Search

This paper describes fundamental principles for two-dimensional pattern design of folded robots, specifically mobile robots consisting of closed-loop kinematic linkage mechanisms. Three fundamental methods for designing closed-chain folded four-bar linkages – the basic building block of these devices – are introduced. Modular connection strategies are also introduced as a method to overcome the challenges of designing assemblies of linkages from a two-dimensional sheet. The result is a design process that explores the tradeoffs between the complexity of linkage fabrication and also allows the designer combine multiple functions or modes of locomotion. A redesigned modular robot capable of multi-modal locomotion and grasping is presented to embody these design principles., National Science Foundation (Grants EFRI-1240383 and CCF- 1138967)

© Springer International Publishing Switzerland 2016. Thiswork presents an autonomous soft-bodied robotic fish that is hydraulically actuated and capable of sustained swimming in three dimensions. The design of a fish-like soft body has been extended to deform under hydraulic instead of pneumatic power. Moreover, a new closed-circuit drive system that uses water as a transmission fluid is used to actuate the soft body. Circulation of water through internal body channels provides control over the fish’s caudal fin propulsion and yaw motion. A new fabrication technique for the soft body is described, which allows for arbitrary internal fluidic channels, enabling a wide-range of continuous body deformations. Furthermore, dynamic diving capabilities are introduced through pectoral fins as dive planes. These innovations enable prolonged fish-like locomotion in three dimensions., NSF (Grants 1117178, 1133224, IIS1226883 and CCF1138967), NSF (Award 1122374)

© 2020 IEEE. This paper tackles the challenge of predicting grasp failures in soft hands before they happen, by combining deep learning with a sensing strategy based on distributed Inertial Measurement Units. We propose two neural architectures, which we implemented and tested with an articulated soft hand-the Pisa/IIT SoftHand-and a continuously deformable soft hand-the RBO Hand. The first architecture (Classifier) implements a-posteriori detection of the failure event, serving as a test-bench to assess the possibility of extracting failure information from the discussed input signals. This network reaches up to 100% of accuracy within our experimental validation. Motivated by these results, we introduce a second architecture (Predictor), which is the main contribution of the paper. This network works on-line and takes as input a multidimensional continuum stream of raw signals coming from the Inertial Measurement Units. The network is trained to predict the occurrence in the near future of a failure event. The Predictor detects 100% of failures with both hands, with the detection happening on average 1.96 seconds before the actual failing occurs-leaving plenty of time to an hypothetical controller to react.

© 2017 IEEE. Context-aware segmentation of laparoscopic and robot assisted surgical video has been shown to improve performance and perioperative workflow efficiency, and can be used for education and time-critical consultation. Modern pressures on productivity preclude manual video analysis, and hospital policies and legacy infrastructure are often prohibitive of recording and storing large amounts of data. In this paper we present a system that automatically generates a video segmentation of laparoscopic and robot-assisted procedures according to their underlying surgical phases using minimal computational resources, and low amounts of training data. Our system uses an SVM and HMM in combination with an augmented feature space that captures the variability of these video streams without requiring analysis of the nonrigid and variable environment. By using the data reduction capabilities of online k-segment coreset algorithms we can efficiently produce results of approximately equal quality, in realtime. We evaluate our system in cross-validation experiments and propose a blueprint for piloting such a system in a real operating room environment with minimal risk factors.

In this paper we propose a new path planning algorithm for coverage tasks in unknown environments that does not rely on recursive search optimization. Given a sensory function that captures the interesting locations in the environment and can be learned, the goal is to compute a set of closed paths that allows a single robot or a multi-robot system to sense/cover the environment according to this function. We present an online adaptive distributed controller, based on gradient descent of a Voronoi-based cost function, that generates these closed paths, which the robots can travel for any coverage task, such as environmental mapping or surveillance. The controller uses local information only, and drives the robots to simultaneously identify the regions of interest and shape their paths online to sense these regions. Lyapunov theory is used to show asymptotic convergence of the system based on a Voronoi-based coverage criterion. Simulated and experimental results, that support the proposed approach, are presented for the single-robot and multi-robot cases in known and unknown environments. © The Author(s) 2013., ONR (MURI award N00014-09-1-1051), NSF Graduate Research Fellowship (award 0645960)

