primitive is enough to induce efficient exploration strategies for acquiring complex dexterous manipulation behaviors. To exhaustively verify these claims, we introduce TCDM, a benchmark of 50 diverse manipulation tasks defined over multiple objects and dexterous manipulators. Tasks for TCDM are defined automatically using exemplar object trajectories from various sources (animators, human behaviors, etc.), without any per-task engineering and/or supervision. Our experiments validate that PGDM's exploration strategy, induced by a surprisingly simple ingredient (single pre-grasp pose), matches the performance of prior methods, which require expensive per-task feature/reward engineering, expert supervision, and hyper-parameter tuning. For animated visualizations, trained policies, and project code, please refer to: https://pregrasps.github.io

Focal Cortical Dysplasia with hippocampal sclerosis

Focal Cortical Dysplasia (FCD) is a group of focal developmental malformations of the cerebral cortex cytoarchitecture. FCD usually manifests as medically intractable epilepsy, especially in young children. Live patients are diagnosed by radiological examination such as magnetic resonance imaging (MRI), fluorodeoxyglucose positron emission tomography (FDG PET), magnetoencephalography (MEG), diffusion-tensor imaging (DTI), and intracranial electroencephalogram (EEG). While some cases can be missed by radiological examination, they are usually diagnosed on the histopathological examination of the surgically removed specimens of medically intractable epilepsy patients. We report a case of a young girl with cerebral palsy, mental retardation, and seizure disorder who died in her sleep. The deceased was diagnosed with FCD type III with hippocampal sclerosis on histopathological examination at autopsy. H & E stain and NeuN immunohistochemistry neuronal cell marker were used to demonstrate the findings of FCD

An Unbiased Look at Datasets for Visuo-Motor Pre-Training

Visual representation learning hold great promise for robotics, but is severely hampered by the scarcity and homogeneity of robotics datasets. Recent works address this problem by pre-training visual representations on large-scale but out-of-domain data (e.g., videos of egocentric interactions) and then transferring them to target robotics tasks. While the field is heavily focused on developing better pre-training algorithms, we find that dataset choice is just as important to this paradigm's success. After all, the representation can only learn the structures or priors present in the pre-training dataset. To this end, we flip the focus on algorithms, and instead conduct a dataset centric analysis of robotic pre-training. Our findings call into question some common wisdom in the field. We observe that traditional vision datasets (like ImageNet, Kinetics and 100 Days of Hands) are surprisingly competitive options for visuo-motor representation learning, and that the pre-training dataset's image distribution matters more than its size. Finally, we show that common simulation benchmarks are not a reliable proxy for real world performance and that simple regularization strategies can dramatically improve real world policy learning. https://data4robotics.github.ioComment: Accepted to CoRL 202

Diagnostic dilemma in a case of malignant mixed mullerian tumor of the cervix

BACKGROUND: Malignant mixed mullerian tumors (MMMT) are rare biphasic malignant neoplasm. The commonest site of their occurrence in female genital tract is body of the uterus. MMMT of the cervix is extremely rare. CASE PRESENTATION: We report the clinical, pathological and immunohistochemical profile and diagnostic difficulties in a case of giant MMMT of the cervix in a postmenopausal woman who presented with a large cervical mass. On microscopic examination, initially tumor appeared to be endometrial stromal sarcoma, however, immunohistochemical examination revealed the biphasic nature of the tumor. The malignant epithelial component was basaloid squamous carcinoma with homologous sarcomatous component. The patient was treated with surgery. However, she experienced vaginal vault recurrence four months after the initial treatment, which was successfully treated with pelvic radiotherapy. CONCLUSION: Accurate diagnosis of cervical MMMT is important for appropriate treatment of the patient

Manipulate by Seeing: Creating Manipulation Controllers from Pre-Trained Representations

The field of visual representation learning has seen explosive growth in the past years, but its benefits in robotics have been surprisingly limited so far. Prior work uses generic visual representations as a basis to learn (task-specific) robot action policies (e.g., via behavior cloning). While the visual representations do accelerate learning, they are primarily used to encode visual observations. Thus, action information has to be derived purely from robot data, which is expensive to collect! In this work, we present a scalable alternative where the visual representations can help directly infer robot actions. We observe that vision encoders express relationships between image observations as distances (e.g., via embedding dot product) that could be used to efficiently plan robot behavior. We operationalize this insight and develop a simple algorithm for acquiring a distance function and dynamics predictor, by fine-tuning a pre-trained representation on human collected video sequences. The final method is able to substantially outperform traditional robot learning baselines (e.g., 70% success v.s. 50% for behavior cloning on pick-place) on a suite of diverse real-world manipulation tasks. It can also generalize to novel objects, without using any robot demonstrations during train time. For visualizations of the learned policies please check: https://agi-labs.github.io/manipulate-by-seeing/.Comment: Oral Presentation at the International Conference on Computer Vision (ICCV), 202

Accuracy of intraoperative frozen section in the diagnosis of ovarian neoplasms: Experience at a tertiary oncology center

BACKGROUND: Epithelial ovarian neoplasms are an important cause of morbidity and mortality in women. The surgical management of ovarian neoplasms depends on their correct categorization as benign, borderline or malignant. This study was undertaken to evaluate the accuracy of intra-operative frozen section in the diagnosis of various categories of ovarian neoplasms. METHODS: Intraoperative frozen section diagnosis was retrospectively evaluated in 217 patients with suspected ovarian neoplasms who underwent surgery as primary line of therapy at our institution. This was compared with the final histopathologic diagnosis on paraffin sections. RESULTS: In 7 patients (3.2%) no opinion on frozen section was possible. In the remaining 210 patients frozen section report had a sensitivity of 100%, 93.5% and 45.5% for benign, malignant and borderline tumors. The corresponding specificities were 93.2%, 98.3% and 98.5% respectively. The overall accuracy of frozen section diagnosis was 91.2%. The majority of cases of disagreement were in the mucinous and borderline tumors. CONCLUSION: Intraoperative frozen section has high accuracy in the diagnosis of suspected ovarian neoplasms. It is a valuable tool to guide the surgical management of these patients and should be routinely used in all major oncology centers

The global cancer genomics consortium\u27s third annual symposium: From oncogenomics to cancer care

The Global Cancer Genomics Consortium (GCGC) is a cohesive network of oncologists, cancer biologists and structural and genomics experts residing in six institutions from Lisbon, United Kingdom, Japan, India, and United States. The team is using its combined resources and infrastructures to address carefully selected, shared, burning questions in cancer medicine. The Third Annual Symposium was organized by the Institute of Molecular Medicine, Lisbon Medical School, Lisbon, Portugal, from September 18 to 20, 2013. To highlight the benefits and limitations of recent advances in cancer genomics, the meeting focused on how to better translate our gains in oncogenomics to cancer patients while engaging our younger colleagues in cancer medicine at-large. Over two hundreds participants actively discussed some of the most recent advances in the areas cancer genomics, transcriptomics and cancer system biology and how to best apply such knowledge to cancer therapeutics, biomarkers discovery and drug development, and an essential role played by bio-banking throughout the process. In brief, the GCGC symposium provided a platform for students and translational cancer researchers to share their excitement and worries as we are beginning to translate the gains in oncogenomics to a better cancer patient treatment