In current robotics research there is a vast body of work on algorithms and control methods for groups of decentralized cooperating robots, called a swarm or collective. These algorithms are generally meant to control collectives of hundreds or even thousands of robots; however, for reasons of cost, time, or complexity, they are generally validated in simulation only, or on a group of a few tens of robots. To address this issue, this paper presents Kilobot, a low-cost robot designed to make testing collective algorithms on hundreds or thousands of robots accessible to robotics researchers. To enable the possibility of large Kilobot collectives where the number of robots is an order of magnitude larger than the largest that exist today, each robot is made with only $14 worth of parts and takes 5 minutes to assemble. Furthermore, the robot design allows a single user to easily operate a large Kilobot collective, such as programming, powering on, and charging all robots, which would be difficult or impossible to do with many existing robotic systems.

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Kilobot: A low cost scalable robot system for collective behaviors

Recent approaches in robot perception follow the insight that perception is facilitated by interaction with the environment. These approaches are subsumed under the term Interactive Perception (IP). This view of perception provides the following benefits. First, interaction with the environment creates a rich sensory signal that would otherwise not be present. Second, knowledge of the regularity in the combined space of sensory data and action parameters facilitates the prediction and interpretation of the sensory signal. In this survey, we postulate this as a principle for robot perception and collect evidence in its support by analyzing and categorizing existing work in this area. We also provide an overview of the most important applications of IP. We close this survey by discussing remaining open questions. With this survey, we hope to help define the field of Interactive Perception and to provide a valuable resource for future research.

Interactive Perception: Leveraging Action in Perception and Perception in Action

The morphometry of a kidney tumor revealed by contrast-enhanced Computed Tomography (CT) imaging is an important factor in clinical decision making surrounding the lesion's diagnosis and treatment. Quantitative study of the relationship between kidney tumor morphology and clinical outcomes is difficult due to data scarcity and the laborious nature of manually quantifying imaging predictors. Automatic semantic segmentation of kidneys and kidney tumors is a promising tool towards automatically quantifying a wide array of morphometric features, but no sizeable annotated dataset is currently available to train models for this task. We present the KiTS19 challenge dataset: A collection of multi-phase CT imaging, segmentation masks, and comprehensive clinical outcomes for 300 patients who underwent nephrectomy for kidney tumors at our center between 2010 and 2018. 210 (70%) of these patients were selected at random as the training set for the 2019 MICCAI KiTS Kidney Tumor Segmentation Challenge and have been released publicly. With the presence of clinical context and surgical outcomes, this data can serve not only for benchmarking semantic segmentation models, but also for developing and studying biomarkers which make use of the imaging and semantic segmentation masks.

The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes

Kilobot: A low cost robot with scalable operations designed for collective behaviors

A key challenge in intelligent robotics is creating robots that are capable of directly interacting with the world around them to achieve their goals. The last decade has seen substantial growth in research on the problem of robot manipulation, which aims to exploit the increasing availability of affordable robot arms and grippers to create robots capable of directly interacting with the world to achieve their goals. Learning will be central to such autonomous systems, as the real world contains too much variation for a robot to expect to have an accurate model of its environment, the objects in it, or the skills required to manipulate them, in advance. We aim to survey a representative subset of that research which uses machine learning for manipulation. We describe a formalization of the robot manipulation learning problem that synthesizes existing research into a single coherent framework and highlight the many remaining research opportunities and challenges.

A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms

This paper explores the idea of manipulation-aided perception and grasping in the context of sorting small objects on a tabletop. We present a robust pipeline that combines perception and manipulation to accurately sort Duplo bricks by color and size. The pipeline uses two simple motion primitives to manipulate the scene in ways that help the robot to improve its perception. This results in the ability to sort cluttered piles of Duplo bricks accurately. We present experimental results on the PR2 robot comparing brick sorting without the aid of manipulation to sorting with manipulation primitives that show the benefits of the latter, particularly as the degree of clutter in the environment increases.