We investigate a data-driven approach to robotic path planning and analyze its performance in the context of interception tasks. Trajectories of moving objects often contain repeated patterns of motion, and learning those patterns can yield interception paths that succeed more often. We therefore propose an original trajectory clustering algorithm for extracting motion patterns from trajectory data and demonstrate its effectiveness over the more common clustering approach of using k-means. We use the results to build a Hidden Markov Model of a target's motion and predict movement. Our simulations show that these predictions lead to more effective interception. The results of this work have potential applications in coordination of multi-robot systems, tracking and surveillance tasks, and dynamic obstacle avoidance. © 2012 IEEE., ONR MURI (Grants N00014-09-1-1051 and N00014-09-1-1031), NSF (Award IIS-1117178)

© 2017 IEEE. The multi-robot path planning problem has been extensively studied for the cases of flying and driving vehicles. However, path planning for the case of vehicles that can both fly and drive has not yet been considered. Driving robots, while stable and energy efficient, are limited to mostly flat terrain. Quadcopters, on the other hand, are agile and highly mobile but have low energy efficiency and limited battery life. Combining a quadcopter with a driving mechanism presents a path planning challenge by enabling the selection of paths based off of both time and energy consumption. In this paper, we introduce a framework for multi-robot path planning for a swarm of flying-and-driving vehicles. By putting a lightweight driving platform on a quadcopter, we create a robust vehicle with an energy efficient driving mode and an agile flight mode. We extend two algorithms, priority planning with Safe Interval Path Planning and a multi-commodity network flow ILP, to accommodate multimodal locomotion, and we show that these algorithms can indeed plan collision-free paths for flying-and-driving vehicles on 3D graphs. Finally, we demonstrate that our system is able to plan paths and control the motions of 8 of our vehicles in a miniature town.

In this paper we present a method for automatically generating optimal robot paths satisfying high-level mission specifications. The motion of the robot in the environment is modeled as a weighted transition system. The mission is specified by an arbitrary linear temporal-logic (LTL) formula over propositions satisfied at the regions of a partitioned environment. The mission specification contains an optimizing proposition, which must be repeatedly satisfied. The cost function that we seek to minimize is the maximum time between satisfying instances of the optimizing proposition. For every environment model, and for every formula, our method computes a robot path that minimizes the cost function. The problem is motivated by applications in robotic monitoring and data-gathering. In this setting, the optimizing proposition is satisfied at all locations where data can be uploaded, and the LTL formula specifies a complex data-collection mission. Our method utilizes Büchi automata to produce an automaton (which can be thought of as a graph) whose runs satisfy the temporal-logic specification. We then present a graph algorithm that computes a run corresponding to the optimal robot path. We present an implementation for a robot performing data collection in a road-network platform. © SAGE Publications 2011.

© 2017 IEEE. microMVP is an affordable, portable, and open source micro-scale mobile robot platform designed for robotics research and education. As a complete and unique multi-vehicle platform enabled by 3D printing and the maker culture, microMVP can be easily reproduced and requires little maintenance: a set of six micro vehicles, each measuring 8 × 5 × 6 cubic centimeters and weighing under 100 grams, and the accompanying tracking platform can be fully assembled in under two hours, all from readily available components. In this paper, we describe microMVP's hardware and software architecture, and the design thoughts that go into the making of the platform. The capabilities of microMVP APIs are then demonstrated with several single- and multi-robot path and motion planning algorithms. microMVP supports all common operation systems., NSF (Grant 1617744)

We present an efficient coresets-based neural network compression algorithm that sparsifies the parameters of a trained fully-connected neural network in a manner that provably approximates the network's output. Our approach is based on an importance sampling scheme that judiciously defines a sampling distribution over the neural network parameters, and as a result, retains parameters of high importance while discarding redundant ones. We leverage a novel, empirical notion of sensitivity and extend traditional coreset constructions to the application of compressing parameters. Our theoretical analysis establishes guarantees on the size and accuracy of the resulting compressed network and gives rise to generalization bounds that may provide new insights into the generalization properties of neural networks. We demonstrate the practical effectiveness of our algorithm on a variety of neural network configurations and real-world data sets.

