Author name cluster

Nikolaus Correll

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

25 papers

2 author rows

IROS Conference 2023 Conference Paper

Optimal Decision Making in Robotic Assembly and Other Trial-and-Error Tasks

James Watson
Nikolaus Correll

Uncertainty in perception, actuation, and the environment often require multiple attempts for a robotic task to be successful. We study a class of problems providing (1) low-entropy indicators of terminal success / failure, and (2) unreliable (high-entropy) data to predict the final outcome of an ongoing task. Examples include a robot trying to connect with a charging station, parallel parking, or assembling a tightly-fitting part. The ability to restart after predicting a failure early, versus simply running to failure, can significantly decrease the makespan, that is, the total time to completion, with the drawback of potentially short-cutting an otherwise successful operation. Assuming task running times to be Poisson distributed, and using a Markov Jump Process to capture the dynamics of the underlying Markov Decision Process, we derive a closed-form solution that predicts makespan based on the confusion matrix of the failure predictor. This allows the robot to learn failure prediction in a production environment, and only adopt a preemptive policy when it actually saves time. We demonstrate this approach using a robotic peg-in-hole assembly problem. Failures are predicted by a dilated convolutional network based on force-torque data, showing an average makespan reduction from 101s to 81s ( $\mathrm{N}=120, \ \mathrm{p} < 0. 05$ ). We posit that the proposed algorithm generalizes to any robotic behavior with an unambiguous terminal reward, with wide ranging applications on how robots can learn and improve their behaviors in the wild.

IROS Conference 2018 Conference Paper

System Identification and Closed-Loop Control of a Hydraulically Amplified Self-Healing Electrostatic (HASEL) Actuator

Cosima Schunk
Levi Pearson
Eric Acome
Timothy G. Morrissey
Nikolaus Correll
Christoph Keplinger
Mark E. Rentschler
J. Sean Humbert

This paper describes a system identification method and the development of a closed-loop controller for a Hydraulically Amplified Self-healing Electrostatic (HASEL) actuator. Our efforts focus on developing a reliable and consistent way to identify system models for these soft robotic actuators using high-speed videography based motion tracking. Utilizing a mass-spring-damper model we are able to accurately capture the behavior of a HASEL actuator. We use the resulting plant model to design a Proportional-Integral controller that demonstrates improved closed-loop tracking and steady-state error performance.

ICRA Conference 2017 Conference Paper

Functionalized textiles for interactive soft robotics

Nicholas Farrow
Lauren McIntire
Nikolaus Correll

We use a conductive fabric substrate as a building material for a soft sensor to extend the functionality of soft actuators. We use PCB etching techniques to apply a pattern to the fabric, yielding distinct conductive surfaces within the same textile. We connect these via flexible wire bus embedded in silicone, terminating in a flexible PCB. We show how touch and metal objects can be localized along the length of the composite fabric strip. We demonstrate an example soft robotic application, by replacing the constraint layer component in a PneuFlex-style soft actuator with the self contained sensing strip. We show that the augmented composite actuator is able to interact with conductive objects in the environment using a capacitive touch sensing with applications in grasping and human-robot interaction.

ICRA Conference 2017 Conference Paper

Recognizing social touch gestures using recurrent and convolutional neural networks

Dana Hughes 0001
Alon Krauthammer
Nikolaus Correll

Deep learning approaches have been used to perform classification in several applications with high-dimensional input data. In this paper, we investigate the potential for deep learning for classifying affective touch on robotic skin in a social setting. Three models are considered, a convolutional neural network, a convolutional-recurrent neural network and an autoencoder-recurrent neural network. These models are evaluated on two publicly available affective touch datasets, and compared with models built to classify the same datasets. The deep learning approaches provide a similar level of accuracy, and allows gestures to be predicted in real-time at a rate of 6 to 9 Hertz. The memory requirements of the models demonstrate that they can be implemented on small, inexpensive microcontrollers, demonstrating that classification can be performed in the skin itself by collocating computing elements with the sensor array.