https://robotics.usc.edu/publications/downloads/pub/739/

Using manipulation primitives for brick sorting in clutter

This paper explores the idea of manipulation-aided perception and grasping in the context of sorting small objects on a cluttered tabletop. We present a robust pipeline that combines perception and manipulation to accurately sort objects by some property (e.g., color, size, shape, etc.). The pipeline uses two motion primitives to manipulate the scene in ways that help the robot to improve its perception and grasps. This results in the ability to sort cluttered object piles accurately. We also present an implementation on the PR2 robot that applies our algorithm to sort Duplo bricks by color and size, and compare our method to brick sorting without the aid of manipulation. The experimental results demonstrate the benefits of our approach, particularly in environments with a high degree of clutter.

https://robotics.usc.edu/publications/downloads/pub/830/

Using Manipulation Primitives for Object Sorting in Cluttered Environments

Using a radiomics framework to quantitatively analyze tumor shape and texture features in three dimensions, we tested its ability to objectively and robustly distinguish between benign and malignant renal masses. We assessed the relative contributions of shape and texture metrics separately and together in the prediction model. Computed tomography (CT) images of 735 patients with 539 malignant and 196 benign masses were segmented in this retrospective study. Thirty-three shape and 760 texture metrics were calculated per tumor. Tumor classification models using shape, texture, and both metrics were built using random forest and AdaBoost with tenfold cross-validation. Sensitivity analyses on five sub-cohorts with respect to the acquisition phase were conducted. Additional sensitivity analyses after multiple imputation were also conducted. Model performance was assessed using AUC. Random forest classifier showed shape metrics featuring within the top 10% performing metrics regardless of phase, attaining the highest variable importance in the corticomedullary phase. Convex hull perimeter ratio is a consistently high-performing shape feature. Shape metrics alone achieved an AUC ranging 0.64–0.68 across multiple classifiers, compared with 0.67–0.75 and 0.68–0.75 achieved by texture-only and combined models, respectively. Shape metrics alone attain high prediction performance and high variable importance in the combined model, while being independent of the acquisition phase (unlike texture). Shape analysis therefore should not be overlooked in its potential to distinguish benign from malignant tumors, and future radiomics platforms powered by machine learning should harness both shape and texture metrics. • Current radiomics research is heavily weighted towards texture analysis, but quantitative shape metrics should not be ignored in their potential to distinguish benign from malignant renal tumors.
 • Shape metrics alone can attain high prediction performance and demonstrate high variable importance in the combined shape and texture radiomics model.
 • Any future radiomics platform powered by machine learning should harness both shape and texture metrics, especially since tumor shape (unlike texture) is independent of the acquisition phase and more robust from the imaging variations.

Shape and texture-based radiomics signature on CT effectively discriminates benign from malignant renal masses.

Quantitative Contour Analysis as an Image-based Discriminator Between Benign and Malignant Renal Tumors.

Robotic environment exploration in cluttered environments is a challenging problem. The number and variety of objects present not only make perception very difficult but also introduce many constraints for robot navigation and manipulation. In this paper, we investigate the idea of exploring a small, bounded environment (e.g., the shelf of a home refrigerator) by prehensile and non-prehensile manipulation of the objects it contains. The presence of multiple objects results in partial and occluded views of the scene. This inherent uncertainty in the scene's state forces the robot to adopt an observe-plan-act strategy and interleave planning with execution. Objects occupying the space and potentially occluding other hidden objects are rearranged to reveal more of the unseen area. The environment is considered explored when the state (free or occupied) of every voxel in the volume is known. The presented algorithm can be easily adapted to real world problems like object search, taking inventory, and mapping. We evaluate our planner in simulation using various metrics like planning time, number of actions required, and length of planning horizon. We then present an implementation on the PR2 robot and use it for object search in clutter.

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Megha Gupta

Papers

Using manipulation primitives for brick sorting in clutter

Using Manipulation Primitives for Object Sorting in Cluttered Environments

Shape and texture-based radiomics signature on CT effectively discriminates benign from malignant renal masses.

Quantitative Contour Analysis as an Image-based Discriminator Between Benign and Malignant Renal Tumors.

Interactive environment exploration in clutter