© 2020 IEEE. The success of hybrid suction + parallel-jaw grippers in the Amazon Robotics/Picking Challenge have demonstrated the effectiveness of multimodal grasping approaches. However, existing multimodal grippers combine grasping modes in isolation and do not incorporate the benefits of compliance found in soft robotic manipulators. In this paper, we present a gripper that integrates three modes of grasping: suction, parallel jaw, and soft fingers. Using complaint handed shearing auxetics actuators as the foundation, this gripper is able to multiplex manipulation by creating unique grasping primitives through permutations of these grasping techniques. This gripper is able to grasp 88% of tested objects, 14% of which could only be grasped using a combination of grasping modes. The gripper is also able to perform in-hand object re-orientation of flat objects without the need for pre-grasp manipulation., National Science Foundation (Grants 1830901, 1122374)

© 2019 IEEE. We propose stochastic control policies for gathering a group of embodied agents in a two-dimensional square tile environment. The policies are fully decentralized and can be executed on anonymous, oblivious agents with chirality, but no sense of orientation. The agents require only 4 ternary digits of information. We prove that a group of agents, irrespective of initial positions, will almost surely reach a Pareto optimal configuration in finite time. For one of the control policies, computer simulations show that groups of up to 20 agents consistently reach Pareto optimal configurations, whereas groups of 1000 agents, given the same amount of time, improve the compactness of their configurations on average by 89.20%. The policy also copes well with sensory noise up to a level of 50%. We also present an experimental validation using 6 physical 3D M-Block modules, demonstrating the feasibility of the stochastic control approach in practice.

© 2017 IEEE. Modern autonomous systems such as driverless vehicles need to safely operate in a wide range of conditions. A potential solution is to employ a hybrid systems approach, where safety is guaranteed in each individual mode within the system. This offsets complexity and responsibility from the individual controllers onto the complexity of determining discrete mode transitions. In this work we propose an efficient framework based on recursive neural networks and coreset data summarization to learn the transitions between an arbitrary number of controller modes that can have arbitrary complexity. Our approach allows us to efficiently gather annotation data from the large-scale datasets that are required to train such hybrid nonlinear systems to be safe under all operating conditions, favoring underexplored parts of the data. We demonstrate the construction of the embedding, and efficient detection of switching points for autonomous and non-autonomous car data. We further show how our approach enables efficient sampling of training data, to further improve either our embedding or the controllers.

© Springer International Publishing Switzerland 2016. In this work, we have developed a design environment to allow casual users to quickly and easily create custom robots. A drag-and-drop graphical interface allows users to intuitively assemble electromechanical systems from a library of predesigned parametrized components. A script-based infrastructure encapsulates and automatically composes mechanical, electrical, and software subsystems based on the user input. The generated design can be passed through output plugins to produce fabrication drawings for a range of rapid manufacturing processes, along with the necessary firmware and software to control the device. From an intuitive description of the desired specification, this system generates ready-to-use printable robots on demand., National Science Foundation (Awards EFRI-1240383 and CCF-1138967)

© Springer-Verlag Berlin Heidelberg 2014. We present an algorithm for estimating thruster configurations of underwater vehicleswith reconfigurable thrusters. The algorithm estimates each thruster’s effect on the vehicle’s attitude and position. The estimated parameters are used to maintain the robot’s attitude and position. The algorithm operates by measuring impulse response of individual thrusters and thruster combinations. Statistical metrics are used to select data samples. Finally, we compute a Moore-Penrose pseudoinverse, which is used to project the desired attitude and position changes onto the thrusters. We verify our algorithm experimentally using our robot AMOUR. The robot consists of a main body with a variable number of thrusters that can be mounted at arbitrary locations. It utilizes an IMU and a pressure sensor to continuously compute its attitude and depth. We use the algorithm to estimate different thruster configurations and show that the estimated parameters successfully control the robot. The gathering of samples together with the estimation computation takes approximately 40 seconds. Further, we show that the performance of the estimated controller matches the performance of a manually tuned controller. We also demonstrate that the estimation algorithm can adapt the controller to unexpected changes in thruster positions. The estimated controller greatly improves the stability and maneuverability of the robot when compared to the manually tuned controller.

© Springer-Verlag Berlin Heidelberg 2014. The impressive agility of living systems seems to stem from modular sensing, actuation and communication capabilities, as well as intelligence embedded in the mechanics in the form of active compliance. As a step towards bridging the gap between man-made machines and their biological counterparts, we developed a class of soft mechanisms that can undergo shape change and locomotion under pneumatic actuation. Sensing, computation, communication and actuation are embedded in the material leading to an amorphous, soft material. Soft mechanisms are harder to control than stiff mechanisms as their kinematics are difficult to model and their degrees of freedom are large. Here we show instances of such mechanisms made from identical cellular elements and demonstrate shape changing, and autonomous, sensor-based locomotion using distributed control. We show that the flexible system is accurately modeled by an equivalent spring-mass model and that shape change of each element is linear with applied pressure. We also derive a distributed feedback control law that lets a belt-shaped robot made of flexible elements locomote and climb up inclinations. These mechanisms and algorithmsmay provide a basis for creating a new generation of biomimetic soft robots that can negotiate openings and manipulate objects with an unprecedented level of compliance and robustness., United States. Defense Advanced Research Projects Agency. Defense Sciences Office. Chembots” Project (W911NF-08-C-0060)

