Search Results for author: Jonathan P. How

Found 82 papers, 25 papers with code

Tube-NeRF: Efficient Imitation Learning of Visuomotor Policies from MPC using Tube-Guided Data Augmentation and NeRFs

no code implementations23 Nov 2023 Andrea Tagliabue, Jonathan P. How

We tailor our approach to the task of localization and trajectory tracking on a multirotor, by learning a visuomotor policy that generates control actions using images from the onboard camera as only source of horizontal position.

Data Augmentation Imitation Learning

A Forward Reachability Perspective on Robust Control Invariance and Discount Factors in Reachability Analysis

no code implementations26 Oct 2023 Jason J. Choi, Donggun Lee, Boyang Li, Jonathan P. How, Koushil Sreenath, Sylvia L. Herbert, Claire J. Tomlin

We also formulate a zero-sum differential game between the control and disturbance, where the inevitable FRT is characterized by the zero-superlevel set of the value function.

RAYEN: Imposition of Hard Convex Constraints on Neural Networks

1 code implementation17 Jul 2023 Jesus Tordesillas, Jonathan P. How, Marco Hutter

This paper presents RAYEN, a framework to impose hard convex constraints on the output or latent variable of a neural network.

Surrogate Neural Networks for Efficient Simulation-based Trajectory Planning Optimization

no code implementations30 Mar 2023 Evelyn Ruff, Rebecca Russell, Matthew Stoeckle, Piero Miotto, Jonathan P. How

This paper presents a novel methodology that uses surrogate models in the form of neural networks to reduce the computation time of simulation-based optimization of a reference trajectory.

Trajectory Planning

Efficient Deep Learning of Robust, Adaptive Policies using Tube MPC-Guided Data Augmentation

no code implementations28 Mar 2023 Tong Zhao, Andrea Tagliabue, Jonathan P. How

We tailor our approach to the task of learning an adaptive position and attitude control policy to track trajectories under challenging disturbances on a multirotor.

Data Augmentation Imitation Learning

DeepSeeColor: Realtime Adaptive Color Correction for Autonomous Underwater Vehicles via Deep Learning Methods

1 code implementation7 Mar 2023 Stewart Jamieson, Jonathan P. How, Yogesh Girdhar

In our experiments, we show that DeepSeeColor offers comparable performance to the popular "Sea-Thru" algorithm (Akkaynak & Treibitz, 2019) while being able to rapidly process images at up to 60Hz, thus making it suitable for use onboard AUVs as a preprocessing step to enable more robust vision-based behaviours.

GraffMatch: Global Matching of 3D Lines and Planes for Wide Baseline LiDAR Registration

no code implementations24 Dec 2022 Parker C. Lusk, Devarth Parikh, Jonathan P. How

Using geometric landmarks like lines and planes can increase navigation accuracy and decrease map storage requirements compared to commonly-used LiDAR point cloud maps.

Loop Closure Detection

Game-Theoretical Perspectives on Active Equilibria: A Preferred Solution Concept over Nash Equilibria

no code implementations28 Oct 2022 Dong-Ki Kim, Matthew Riemer, Miao Liu, Jakob N. Foerster, Gerald Tesauro, Jonathan P. How

By directly comparing active equilibria to Nash equilibria in these examples, we find that active equilibria find more effective solutions than Nash equilibria, concluding that an active equilibrium is the desired solution for multiagent learning settings.

Output Feedback Tube MPC-Guided Data Augmentation for Robust, Efficient Sensorimotor Policy Learning

no code implementations18 Oct 2022 Andrea Tagliabue, Jonathan P. How

Thanks to the augmented data, we reduce the computation time and the number of demonstrations needed by IL, while providing robustness to sensing and process uncertainty.

Data Augmentation Imitation Learning

MIXER: Multiattribute, Multiway Fusion of Uncertain Pairwise Affinities

no code implementations15 Oct 2022 Parker C. Lusk, Kaveh Fathian, Jonathan P. How

We present a multiway fusion algorithm capable of directly processing uncertain pairwise affinities.


