Search Results for author:
Found 91 papers, 26 papers with code
GRAM: Generalization in Deep RL with a Robust Adaptation Module
The reliable deployment of deep reinforcement learning in real-world settings requires the ability to generalize across a variety of conditions, including both in-distribution scenarios seen during training as well as novel out-of-distribution scenarios.
An Overview of the Burer-Monteiro Method for Certifiable Robot Perception
This paper presents an overview of the Burer-Monteiro method (BM), a technique that has been applied to solve robot perception problems to certifiable optimality in real-time.
Constraint-Aware Refinement for Safety Verification of Neural Feedback Loops
Refinement strategies such as partitioning or symbolic propagation are typically used to limit the conservativeness of RSOAs, but these approaches come with a high computational cost and often can only be used to verify safety for simple reachability problems.
PIETRA: Physics-Informed Evidential Learning for Traversing Out-of-Distribution Terrain
Self-supervised learning is a powerful approach for developing traversability models for off-road navigation, but these models often struggle with inputs unseen during training.
PRIMER: Perception-Aware Robust Learning-based Multiagent Trajectory Planner
In decentralized multiagent trajectory planners, agents need to communicate and exchange their positions to generate collision-free trajectories.
Out-of-Distribution Adaptation in Offline RL: Counterfactual Reasoning via Causal Normalizing Flows
In this paper, we propose the MOOD-CRL (Model-based Offline OOD-Adapting Causal RL) algorithm, which aims to address the challenge of extrapolation for offline policy training through causal inference instead of policy-regularizing methods.
CGD: Constraint-Guided Diffusion Policies for UAV Trajectory Planning
Although the resulting NN policies are effective at quickly generating trajectories similar to those from the expert, (1) their output does not explicitly account for dynamic feasibility, and (2) the policies do not accommodate changes in the constraints different from those used during training.
CLIPPER: Robust Data Association without an Initial Guess
When an informative initial estimation guess is available, the data association challenge is less acute; however, the existence of a high-quality initial guess is rare in most contexts.
SOS-Match: Segmentation for Open-Set Robust Correspondence Search and Robot Localization in Unstructured Environments
SOS-Match is a promising new approach for landmark detection and correspondence search in unstructured environments that is robust to changes in lighting and appearance and is more computationally efficient than other approaches, suggesting that the geometric arrangement of segments is a valuable localization cue in unstructured environments.
Simultaneous Localization and Mapping
Zero Shot Segmentation
Tube-NeRF: Efficient Imitation Learning of Visuomotor Policies from MPC using Tube-Guided Data Augmentation and NeRFs
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.
EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road Autonomy
For uncertainty quantification, we efficiently model both aleatoric and epistemic uncertainty by learning discrete traction distributions and probability densities of the traction predictor's latent features.
A Forward Reachability Perspective on Robust Control Invariance and Discount Factors in Reachability Analysis
This strong link we establish between the reachability problem and the barrier constraint, while guaranteeing the continuity of the value function, is not achievable by previous backward reachability-based formulations.
RAYEN: Imposition of Hard Convex Constraints on Neural Networks
This paper presents RAYEN, a framework to impose hard convex constraints on the output or latent variable of a neural network.
Principles and Guidelines for Evaluating Social Robot Navigation Algorithms
A major challenge to deploying robots widely is navigation in human-populated environments, commonly referred to as social robot navigation.
Surrogate Neural Networks for Efficient Simulation-based Trajectory Planning Optimization
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.
Efficient Deep Learning of Robust, Adaptive Policies using Tube MPC-Guided Data Augmentation
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.
DeepSeeColor: Realtime Adaptive Color Correction for Autonomous Underwater Vehicles via Deep Learning Methods
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
Using geometric landmarks like lines and planes can increase navigation accuracy and decrease map storage requirements compared to commonly-used LiDAR point cloud maps.
Game-Theoretical Perspectives on Active Equilibria: A Preferred Solution Concept over Nash Equilibria
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
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.
MIXER: Multiattribute, Multiway Fusion of Uncertain Pairwise Affinities
We present a multiway fusion algorithm capable of directly processing uncertain pairwise affinities.