Background Artificial intelligence (AI) and computer vision (CV) have revolutionized image analysis. In surgery, CV applications have focused on surgical phase identification in laparoscopic videos. We proposed to apply CV techniques to identify phases in an endoscopic procedure, peroral endoscopic myotomy (POEM). Methods POEM videos were collected from Massachusetts General and Showa University Koto Toyosu Hospitals. Videos were labeled by surgeons with the following ground truth phases: (1) Submucosal injection, (2) Mucosotomy, (3) Submucosal tunnel, (4) Myotomy, and (5) Mucosotomy closure. The deep-learning CV model—Convolutional Neural Network (CNN) plus Long Short-Term Memory (LSTM)—was trained on 30 videos to create POEMNet. We then used POEMNet to identify operative phases in the remaining 20 videos. The model’s performance was compared to surgeon annotated ground truth. Results POEMNet’s overall phase identification accuracy was 87.6% (95% CI 87.4–87.9%). When evaluated on a per-phase basis, the model performed well, with mean unweighted and prevalence-weighted F1 scores of 0.766 and 0.875, respectively. The model performed best with longer phases, with 70.6% accuracy for phases that had a duration under 5 min and 88.3% accuracy for longer phases. Discussion A deep-learning-based approach to CV, previously successful in laparoscopic video phase identification, translates well to endoscopic procedures. With continued refinements, AI could contribute to intra-operative decision-support systems and post-operative risk prediction.

© 2019 Wolters Kluwer Health, Inc. All rights reserved. Objective(s):To develop and assess AI algorithms to identify operative steps in laparoscopic sleeve gastrectomy (LSG).Background:Computer vision, a form of artificial intelligence (AI), allows for quantitative analysis of video by computers for identification of objects and patterns, such as in autonomous driving.Methods:Intraoperative video from LSG from an academic institution was annotated by 2 fellowship-Trained, board-certified bariatric surgeons. Videos were segmented into the following steps: 1) port placement, 2) liver retraction, 3) liver biopsy, 4) gastrocolic ligament dissection, 5) stapling of the stomach, 6) bagging specimen, and 7) final inspection of staple line. Deep neural networks were used to analyze videos. Accuracy of operative step identification by the AI was determined by comparing to surgeon annotations.Results:Eighty-eight cases of LSG were analyzed. A random 70% sample of these clips was used to train the AI and 30% to test the AI's performance. Mean concordance correlation coefficient for human annotators was 0.862, suggesting excellent agreement. Mean (±SD) accuracy of the AI in identifying operative steps in the test set was 82%±4% with a maximum of 85.6%.Conclusions:AI can extract quantitative surgical data from video with 85.6% accuracy. This suggests operative video could be used as a quantitative data source for research in intraoperative clinical decision support, risk prediction, or outcomes studies.

© 2019 IEEE. We present a trajectory planning algorithm for the shapeshifting of reconfigurable modular surface vessels. Each vessel is designed to latch with and unlatch from other vessels, which we aim to use to create dynamic infrastructure, such as on-demand bridges and temporary market squares, in canal environments. Our algorithm generates smooth and collision-free trajectories that the vessels can track to reconfigure their connections. We formulate the trajectory planning problem as Mixed Integer Quadratic Programming (MIQP) with a B-spline representation. We conceive a physical platform of the reconfigurable modular vessels and, through swimming pool experiments, show the efficacy of our trajectory planning algorithm for the shapeshifting of the vessels.

© Springer-Verlag Berlin Heidelberg 2014. This paper considers the effect of network-induced time delays on the stability of distributed controllers for groups of robots. A linear state space model is proposed for analyzing the coupled interaction of the information flow over the network with the dynamics of the robots. It is shown both analytically and experimentally that control gain, network update rate, and communication and control graph topologies are all critical factors determining the stability of the group of robots. Experiments with a group of flying quadrotor robots demonstrate the effect of different control gains for two different control graph topologies.