A Hybrid Partitioning Strategy for Backward Reachability of Neural Feedback Loops

no code implementations14 Oct 2022 Nicholas Rober, Michael Everett, Songan Zhang, Jonathan P. How

We introduce a hybrid partitioning method that uses both target set partitioning (TSP) and backreachable set partitioning (BRSP) to overcome a lower bound on estimation error that is present when using BRSP.

Backward Reachability Analysis of Neural Feedback Loops: Techniques for Linear and Nonlinear Systems

no code implementations28 Sep 2022 Nicholas Rober, Sydney M. Katz, Chelsea Sidrane, Esen Yel, Michael Everett, Mykel J. Kochenderfer, Jonathan P. How

As neural networks (NNs) become more prevalent in safety-critical applications such as control of vehicles, there is a growing need to certify that systems with NN components are safe.

View-Invariant Localization using Semantic Objects in Changing Environments

no code implementations28 Sep 2022 Jacqueline Ankenbauer, Kaveh Fathian, Jonathan P. How

To demonstrate our framework, we consider an example of localizing a ground vehicle in a reference object map using only cars as objects.

Wide-Area Geolocalization with a Limited Field of View Camera

no code implementations23 Sep 2022 Lena M. Downes, Ted J. Steiner, Rebecca L. Russell, Jonathan P. How

This paper presents Restricted FOV Wide-Area Geolocalization (ReWAG), a cross-view geolocalization approach that generalizes WAG for use with standard, non-panoramic ground cameras by creating pose-aware embeddings and providing a strategy to incorporate particle pose into the Siamese network.

Robust, High-Rate Trajectory Tracking on Insect-Scale Soft-Actuated Aerial Robots with Deep-Learned Tube MPC

no code implementations20 Sep 2022 Andrea Tagliabue, Yi-Hsuan Hsiao, Urban Fasel, J. Nathan Kutz, Steven L. Brunton, Yufeng Chen, Jonathan P. How

Accurate and agile trajectory tracking in sub-gram Micro Aerial Vehicles (MAVs) is challenging, as the small scale of the robot induces large model uncertainties, demanding robust feedback controllers, while the fast dynamics and computational constraints prevent the deployment of computationally expensive strategies.

Backward Reachability Analysis for Neural Feedback Loops

1 code implementation14 Apr 2022 Nicholas Rober, Michael Everett, Jonathan P. How

The increasing prevalence of neural networks (NNs) in safety-critical applications calls for methods to certify their behavior and guarantee safety.

Collision Avoidance

Risk-Aware Off-Road Navigation via a Learned Speed Distribution Map

1 code implementation25 Mar 2022 Xiaoyi Cai, Michael Everett, Jonathan Fink, Jonathan P. How

Motion planning in off-road environments requires reasoning about both the geometry and semantics of the scene (e. g., a robot may be able to drive through soft bushes but not a fallen log).

Motion Planning Unity

Safe adaptation in multiagent competition

no code implementations14 Mar 2022 Macheng Shen, Jonathan P. How

Achieving the capability of adapting to ever-changing environments is a critical step towards building fully autonomous robots that operate safely in complicated scenarios.

City-wide Street-to-Satellite Image Geolocalization of a Mobile Ground Agent

no code implementations10 Mar 2022 Lena M. Downes, Dong-Ki Kim, Ted J. Steiner, Jonathan P. How

Taken together, WAG's network training and particle filter weighting approach achieves city-scale position estimation accuracies on the order of 20 meters, a 98% reduction compared to a baseline training and weighting approach.

Influencing Long-Term Behavior in Multiagent Reinforcement Learning

1 code implementation7 Mar 2022 Dong-Ki Kim, Matthew Riemer, Miao Liu, Jakob N. Foerster, Michael Everett, Chuangchuang Sun, Gerald Tesauro, Jonathan P. How

An effective approach that has recently emerged for addressing this non-stationarity is for each agent to anticipate the learning of other agents and influence the evolution of future policies towards desirable behavior for its own benefit.

reinforcement-learning Reinforcement Learning (RL)

Incremental Non-Gaussian Inference for SLAM Using Normalizing Flows

1 code implementation2 Oct 2021 Qiangqiang Huang, Can Pu, Kasra Khosoussi, David M. Rosen, Dehann Fourie, Jonathan P. How, John J. Leonard

This paper presents normalizing flows for incremental smoothing and mapping (NF-iSAM), a novel algorithm for inferring the full posterior distribution in SLAM problems with nonlinear measurement models and non-Gaussian factors.