IROS Conference 2015 Conference Paper

A soft pneumatic actuator that can sense grasp and touch

Nicholas Farrow
Nikolaus Correll

We present a fiber reinforced soft pneumatic actuator with integrated strain and pressure sensors. We demonstrate that combining these sensors into the same actuator enables proprioception of both actuator curvature and environmental contact forces. We describe the manufacture and integration of a simple liquid metal strain sensor, a pressure sensor, and electrical circuits used for the sensors. We derive a constant curvature model for the actuator which predicts actuator curvature from the air pressure, and other constants of manufacture. The utility of the sensor integration is demonstrated by using the actuator to distinguish successful grasps amongst a set of common cylindrical objects with varying diameter. The grasp radius is estimated from the relationship between the sensor pair. Contact forces with the environment (touch) may be inferred from sensor readings which deviate from unconstrained motion.

TAAS Journal 2015 Journal Article

Distributed Spatiotemporal Gesture Recognition in Sensor Arrays

Homa Hosseinmardi
Akshay Mysore
Nicholas Farrow
Nikolaus Correll
Richard Han

We present algorithms for gesture recognition using in-network processing in distributed sensor arrays embedded within systems such as tactile input devices, sensing skins for robotic applications, and smart walls. We describe three distributed gesture-recognition algorithms that are designed to function on sensor arrays with minimal computational power, limited memory, limited bandwidth, and possibly unreliable communication. These constraints cause storage of gesture templates within the system and distributed consensus algorithms for recognizing gestures to be difficult. Building up on a chain vector encoding algorithm commonly used for gesture recognition on a central computer, we approach this problem by dividing the gesture dataset between nodes such that each node has access to the complete dataset via its neighbors. Nodes share gesture information among each other, then each node tries to identify the gesture. In order to distribute the computational load among all nodes, we also investigate an alternative algorithm, in which each node that detects a motion will apply a recognition algorithm to part of the input gesture, then share its data with all other motion nodes. Next, we show that a hybrid algorithm that distributes both computation and template storage can address trade-offs between memory and computational efficiency.

ICRA Conference 2015 Conference Paper

Experience-based planning with sparse roadmap spanners

Dave Coleman
Ioan Alexandru Sucan
Mark Moll
Kei Okada
Nikolaus Correll

We present an experience-based planning framework called Thunder that learns to reduce computation time required to solve high-dimensional planning problems in varying environments. The approach is especially suited for large configuration spaces that include many invariant constraints, such as those found with whole body humanoid motion planning. Experiences are generated using probabilistic sampling and stored in a sparse roadmap spanner (SPARS), which provides asymptotically near-optimal coverage of the configuration space, making storing, retrieving, and repairing past experiences very efficient with respect to memory and time. The Thunder framework improves upon past experience-based planners by storing experiences in a graph rather than in individual paths, eliminating redundant information, providing more opportunities for path reuse, and providing a theoretical limit to the size of the experience graph. These properties also lead to improved handling of dynamically changing environments, reasoning about optimal paths, and reducing query resolution time. The approach is demonstrated on a 30 degrees of freedom humanoid robot and compared with the Lightning framework, an experience-based planner that uses individual paths to store past experiences. In environments with variable obstacles and stability constraints, experiments show that Thunder is on average an order of magnitude faster than Lightning and planning from scratch. Thunder also uses 98. 8% less memory to store its experiences after 10, 000 trials when compared to Lightning. Our framework is implemented and freely available in the Open Motion Planning Library.

ICRA Conference 2015 Conference Paper

Simultaneous localization, mapping, and manipulation for unsupervised object discovery

Lu Ma
Mahsa Ghafarianzadeh
Dave Coleman
Nikolaus Correll
Gabe Sibley

We present an unsupervised framework for simultaneous appearance-based object discovery, detection, tracking and reconstruction using RGBD cameras and a robot manipulator. The system performs dense 3D simultaneous localization and mapping concurrently with unsupervised object discovery. Putative objects that are spatially and visually coherent are manipulated by the robot to gain additional motion-cues. The robot uses appearance alone, followed by structure and motion cues, to jointly discover, verify, learn and improve models of objects. Induced motion segmentation reinforces learned models which are represented implicitly as 2D and 3D level sets to capture both shape and appearance. We compare three different approaches for appearance-based object discovery and find that a novel form of spatio-temporal super-pixels gives the highest quality candidate object models in terms of precision and recall. Live experiments with a Baxter robot demonstrate a holistic pipeline capable of automatic discovery, verification, detection, tracking and reconstruction of unknown objects.