© 2019 IEEE. We propose a feedback control system for a reconfigurable multi-vessel platform. The platform consists of N propeller-driven vessels each of which is capable of latching to another vessel to form a rigid body of connected vessels. The main technical challenges are that i) depending on configurations of the platform the dynamic model would be different, and ii) the number of control variables in control system design increases as does the total number of vessels in the platform. To address these challenges, we develop a coordinated robust control scheme. Through experiments we assess trajectory tracking and disturbance attenuation performance of the control scheme in various configurations of the platform. Experiment results yield that average position and orientation tracking error are approximately 0.09m and 3°, and the maximum tracking error-to-disturbance ratio is 1.12.

A critical component of autonomous driving in urban environment is the vehicle’s ability to interact safely and intelligently with the human drivers and on-road pedestrians. This requires identifying the human intentions in real time based on a limited observation history and reacting accordingly. In the context of pedestrian avoidance, traditional approaches like proximity based reactive avoidance, or taking the most likely behavior of the pedestrian into account, often fail to generate a safe and successful avoidance strategy. This is mainly because they fail to take into account the human intention and the inherent uncertainty resulting in identifying such intentions from direct observations. This work formulates the on-road pedestrian avoidance problem as an instance of the Intention-Aware Motion Planning (IAMP) problem, where the human intention uncertainty is incorporated in a principled manner into the planning framework. Assuming a set of all possible pedestrian intentions in the environment, IAMPs generate a Mixed Observable Markov Decision Process (MOMDP), (a factored variant of Partially Obervable Markov Decision Process (POMDP)) with the human intentions being the unobserved variables. Solving the resulting MOMDP generates a robust pedestrian avoidance policy. In spite of the criticism of POMDPs to be computationally intractable in general, we show that with proper state factorization and latest sampling based approaches the policy can be executed online on a real vehicle on road. We demonstrate this by running the algorithm on a real pedestrian crossing in the NUS campus successfully handling the intentions for multiple pedestrians, even when they are jaywalking. In this paper, we present results in simulation to show the improved performance of the proposed approach over existing methods. Additionally, we present results validating experimentally the assumptions made in formulating the intention aware pedestrian avoidance problem. This work presents a

In this work, we present MapLite: a one-click autonomous navigation system capable of piloting a vehicle to an arbitrary desired destination point given only a sparse publicly available topometric map (from OpenStreetMap). The onboard sensors are used to segment the road region and register the topometric map in order to fuse the high-level navigation goals with a variational path planner in the vehicle frame. This enables the system to plan trajectories that correctly navigate road intersections without the use of an external localization system such as GPS or a detailed prior map. Since the topometric maps already exist for the vast majority of roads, this solution greatly increases the geographical scope for autonomous mobility solutions. We implement MapLite on a full-scale autonomous vehicle and exhaustively test it on over 15 km of road including over 100 autonomous intersection traversals. We further extend these results through simulated testing to validate the system on complex road junction topologies such as traffic circles.

In this work, we present a data-driven simulation and training engine capable of learning end-to-end autonomous vehicle control policies using only sparse rewards. By leveraging real, human-collected trajectories through an environment, we render novel training data that allows virtual agents to drive along a continuum of new local trajectories consistent with the road appearance and semantics, each with a different view of the scene. We demonstrate the ability of policies learned within our simulator to generalize to and navigate in previously unseen real-world roads, without access to any human control labels during training. Our results validate the learned policy onboard a full-scale autonomous vehicle, including in previously un-encountered scenarios, such as new roads and novel, complex, near-crash situations. Our methods are scalable, leverage reinforcement learning, and apply broadly to situations requiring effective perception and robust operation in the physical world.

Piecewise constant curvature models have proven to be an useful tool for describing kinematics and dynamics of soft robots. However, in their three dimensional formulation they suffer from many issues limiting their range of applicability - as discontinuities and singularities - mainly concerning the straight configuration of the robot. In this work we analyze these flaws, and we show that they are not due to the piecewise constant curvature assumption itself, but that instead they are a byproduct of the commonly employed direction/angle of bending parametrization of the state. We therefore consider an alternative state representation which solves all the discussed issues, and we derive a model based controller based on it. Examples in simulation are provided to support and describe the theoretical results. When using the novel parametrization, the system is able to perform more complex tasks, with a strongly reduced computational burden, and without incurring in spikes and discontinuous behaviors., National Science Foundation (U.S.) (Grant 1226883), National Science Foundation (U.S.) (Grant EFRI 1830901)

The SARS-CoV-2 pandemic is straining healthcare systems worldwide, and a global ventilator shortage is fueling the dire situation. As a response, the MIT E-Vent Team (S1) manufactured a scalable ventilator prototype for mass production and demonstrated basic clinical feasibility.