Demonstration-Efficient Guided Policy Search via Imitation of Robust Tube MPC

no code implementations21 Sep 2021 Andrea Tagliabue, Dong-Ki Kim, Michael Everett, Jonathan P. How

Our approach opens the possibility of zero-shot transfer from a single demonstration collected in a nominal domain, such as a simulation or a robot in a lab/controlled environment, to a domain with bounded model errors/perturbations.

Data Augmentation Imitation Learning

Context-Specific Representation Abstraction for Deep Option Learning

1 code implementation20 Sep 2021 Marwa Abdulhai, Dong-Ki Kim, Matthew Riemer, Miao Liu, Gerald Tesauro, Jonathan P. How

Hierarchical reinforcement learning has focused on discovering temporally extended actions, such as options, that can provide benefits in problems requiring extensive exploration.

Hierarchical Reinforcement Learning

ROMAX: Certifiably Robust Deep Multiagent Reinforcement Learning via Convex Relaxation

no code implementations14 Sep 2021 Chuangchuang Sun, Dong-Ki Kim, Jonathan P. How

In a multirobot system, a number of cyber-physical attacks (e. g., communication hijack, observation perturbations) can challenge the robustness of agents.

reinforcement-learning Reinforcement Learning (RL)

Reachability Analysis of Neural Feedback Loops

1 code implementation9 Aug 2021 Michael Everett, Golnaz Habibi, Chuangchuang Sun, Jonathan P. How

While the solutions are less tight than previous (semidefinite program-based) methods, they are substantially faster to compute, and some of those computational time savings can be used to refine the bounds through new input set partitioning techniques, which is shown to dramatically reduce the tightness gap.

Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems

1 code implementation28 Jun 2021 Yulun Tian, Yun Chang, Fernando Herrera Arias, Carlos Nieto-Granda, Jonathan P. How, Luca Carlone

This paper presents Kimera-Multi, the first multi-robot system that (i) is robust and capable of identifying and rejecting incorrect inter and intra-robot loop closures resulting from perceptual aliasing, (ii) is fully distributed and only relies on local (peer-to-peer) communication to achieve distributed localization and mapping, and (iii) builds a globally consistent metric-semantic 3D mesh model of the environment in real-time, where faces of the mesh are annotated with semantic labels.

3D Reconstruction Benchmarking +1

Where to go next: Learning a Subgoal Recommendation Policy for Navigation Among Pedestrians

no code implementations25 Feb 2021 Bruno Brito, Michael Everett, Jonathan P. How, Javier Alonso-Mora

Robotic navigation in environments shared with other robots or humans remains challenging because the intentions of the surrounding agents are not directly observable and the environment conditions are continuously changing.

Collision Avoidance Reinforcement Learning (RL)

Efficient Reachability Analysis of Closed-Loop Systems with Neural Network Controllers

1 code implementation5 Jan 2021 Michael Everett, Golnaz Habibi, Jonathan P. How

Neural Networks (NNs) can provide major empirical performance improvements for robotic systems, but they also introduce challenges in formally analyzing those systems' safety properties.

Kimera-Multi: a System for Distributed Multi-Robot Metric-Semantic Simultaneous Localization and Mapping

no code implementations8 Nov 2020 Yun Chang, Yulun Tian, Jonathan P. How, Luca Carlone

Our system, dubbed Kimera-Multi, is implemented by a team of robots equipped with visual-inertial sensors, and builds a 3D mesh model of the environment in real-time, where each face of the mesh is annotated with a semantic label (e. g., building, road, objects).