ICRA Conference 2014 Conference Paper

A soft, amorphous skin that can sense and localize textures

Dana Hughes 0001
Nikolaus Correll

We present a soft, amorphous skin that can sense and localize textures. The skin consists of a series of sensing and computing elements that are networked with their local neighbors and mimic the function of the Pacinian corpuscle in human skin. Each sensor node samples a vibration signal at 1KHz, transforms the signal into the frequency domain, and classifies up to 15 textures using logistic regression. By measuring the power spectrum of the signal and comparing it with its local neighbors, computing elements can then collaboratively estimate the location of the stimulus. The resulting low-bandwidth information, consisting of the texture probability distribution and its location are then routed to a sink anywhere in the skin in a multi-hop fashion. We describe the design, manufacturing, classification, localization and networking algorithms and experimentally validate the proposed approach. In particular, we demonstrate texture classification with 71% accuracy and centimeter accuracy in localization over an area of approximately three square feet using ten networked sensor nodes.

IROS Conference 2014 Conference Paper

A stick-slip omnidirectional powertrain for low-cost swarm robotics: Mechanism, calibration, and control

John Klingner
Anshul Kanakia
Nicholas Farrow
Dustin Reishus
Nikolaus Correll

We present an omnidirectional powertrain for swarm robotic platforms that relies on low-cost vibration motors. We describe a mechanism and controller to achieve full 3-DoF motion on the plane. The proposed approach does not require the motors to be in phase, and overcomes differences in manufacturing by a hardware-in-the-loop auto-calibration routine based on the Nelder-Mead algorithm, which issues motion commands via infrared and records the resulting trajectories using an off-the-shelf webcam. We show convergence results of the calibration routine and sample trajectories of the swarm robotic platform “Droplet” demonstrating turning and omnidirectional drive.

ICAPS Conference 2014 Conference Paper

C-FOREST: Parallel Shortest-Path Planning with Super Linear Speedup

Michael W. Otte
Nikolaus Correll

In (Otte and Correll 2013) we present C-FOREST, a parallelization framework for single-query sampling-based shortest-path planning algorithms. C-FOREST has been observed to have super linear speedup on many problems, e. g. , paths of quality Ltarget are found 350X faster by 64 CPUs working in parallel than by 1 CPU. In (Otte and Correll 2013) C-FOREST is tested in conjunction with the RRT* algorithm. In the current work we perform additional experiments that show C-FOREST provides similar advantages when used conjunction with the SPRT algorithm. This reinforces our original claim that C-FOREST is generally applicable to a wide range of sampling based motion planning algorithms.

ICRA Conference 2014 Conference Paper

Miniature six-channel range and bearing system: Algorithm, analysis and experimental validation

Nicholas Farrow
John Klingner
Dustin Reishus
Nikolaus Correll

We present an algorithm, analysis, and implementation of a six-channel range and bearing system for swarm robot systems with sizes in the order of centimeters. The proposed approach relies on a custom sensor and receiver model, and collection of intensity signals from all possible sensor/emitter pairs. This allows us to improve range calculation by accounting for orientation-dependent variations in the transmitted intensity, as well as to determine the orientation of the emitting robot. We show how the algorithm and analysis generalize to other range and bearing systems, and evaluate its performance experimentally using two ping-pong ball-sized “Droplets” mounted on a precise gantry system.