In this paper, we introduce a novel method to interpret recurrent neural networks (RNNs), particularly long short-term memory networks (LSTMs) at the cellular level. We propose a systematic pipeline for interpreting individual hidden state dynamics within the network using response characterization methods. The ranked contribution of individual cells to the network's output is computed by analyzing a set of interpretable metrics of their decoupled step and sinusoidal responses. As a result, our method is able to uniquely identify neurons with insightful dynamics, quantify relationships between dynamical properties and test accuracy through ablation analysis, and interpret the impact of network capacity on a network's dynamical distribution. Finally, we demonstrate the generalizability and scalability of our method by evaluating a series of different benchmark sequential datasets.

We present an efficient coreset construction algorithm for large-scale Support Vector Machine (SVM) training in Big Data and streaming applications. A coreset is a small, representative subset of the original data points such that a models trained on the coreset are provably competitive with those trained on the original data set. Since the size of the coreset is generally much smaller than the original set, our preprocess-then-train scheme has potential to lead to significant speedups when training SVM models. We prove lower and upper bounds on the size of the coreset required to obtain small data summaries for the SVM problem. As a corollary, we show that our algorithm can be used to extend the applicability of any off-the-shelf SVM solver to streaming, distributed, and dynamic data settings. We evaluate the performance of our algorithm on real-world and synthetic data sets. Our experimental results reaffirm the favorable theoretical properties of our algorithm and demonstrate its practical effectiveness in accelerating SVM training., National Science Foundation (U.S.) (Awards 1723943 and 1526815), United States. Office of Naval Research (Grant N00014-18-1-2830)

This paper presents a novel semantic categorization method for 3D point cloud data using supervised, multiclass Gaussian Process (GP) classification. In contrast to other approaches, and particularly Support Vector Machines, which probably are the most used method for this task to date, GPs have the major advantage of providing informative uncertainty estimates about the resulting class labels. As we show in experiments, these uncertainty estimates can either be used to improve the classification by neglecting uncertain class labels or - more importantly - they can serve as an indication of the under-representation of certain classes in the training data. This means that GP classifiers are much better suited in a lifelong learning framework, where not all classes are represented initially, but instead new training data arrives during the operation of the robot. © 2012 IEEE., ARL (Grant W911NF-08-2-0004), ONR (Grants N00014-09-1- 1051 and N00014-09-1-1031)

Many of the policies that were put into place during the Covid-19 pandemic had a common goal: to flatten the curve of the number of infected people so that its peak remains under a critical threshold. This letter considers the challenge of engineering a strategy that enforces such a goal using control theory. We introduce a simple formulation of the optimal flattening problem, and provide a closed form solution. This is augmented through nonlinear closed loop tracking of the nominal solution, with the aim of ensuring close-to-optimal performance under uncertain conditions. A key contribution of this letter is to provide validation of the method with extensive and realistic simulations in a Covid-19 scenario, with particular focus on the case of Codogno - a small city in Northern Italy that has been among the most harshly hit by the pandemic.

This paper presents a self-folding method for multi-creasestructures. The proposed method utilizes the symmetric breaking of a 3-layered two-dimensional sheet, where an inner contraction sheet induces shear force when heated, which directs the inclined folding direction. The fabrication technique developed enables distant placements of tiling patterns of the surfaces. The experimental result shows that by applying uniform heat, a feature with 62 folds can be simultaneously folded, an advantage over manual folding. The method presented is a new instant fabrication technique for making semi-rigid structures.

Objective: The aim of this review was to summarize major topics in artificial intelligence (AI), including their applications and limitations in surgery. This paper reviews the key capabilities of AI to help surgeons understand and critically evaluate new AI applications and to contribute to new developments. Summary Background Data: AI is composed of various subfields that each provide potential solutions to clinical problems. Each of the core subfields of AI reviewed in this piece has also been used in other industries such as the autonomous car, social networks, and deep learning computers. Methods: A review of AI papers across computer science, statistics, and medical sources was conducted to identify key concepts and techniques within AI that are driving innovation across industries, including surgery. Limitations and challenges of working with AI were also reviewed. Results: Four main subfields of AI were defined: (1) machine learning, (2) artificial neural networks, (3) natural language processing, and (4) computer vision. Their current and future applications to surgical practice were introduced, including big data analytics and clinical decision support systems. The implications of AI for surgeons and the role of surgeons in advancing the technology to optimize clinical effectiveness were discussed. Conclusions: Surgeons are well positioned to help integrate AI into modern practice. Surgeons should partner with data scientists to capture data across phases of care and to provide clinical context, for AI has the potential to revolutionize the way surgery is taught and practiced with the promise of a future optimized for the highest quality patient care.