Simultaneous Localization and Mapping

A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning

1 code implementation31 Oct 2020 Dong-Ki Kim, Miao Liu, Matthew Riemer, Chuangchuang Sun, Marwa Abdulhai, Golnaz Habibi, Sebastian Lopez-Cot, Gerald Tesauro, Jonathan P. How

A fundamental challenge in multiagent reinforcement learning is to learn beneficial behaviors in a shared environment with other simultaneously learning agents.

reinforcement-learning Reinforcement Learning (RL)

Robustness Analysis of Neural Networks via Efficient Partitioning with Applications in Control Systems

no code implementations1 Oct 2020 Michael Everett, Golnaz Habibi, Jonathan P. How

Recent works approximate the propagation of sets through nonlinear activations or partition the uncertainty set to provide a guaranteed outer bound on the set of possible NN outputs.

Lunar Terrain Relative Navigation Using a Convolutional Neural Network for Visual Crater Detection

no code implementations15 Jul 2020 Lena M. Downes, Ted J. Steiner, Jonathan P. How

Terrain relative navigation can improve the precision of a spacecraft's position estimate by detecting global features that act as supplementary measurements to correct for drift in the inertial navigation system.

FISAR: Forward Invariant Safe Reinforcement Learning with a Deep Neural Network-Based Optimize

no code implementations19 Jun 2020 Chuangchuang Sun, Dong-Ki Kim, Jonathan P. How

To drive the constraint violation monotonically decrease, we take the constraints as Lyapunov functions and impose new linear constraints on the policy parameters' updating dynamics.

reinforcement-learning Reinforcement Learning (RL) +1

Certifiable Robustness to Adversarial State Uncertainty in Deep Reinforcement Learning

no code implementations11 Apr 2020 Michael Everett, Bjorn Lutjens, Jonathan P. How

Deep Neural Network-based systems are now the state-of-the-art in many robotics tasks, but their application in safety-critical domains remains dangerous without formal guarantees on network robustness.

Adversarial Robustness Collision Avoidance +2

Active Reward Learning for Co-Robotic Vision Based Exploration in Bandwidth Limited Environments

no code implementations10 Mar 2020 Stewart Jamieson, Jonathan P. How, Yogesh Girdhar

We present a novel POMDP problem formulation for a robot that must autonomously decide where to go to collect new and scientifically relevant images given a limited ability to communicate with its human operator.

A Distributed Pipeline for Scalable, Deconflicted Formation Flying

1 code implementation4 Mar 2020 Parker C. Lusk, Xiaoyi Cai, Samir Wadhwania, Aleix Paris, Kaveh Fathian, Jonathan P. How

While solutions using onboard localization address the dependency on external infrastructure, the associated coordination strategies typically lack collision avoidance and scalability.

Robotics Multiagent Systems

Scaling Up Multiagent Reinforcement Learning for Robotic Systems: Learn an Adaptive Sparse Communication Graph

no code implementations2 Mar 2020 Chuangchuang Sun, Macheng Shen, Jonathan P. How

Through this sparsity structure, the agents can communicate in an effective as well as efficient way via only selectively attending to agents that matter the most and thus the scale of the MARL problem is reduced with little optimality compromised.

Reinforcement Learning (RL)

R-MADDPG for Partially Observable Environments and Limited Communication

1 code implementation16 Feb 2020 Rose E. Wang, Michael Everett, Jonathan P. How

There are several real-world tasks that would benefit from applying multiagent reinforcement learning (MARL) algorithms, including the coordination among self-driving cars.

reinforcement-learning Reinforcement Learning (RL) +1

Multi-agent Motion Planning for Dense and Dynamic Environments via Deep Reinforcement Learning

no code implementations18 Jan 2020 Samaneh Hosseini Semnani, Hugh Liu, Michael Everett, Anton de Ruiter, Jonathan P. How

This paper introduces a hybrid algorithm of deep reinforcement learning (RL) and Force-based motion planning (FMP) to solve distributed motion planning problem in dense and dynamic environments.

Motion Planning Reinforcement Learning (RL)

FASTER: Fast and Safe Trajectory Planner for Navigation in Unknown Environments

2 code implementations9 Jan 2020 Jesus Tordesillas, Brett T. Lopez, Michael Everett, Jonathan P. How

The standard approaches that ensure safety by enforcing a "stop" condition in the free-known space can severely limit the speed of the vehicle, especially in situations where much of the world is unknown.