IROS Conference 2014 Conference Paper

Optimal parameter identification for discrete mechanical systems with application to flexible object manipulation

Timothy M. Caldwell
Dave Coleman
Nikolaus Correll

We present a method for system identification of flexible objects by measuring forces and displacement during interaction with a manipulating arm. We model the object's structure and flexibility by a chain of rigid bodies connected by torsional springs. Unlike previous work, the proposed optimal control approach using variational integrators allows identification of closed loops, which include the robot arm itself. This allows using the resulting models for planning in configuration space of the robot. In order to solve the resulting problem efficiently, we develop a novel method for fast discrete-time adjoint-based gradient calculation. The feasibility of the approach is demonstrated using full physics simulation in trep and using data recorded from a 7-DOF series elastic robot arm.

IROS Conference 2013 Conference Paper

Navigation with foraging

Michael W. Otte
Nikolaus Correll
Emilio Frazzoli

We propose and study the navigation with foraging problem, where an agent with a limited sensor range must simultaneously: (1) navigate to a global goal and (2) forage en route as opportunities to forage are detected. Each foraging act causes a deviation from the shortest path to the long-term goal, with consequences for path length, mission duration, and fuel usage. We analytically calculate and/or bound the expected distance the robot actually travels, given the initial distance to the the global goal. In particular, for either of two non-trivial greedy strategies: (A) forage the point that minimizes goal-heading deviation. (B) forage the closest point ahead of the robot. Our results generalize to problems in higher dimensions.

ICRA Conference 2011 Conference Paper

Decentralized self-repair to maintain connectivity and coverage in networked multi-robot systems

Anna Derbakova
Nikolaus Correll
Daniela Rus

We present a suite of algorithms that enable a team of mobile robots to repair connectivity in a wireless mesh network. Each robot carries a wireless router and can act as a mobile access point. The algorithms are distributed, with each robot computing it's trajectory using its position, the positions of its neighbors within communication range, and the position of a gateway node. The algorithms are validated via an analytical model as well as field experiments with 7 Create robots.

IROS Conference 2011 Conference Paper

Self-assembly of modular robots from finite number of modules using graph grammars

Vijeth Rai
Anne C. van Rossum
Nikolaus Correll

We wish to design decentralized algorithms for self-assembly of robotic modules that have 100% yield even if the number of available building blocks is limited, and specifically when the number of available building blocks is identical to the number of blocks required by the structure. In contrast to self-assembly at the nano and micro scales where abundant building blocks are available, modular robotic systems need to self-assemble from a limited number of modules. In particular, when self-assembly is used for reconfiguration, it is desirable that the new conformation includes all of the available modules. We propose a suite of algorithms that (1) generate a reversible graph grammar, i. e. , generates rules for a desired structure that allow the structure not only to assemble, but also to disassemble, and (2) have a set of structures that are growing in parallel converge to a single structure using broadcast communication. We show that by omitting a reversal rule for the last attached module, self-assembly eventually completes, and that communication can drastically speed up this process.

IROS Conference 2010 Conference Paper

Object Interaction Language (OIL): An intent-based language for programming self-organized sensor/actuator networks

Daniel J. Sutton
Peter T. Klein
Michael W. Otte
Nikolaus Correll

This paper introduces the Object Interaction Language (OIL) that allows programming and coordination of distributed, heterogeneous sensor-actuator networks, such as sensor networks and multi-robot systems. OIL is an interpreted, object oriented language and is contained in an OIL environment. An OIL environment provides communication between agents and allows agents to exchange code snippets among each other. Possible implementations of OIL environments can be — in the simplest case — a sheet of paper with OIL code literally printed on it, or a computational agent endowed with sensors, actuators and wireless communication. The atomic primitive in OIL is the intent for which implementation is resolved during runtime, potentially using code from other OIL environments and leading to distributed execution. We develop the structure of the language and demonstrate its key properties using a distributed computation task that is parallelized via an OIL environment. We evaluate the algorithm empirically by running OIL code on a team of six computational agents that communicate wirelessly. We then show experimentally how OIL can be used to allocate sensing and mobility in a multi-robot system using a case study in navigation, where one robot dynamically provides laser range data to another robot which is blind to its environment.