We present a novel end-to-end solution for distributed multirobot coordination that translates multitouch gestures into low-level control inputs for teams of robots. Highlighting the need for a holistic solution to the problem of scalable human control of multirobot teams, we present a novel control algorithm with provable guarantees on the robots' motion that lends itself well to input from modern tablet and smartphone interfaces. Concretely, we develop an iOS application in which the user is presented with a team of robots and a bounding box (prism). The user carefully translates and scales the prism in a virtual environment; these prism coordinates are wirelessly transferred to our server and then received as input to distributed onboard robot controllers. We develop a novel distributed multirobot control policy which provides guarantees on convergence to a goal with distance bounded linearly in the number of robots, and avoids interrobot collisions. This approach allows the human user to solve the cognitive tasks such as path planning, while leaving precise motion to the robots. Our system was tested in simulation and experiments, demonstrating its utility and effectiveness., United States. Office of Naval Research. Multidisciplinary University Research Initiative. (ANTIDOTE N00014-09-1-1031), United States. Office of Naval Research. Multidisciplinary University Research Initiative. (SMARTS N00014-09-1051), Boeing Company

We consider the task of providing communication coverage to a group of sensing robots (sensors) moving independently to collect data. We provide communication via controlled placement of router vehicles that relay messages from any sensor to any other sensor in the system under the assumptions of 1) no cooperation from the sensors, and 2) only sensor-router or router-router communication over a maximum distance of R is reliable. We provide a formal framework and design provable exact and approximate (faster) algorithms for finding optimal router vehicle locations that are updated according to sensor movement. Using vehicle limitations, such as bounded control effort and maximum velocities of the sensors, our algorithm approximates areas that each router can reach while preserving connectivity and returns an expiration time window over which these positions are guaranteed to maintain communication of the entire system. The expiration time is compared against computation time required to update positions as a decision variable for choosing either the exact or approximate solution for maintaining connectivity with the sensors on-line.

Soft robots have continuum solid bodies that can deform in an infinite number of ways. Controlling soft robots is very challenging as there are no closed form solutions. We present a learning-in-the-loop co-optimization algorithm in which a latent state representation is learned as the robot figures out how to solve the task. Our solution marries hybrid particle-grid-based simulation with deep, variational convolutional autoencoder architectures that can capture salient features of robot dynamics with high efficacy. We demonstrate our dynamics-aware feature learning algorithm on both 2D and 3D soft robots, and show that it is more robust and faster converging than the dynamics-oblivious baseline. We validate the behavior of our algorithm with visualizations of the learned representation., NSF (Grant 1138967), IARPA (Grant 2019-19020100001)

Effective human supervision of robots can be key for ensuring correct robot operation in a variety of potentially safety-critical scenarios. This paper takes a step towards fast and reliable human intervention in supervisory control tasks by combining two streams of human biosignals: muscle and brain activity acquired via EMG and EEG, respectively. It presents continuous classification of left and right hand-gestures using muscle signals, time-locked classification of error-related potentials using brain signals (unconsciously produced when observing an error), and a framework that combines these pipelines to detect and correct robot mistakes during multiple-choice tasks. The resulting hybrid system is evaluated in a “plug-and-play” fashion with 7 untrained subjects supervising an autonomous robot performing a target selection task. Offline analysis further explores the EMG classification performance, and investigates methods to select subsets of training data that may facilitate generalizable plug-and-play classifiers.

When driving in urban environments, an autonomous vehicle must account for the interaction with other traffic participants. It must reason about their future behavior, how its actions affect their future behavior, and potentially consider multiple motion hypothesis. In this paper we introduce a method for joint behavior estimation and trajectory planning that models interaction and multi-policy decision-making. The method leverages Partially Observable Markov Decision Processes to estimate the behavior of other traffic participants given the planned trajectory for the ego-vehicle, and Receding-Horizon Control for generating safe trajectories for the ego-vehicle. To achieve safe navigation we introduce chance constraints over multiple motion policies in the receding-horizon planner. These constraints account for uncertainty over the behavior of other traffic participants. The method is capable of running in real-time and we show its performance and good scalability in simulated multi-vehicle intersection scenarios.