Motion Planning Trajectory Planning

Incremental Learning of Motion Primitives for Pedestrian Trajectory Prediction at Intersections

no code implementations21 Nov 2019 Golnaz Habibi, Nikita Japuria, Jonathan P. How

This paper presents a novel incremental learning algorithm for pedestrian motion prediction, with the ability to improve the learned model over time when data is incrementally available.

Gaussian Processes Incremental Learning +3

Certified Adversarial Robustness for Deep Reinforcement Learning

no code implementations28 Oct 2019 Björn Lütjens, Michael Everett, Jonathan P. How

Deep Neural Network-based systems are now the state-of-the-art in many robotics tasks, but their application in safety-critical domains remains dangerous without formal guarantees on network robustness.

Adversarial Robustness Collision Avoidance +2

Robust Opponent Modeling via Adversarial Ensemble Reinforcement Learning in Asymmetric Imperfect-Information Games

no code implementations18 Sep 2019 Macheng Shen, Jonathan P. How

In order to achieve a good trade-off between the robustness of the learned policy and the computation complexity, we propose to train a separate opponent policy against the protagonist agent for evaluation purposes.

reinforcement-learning Reinforcement Learning (RL) +1

Predicting optimal value functions by interpolating reward functions in scalarized multi-objective reinforcement learning

1 code implementation11 Sep 2019 Arpan Kusari, Jonathan P. How

A Gaussian process is used to obtain a smooth interpolation over the reward function weights of the optimal value function for three well-known examples: GridWorld, Objectworld and Pendulum.

Autonomous Vehicles Multi-Objective Reinforcement Learning

Planning Beyond the Sensing Horizon Using a Learned Context

1 code implementation24 Aug 2019 Michael Everett, Justin Miller, Jonathan P. How

Context is key information about structured environments that could guide exploration toward the unknown goal location, but the abstract idea is difficult to quantify for use in a planning algorithm.

Image-to-Image Translation Test

Policy Distillation and Value Matching in Multiagent Reinforcement Learning

no code implementations15 Mar 2019 Samir Wadhwania, Dong-Ki Kim, Shayegan Omidshafiei, Jonathan P. How

Multiagent reinforcement learning algorithms (MARL) have been demonstrated on complex tasks that require the coordination of a team of multiple agents to complete.

reinforcement-learning Reinforcement Learning (RL)

FaSTraP: Fast and Safe Trajectory Planner for Flights in Unknown Environments

3 code implementations8 Mar 2019 Jesus Tordesillas, Brett T. Lopez, Jonathan P. How

The desire of maintaining computational tractability typically leads to optimization problems that do not include the obstacles (collision checks are done on the solutions) or to formulations that use a convex decomposition of the free space and then impose an ad hoc allocation of each interval of the trajectory in a specific polyhedron.


Block-Coordinate Minimization for Large SDPs with Block-Diagonal Constraints

no code implementations2 Mar 2019 Yulun Tian, Kasra Khosoussi, Jonathan P. How

The so-called Burer-Monteiro method is a well-studied technique for solving large-scale semidefinite programs (SDPs) via low-rank factorization.

Riemannian optimization

Active Perception in Adversarial Scenarios using Maximum Entropy Deep Reinforcement Learning

no code implementations14 Feb 2019 Macheng Shen, Jonathan P. How

We pose an active perception problem where an autonomous agent actively interacts with a second agent with potentially adversarial behaviors.

reinforcement-learning Reinforcement Learning (RL)

CLEAR: A Consistent Lifting, Embedding, and Alignment Rectification Algorithm for Multi-View Data Association

1 code implementation6 Feb 2019 Kaveh Fathian, Kasra Khosoussi, Yulun Tian, Parker Lusk, Jonathan P. How

Many robotics applications require alignment and fusion of observations obtained at multiple views to form a global model of the environment.

Clustering Graph Clustering +1

Safe Reinforcement Learning with Model Uncertainty Estimates

no code implementations19 Oct 2018 Björn Lütjens, Michael Everett, Jonathan P. How

The importance of predictions that are robust to this distributional shift is evident for safety-critical applications, such as collision avoidance around pedestrians.