ICRA Conference 2009 Conference Paper

Ad-hoc wireless network coverage with networked robots that cannot localize

Nikolaus Correll
Jonathan Bachrach
Daniel Vickery
Daniela Rus

We study a fully distributed, reactive algorithm for deployment and maintenance of a mobile communication backbone that provides an area around a network gateway with wireless network access for higher-level agents. Possible applications of such a network are distributed sensor networks as well as communication support for disaster or military operations. The algorithm has minimalist requirements on the individual robotic node and does not require any localization. This makes the proposed solution suitable for deployment of large numbers of comparably cheap mobile communication nodes and as a backup solution for more capable systems in GPS-denied environments. Robots keep exploring the configuration space by random walk and stop only if their current location satisfies user-specified constraints on connectivity (number of neighbors). Resulting deployments are robust and convergence is analyzed using both kinematic simulation with a simplified collision and communication model as well as a probabilistic macroscopic model. The approach is validated on a team of 9 iRobot Create robots carrying wireless access points in an indoor environment.

IROS Conference 2009 Conference Paper

Building a distributed robot garden

Nikolaus Correll
Nikos Aréchiga
Adrienne Bolger
Mario Bollini
Benjamin Charrow
Adam Clayton
Felipe Dominguez
Kenneth Donahue

This paper describes the architecture and implementation of a distributed autonomous gardening system. The garden is a mesh network of robots and plants. The gardening robots are mobile manipulators with an eye-in-hand camera. They are capable of locating plants in the garden, watering them, and locating and grasping fruit. The plants are potted cherry tomatoes enhanced with sensors and computation to monitor their well-being (e. g. soil humidity, state of fruits) and with networking to communicate servicing requests to the robots. Task allocation, sensing and manipulation are distributed in the system and de-centrally coordinated. We describe the architecture of this system and present experimental results for navigation, object recognition and manipulation.

ICRA Conference 2008 Conference Paper

Parameter estimation and optimal control of swarm-robotic systems: A case study in distributed task allocation

Nikolaus Correll

This paper presents a methodology for finding optimal control parameters as well as optimal system parameters for robot swarm controllers using probabilistic, population dynamic models. With distributed task allocation as a case study, we show how optimal control parameters leading to a desired steady-state task distribution for two fully-distributed algorithms can be found even if the parameters of the system are unknown. First, a reactive algorithm in which robots change states independently from each other and which leads to a linear macroscopic model describing the dynamics of the system is considered. Second, a threshold-based algorithm where robots change states based on the number of other robots in this state and which leads to a non-linear model is investigated. Whereas analytical results can be obtained for the linear system, the optimization of the non-linear controller is performed numerically. Finally, we show using stochastic simulations that whereas the presented methodology and models work best if the swarm size is large, useful results can already be obtained for team-sizes below a hundred robots. The methodology presented can be applied to scenarios involving the control of large numbers of entities with limited computational and communication abilities as well as a tight energy budget, such as swarms of robots from the centimeter to nanometer range or sensor networks.

IROS Conference 2008 Conference Paper

SwisTrack - a flexible open source tracking software for multi-agent systems

Thomas Lochmatter
Pierre Roduit
Christopher M. Cianci
Nikolaus Correll
Jacques Jacot
Alcherio Martinoli

Vision-based tracking is used in nearly all robotic laboratories for monitoring and extracting of agent positions, orientations, and trajectories. However, there is currently no accepted standard software solution available, so many research groups resort to developing and using their own custom software. In this paper, we present Version 4 of SwisTrack, an open source project for simultaneous tracking of multiple agents. While its broad range of pre-implemented algorithmic components allows it to be used in a variety of experimental applications, its novelty stands in its highly modular architecture. Advanced users can therefore also implement additional customized modules which extend the functionality of the existing components within the provided interface. This paper introduces SwisTrack and shows experiments with both marked and marker-less agents.