Our goal is to develop an automated digital fabrication process that can make any object out of smart materials. In this paper, we present an algorithm for creating shapes by the process of duplication, using modules we have termed smart sand. The object to be duplicated is dipped into a bag of smart sand; the particles exchange messages to sense the object's shape; and then the particles selectively form mechanical bonds with their neighbors to form a duplicate of the original. Our algorithm is capable of duplicating convex and concave 3D objects in a completely distributed manner. It uses O(1) storage space and O(n) inter-module messages per module. We perform close to 500 experiments using a realistic simulator with over 1400 modules. These experiments confirm the functionality and messaging demands of our distributed duplication algorithm while demonstrating that the algorithm can be used to form interesting and useful shapes., US Army Research Office (Grant W911NF-08-1-0228), NSF (Grants 0735953 and 1138967)

Deep learning has revolutionized the ability to learn 'end-to-end' autonomous vehicle control directly from raw sensory data. While there have been recent extensions to handle forms of navigation instruction, these works are unable to capture the full distribution of possible actions that could be taken and to reason about localization of the robot within the environment. In this paper, we extend end-to-end driving networks with the ability to perform point-to-point navigation as well as probabilistic localization using only noisy GPS data. We define a novel variational network capable of learning from raw camera data of the environment as well as higher level roadmaps to predict (1) a full probability distribution over the possible control commands; and (2) a deterministic control command capable of navigating on the route specified within the map. Additionally, we formulate how our model can be used to localize the robot according to correspondences between the map and the observed visual road topology, inspired by the rough localization that human drivers can perform. We test our algorithms on real-world driving data that the vehicle has never driven through before, and integrate our point-topoint navigation algorithms onboard a full-scale autonomous vehicle for real-time performance. Our localization algorithm is also evaluated over a new set of roads and intersections to demonstrates rough pose localization even in situations without any GPS prior.

Among traffic accidents in the USA, 23% of fatal and 32% of non-fatal incidents occurred at intersections. For driver assistance systems, intersection navigation remains a difficult problem that is critically important to increasing driver safety. In this letter, we examine how to navigate an unsignalized intersection safely under occlusions and faulty perception. We propose a real-time, probabilistic, risk assessment for parallel autonomy control applications for occluded intersection scenarios. The algorithms are implemented on real hardware and are deployed in a variety of turning and merging topologies. We show phenomena that establish go/no-go decisions, augment acceleration through an intersection and encourage nudging behaviors toward intersections., United States. Office of Naval Research (Grant N00014-18-1-2830)

Physical simulators have been widely used in robot planning and control. Among them, differentiable simulators are particularly favored, as they can be incorporated into gradient-based optimization algorithms that are efficient in solving inverse problems such as optimal control and motion planning. Therefore, rigid body simulators and recently their differentiable variants are studied extensively. Simulating deformable objects is, however, more challenging compared to rigid body dynamics. The underlying physical laws of deformable objects are more complex, and the resulting systems have orders of magnitude more degrees of freedom and there-fore they are significantly more computationally expensive to simulate. Computing gradients with respect to physical design or controller parameters is typically even more computationally challenging. In this paper, we propose a real-time, differentiable hybrid Lagrangian-Eulerian physical simulator for deformable objects, ChainQueen, based on the Moving Least Squares Material Point Method (MLS-MPM). MLS-MPM can simulate deformable objects with collisions and can be seamlessly incorporated into soft robotic systems. We demonstrate that our simulator achieves high precision in both forward simulation and backward gradient computation. We have successfully employed it in a diverse set of inference, control and co-design tasks for soft robotics.

Current methods for autonomous management use strict first-come, first-serve (FCFS) ordering to manage incoming autonomous vehicles at an intersection. In this work, we present a coordination policy that swaps agent ordering to increase the system-wide performance while ensuring that the swaps are socially compliant. By considering an agent's Social Value Orientation (SVO), a social psychology metric for their willingness to help another vehicle, the central coordinator can reduce system delays while ensuring each individual vehicle increases their own utility. The FCFS-SVO algorithm is both computationally tractable and accounts for a variety of real-world agent types, such as human drivers and a variety of social orientations. Simulation results show that average vehicle delays decrease with swapping by enabling cooperation between agents. In addition, we show that the proportion of human drivers, as well as, the distribution of prosocial and egoistic vehicles in the system can have a prominent effect on the performance of the system.