Collision Avoidance reinforcement-learning +3

Real-Time Planning with Multi-Fidelity Models for Agile Flights in Unknown Environments

2 code implementations2 Oct 2018 Jesus Tordesillas, Brett T. Lopez, John Carter, John Ware, Jonathan P. How

However, in unknown environments, this approach can lead to erratic or unstable behavior due to the interaction between the global planner, whose solution is changing constantly, and the local planner; a consequence of not capturing higher-order dynamics in the global plan.


Efficient Constellation-Based Map-Merging for Semantic SLAM

no code implementations25 Sep 2018 Kristoffer M. Frey, Ted J. Steiner, Jonathan P. How

Data association in SLAM is fundamentally challenging, and handling ambiguity well is crucial to achieve robust operation in real-world environments.


Context-Aware Pedestrian Motion Prediction In Urban Intersections

no code implementations25 Jun 2018 Golnaz Habibi, Nikita Jaipuria, Jonathan P. How

This paper presents a novel context-based approach for pedestrian motion prediction in crowded, urban intersections, with the additional flexibility of prediction in similar, but new, environments.

Clustering motion prediction

Learning to Teach in Cooperative Multiagent Reinforcement Learning

no code implementations20 May 2018 Shayegan Omidshafiei, Dong-Ki Kim, Miao Liu, Gerald Tesauro, Matthew Riemer, Christopher Amato, Murray Campbell, Jonathan P. How

The problem of teaching to improve agent learning has been investigated by prior works, but these approaches make assumptions that prevent application of teaching to general multiagent problems, or require domain expertise for problems they can apply to.

reinforcement-learning Reinforcement Learning (RL)

Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning

6 code implementations4 May 2018 Michael Everett, Yu Fan Chen, Jonathan P. How

This work extends our previous approach to develop an algorithm that learns collision avoidance among a variety of types of dynamic agents without assuming they follow any particular behavior rules.

Collision Avoidance Decision Making +4

Transferable Pedestrian Motion Prediction Models at Intersections

no code implementations15 Mar 2018 Macheng Shen, Golnaz Habibi, Jonathan P. How

We are interested in developing transfer learning algorithms that can be trained on the pedestrian trajectories collected at one intersection and yet still provide accurate predictions of the trajectories at another, previously unseen intersection.

feature selection motion prediction +2

Crossmodal Attentive Skill Learner

1 code implementation28 Nov 2017 Shayegan Omidshafiei, Dong-Ki Kim, Jason Pazis, Jonathan P. How

This paper presents the Crossmodal Attentive Skill Learner (CASL), integrated with the recently-introduced Asynchronous Advantage Option-Critic (A2OC) architecture [Harb et al., 2017] to enable hierarchical reinforcement learning across multiple sensory inputs.

Atari Games Hierarchical Reinforcement Learning +2

Complexity Analysis and Efficient Measurement Selection Primitives for High-Rate Graph SLAM

no code implementations20 Sep 2017 Kristoffer M. Frey, Ted J. Steiner, Jonathan P. How

To demonstrate why, we propose a quantitative metric called elimination complexity (EC) that bridges the existing analytic gap between graph structure and computation.


Learning for Multi-robot Cooperation in Partially Observable Stochastic Environments with Macro-actions

no code implementations24 Jul 2017 Miao Liu, Kavinayan Sivakumar, Shayegan Omidshafiei, Christopher Amato, Jonathan P. How

We implement two variants of multi-robot Search and Rescue (SAR) domains (with and without obstacles) on hardware to demonstrate the learned policies can effectively control a team of distributed robots to cooperate in a partially observable stochastic environment.

Decision Making Decision Making Under Uncertainty

Socially Aware Motion Planning with Deep Reinforcement Learning

2 code implementations26 Mar 2017 Yu Fan Chen, Michael Everett, Miao Liu, Jonathan P. How

For robotic vehicles to navigate safely and efficiently in pedestrian-rich environments, it is important to model subtle human behaviors and navigation rules (e. g., passing on the right).

Autonomous Navigation Motion Planning +3

Improving PAC Exploration Using the Median Of Means

no code implementations NeurIPS 2016 Jason Pazis, Ronald E. Parr, Jonathan P. How

We present the first application of the median of means in a PAC exploration algorithm for MDPs.

Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learning

no code implementations26 Sep 2016 Yu Fan Chen, Miao Liu, Michael Everett, Jonathan P. How

Finding feasible, collision-free paths for multiagent systems can be challenging, particularly in non-communicating scenarios where each agent's intent (e. g. goal) is unobservable to the others.

Multiagent Systems

Small-Variance Nonparametric Clustering on the Hypersphere

no code implementations CVPR 2015 Julian Straub, Trevor Campbell, Jonathan P. How, John W. Fisher III

Based on the small-variance limit of Bayesian nonparametric von-Mises-Fisher (vMF) mixture distributions, we propose two new flexible and efficient k-means-like clustering algorithms for directional data such as surface normals.

Clustering Nonparametric Clustering +1

Hierarchical Bayesian Noise Inference for Robust Real-time Probabilistic Object Classification

no code implementations3 May 2016 Shayegan Omidshafiei, Brett T. Lopez, Jonathan P. How, John Vian

This paper presents an approach for filtering sequences of object classification probabilities using online modeling of the noise characteristics of the classifier outputs.

Classification Decision Making +3

Efficient Global Point Cloud Alignment using Bayesian Nonparametric Mixtures

no code implementations CVPR 2017 Julian Straub, Trevor Campbell, Jonathan P. How, John W. Fisher III

Point cloud alignment is a common problem in computer vision and robotics, with applications ranging from 3D object recognition to reconstruction.

3D Object Recognition

Stick-Breaking Policy Learning in Dec-POMDPs

no code implementations1 May 2015 Miao Liu, Christopher Amato, Xuejun Liao, Lawrence Carin, Jonathan P. How

Expectation maximization (EM) has recently been shown to be an efficient algorithm for learning finite-state controllers (FSCs) in large decentralized POMDPs (Dec-POMDPs).

Decentralized Control of Partially Observable Markov Decision Processes using Belief Space Macro-actions

no code implementations20 Feb 2015 Shayegan Omidshafiei, Ali-akbar Agha-mohammadi, Christopher Amato, Jonathan P. How

To allow for a high-level representation that is natural for multi-robot problems and scalable to large discrete and continuous problems, this paper extends the Dec-POMDP model to the decentralized partially observable semi-Markov decision process (Dec-POSMDP).

Decision Making

Real-Time Predictive Modeling and Robust Avoidance of Pedestrians with Uncertain, Changing Intentions

no code implementations22 May 2014 Sarah Ferguson, Brandon Luders, Robert C. Grande, Jonathan P. How

To plan safe trajectories in urban environments, autonomous vehicles must be able to quickly assess the future intentions of dynamic agents.

Robotics 68T40

Approximate Decentralized Bayesian Inference

no code implementations28 Mar 2014 Trevor Campbell, Jonathan P. How

The method first employs variational inference on each individual learning agent to generate a local approximate posterior, the agents transmit their local posteriors to other agents in the network, and finally each agent combines its set of received local posteriors.

Bayesian Inference Test +1

Planning for Decentralized Control of Multiple Robots Under Uncertainty

no code implementations12 Feb 2014 Christopher Amato, George D. Konidaris, Gabriel Cruz, Christopher A. Maynor, Jonathan P. How, Leslie P. Kaelbling

We describe a probabilistic framework for synthesizing control policies for general multi-robot systems, given environment and sensor models and a cost function.

Sensor Selection in High-Dimensional Gaussian Trees with Nuisances

no code implementations NeurIPS 2013 Daniel S. Levine, Jonathan P. How

We consider the sensor selection problem on multivariate Gaussian distributions where only a \emph{subset} of latent variables is of inferential interest.

valid Vocal Bursts Intensity Prediction

Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture

1 code implementation NeurIPS 2013 Trevor Campbell, Miao Liu, Brian Kulis, Jonathan P. How, Lawrence Carin

This paper presents a novel algorithm, based upon the dependent Dirichlet process mixture model (DDPMM), for clustering batch-sequential data containing an unknown number of evolving clusters.

Clustering Test

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