ICRA Conference 2007 Conference Paper

Robust Distributed Coverage using a Swarm of Miniature Robots

Nikolaus Correll
Alcherio Martinoli

For the multi-robot coverage problem deterministic deliberative as well as probabilistic approaches have been proposed. Whereas deterministic approaches usually provide provable completeness and promise good performance under perfect conditions, probabilistic approaches are more robust to sensor and actuator noise, but completion cannot be guaranteed and performance is sub-optimal in terms of time to completion. In reality, however, almost all deterministic algorithms for robot coordination can be considered probabilistic when considering the unpredictability of real world factors. This paper investigates experimentally and analytically how probabilistic and deterministic algorithms can be combined for maintaining the robustness of probabilistic approaches, and explicitly model the reliability of a robotic platform. Using realistic simulation and data from real robot experiments, we study system performance of a swarm-robotic inspection system at different levels of noise (wheel-slip). The prediction error of a purely deterministic model increases when the assumption of perfect sensors and actuators is violated, whereas a combination of probabilistic and deterministic models provides a better match with experimental data.

IROS Conference 2006 Conference Paper

SwisTrack: A Tracking Tool for Multi-Unit Robotic and Biological Systems

Nikolaus Correll
Grégory Sempo
Yuri López de Meneses
José Halloy
Jean-Louis Deneubourg
Alcherio Martinoli

Tracking of miniature robotic platforms involves major challenges in image recognition and data association. We present our 2. 5 years effort into developing a platform-independent, easy to use, and robust tracking software SwisTrack, which is tailored to research in swarm robotics and behavioral biology. We demonstrate the software and algorithm's abilities using two case studies, tracking of a swarm of cockroaches, and a swarm-robotic inspection task, while outlining hard problems in tracking and data-association of marker-less objects. Its open, platform-independent architecture, and easy-to-use interfaces (Matlab, Java, and C++), allowing for (distributed) post-processing of trajectory data online, make the software highly adaptive to particular research projects without changes to the source code. SwisTrack will be publicly available shortly under the OSI Adaptive License via SourceForge.net.

IROS Conference 2006 Conference Paper

SwisTrack: A Tracking Tool for Multi-Unit Robotic and Biological Systems

Nikolaus Correll
Grégory Sempo
Yuri López de Meneses
José Halloy
Jean-Louis Deneubourg
Alcherio Martinoli

Tracking of miniature robotic platforms involves major challenges in image recognition and data association. We present our 3-year effort into developing the platform-independent, easy-to-use, and robust tracking software SwisTrack, which is tailored to research in swarm robotics and behavioral biology. We demonstrate the software and algorithms abilities using two case studies, tracking of a swarm of cockroaches, and a swarm-robotic inspection task, while outlining hard problems in tracking and data-association of marker-less objects. Tracking accuracy of a moving robot with respect to camera noise and the calibration model are calculated experimentally. Its open, platform-independent architecture, and easy-to-use interfaces (Matlabtrade, Javatrade, and C++), allowing for (distributed) post-processing of trajectory data online, make the software highly adaptive to particular research projects without changes to the source code. SwisTrack is publicly available on Sourceforge.net under the OSI Adaptive License and contributions from the robotics and biology community are encouraged

ICRA Conference 2005 Conference Paper

Modeling and Analysis of Beaconless and Beacon-Based Policies for a Swarm-Intelligent Inspection System

Nikolaus Correll
Alcherio Martinoli

We are developing a swarm-intelligent inspection system based on a swarm of autonomous, miniature robots, using only on-board, local sensors. To estimate intrinsic advantages and limitations of the proposed possible distributed control solution, we capture the dynamic of the system at a higher abstraction level using non-spatial probabilistic microscopic and macroscopic models. In a previous publication, we showed that we are able to predict quantitatively the performances of the swarm of robots for a given metric and a beaconless policy. In this paper, after briefly reviewing our modeling methodology, we explore the effect of adding an additional state to the individual robot controller, which allow robots to serve as a beacon for teammates and therefore bias their inspection routes. Results show that this additional complexity helps the swarm of robots to be more efficient in terms of energy consumption but not necessarily in terms of time required to complete the inspection. We also demonstrate that a beacon-based policy introduces a strong coupling among the behavior of robots, coupling which in turn results in nonlinearities at the macroscopic model level.