A Hybrid Partitioning Strategy for Backward Reachability of Neural Feedback Loops
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
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
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
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
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
The increasing prevalence of neural networks (NNs) in safety-critical applications calls for methods to certify their behavior and guarantee safety.
Risk-Aware Off-Road Navigation via a Learned Speed Distribution Map
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).
Safe adaptation in multiagent competition
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
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
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.
Incremental Non-Gaussian Inference for SLAM Using Normalizing Flows
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
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.
Context-Specific Representation Abstraction for Deep Option Learning
Hierarchical reinforcement learning has focused on discovering temporally extended actions, such as options, that can provide benefits in problems requiring extensive exploration.
ROMAX: Certifiably Robust Deep Multiagent Reinforcement Learning via Convex Relaxation
In a multirobot system, a number of cyber-physical attacks (e. g., communication hijack, observation perturbations) can challenge the robustness of agents.
Reachability Analysis of Neural Feedback Loops
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
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.
Where to go next: Learning a Subgoal Recommendation Policy for Navigation Among Pedestrians
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.
Efficient Reachability Analysis of Closed-Loop Systems with Neural Network Controllers
Neural Networks (NNs) can provide major empirical performance improvements for robotic systems, but they also introduce challenges in formally analyzing those systems' safety properties.
CLIPPER: A Graph-Theoretic Framework for Robust Data Association
We formulate the problem in a graph-theoretic framework using the notion of geometric consistency.
Kimera-Multi: a System for Distributed Multi-Robot Metric-Semantic Simultaneous Localization and Mapping
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).
A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning
A fundamental challenge in multiagent reinforcement learning is to learn beneficial behaviors in a shared environment with other simultaneously learning agents.
Robustness Analysis of Neural Networks via Efficient Partitioning with Applications in Control Systems
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
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
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.
Certifiable Robustness to Adversarial State Uncertainty in Deep Reinforcement Learning
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.
Active Reward Learning for Co-Robotic Vision Based Exploration in Bandwidth Limited Environments
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
While solutions using onboard localization address the dependency on external infrastructure, the associated coordination strategies typically lack collision avoidance and scalability.
Robotics Multiagent Systems
Touch the Wind: Simultaneous Airflow, Drag and Interaction Sensing on a Multirotor
Disturbance estimation for Micro Aerial Vehicles (MAVs) is crucial for robustness and safety.
Scaling Up Multiagent Reinforcement Learning for Robotic Systems: Learn an Adaptive Sparse Communication Graph
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.
R-MADDPG for Partially Observable Environments and Limited Communication
There are several real-world tasks that would benefit from applying multiagent reinforcement learning (MARL) algorithms, including the coordination among self-driving cars.
Multi-agent Motion Planning for Dense and Dynamic Environments via Deep Reinforcement Learning
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.
FASTER: Fast and Safe Trajectory Planner for Navigation in Unknown Environments
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.
Incremental Learning of Motion Primitives for Pedestrian Trajectory Prediction at Intersections
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.
Certified Adversarial Robustness for Deep Reinforcement Learning
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.
Collision Avoidance in Pedestrian-Rich Environments with Deep Reinforcement Learning
Collision avoidance algorithms are essential for safe and efficient robot operation among pedestrians.
Robust Opponent Modeling via Adversarial Ensemble Reinforcement Learning in Asymmetric Imperfect-Information Games
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.
Predicting optimal value functions by interpolating reward functions in scalarized multi-objective reinforcement learning
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
+1
Planning Beyond the Sensing Horizon Using a Learned Context
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.
Policy Distillation and Value Matching in Multiagent Reinforcement Learning
Multiagent reinforcement learning algorithms (MARL) have been demonstrated on complex tasks that require the coordination of a team of multiple agents to complete.
FaSTraP: Fast and Safe Trajectory Planner for Flights in Unknown Environments
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.
Robotics
Learning Hierarchical Teaching Policies for Cooperative Agents
Collective learning can be greatly enhanced when agents effectively exchange knowledge with their peers.
Block-Coordinate Minimization for Large SDPs with Block-Diagonal Constraints
The so-called Burer-Monteiro method is a well-studied technique for solving large-scale semidefinite programs (SDPs) via low-rank factorization.
Active Perception in Adversarial Scenarios using Maximum Entropy Deep Reinforcement Learning
We pose an active perception problem where an autonomous agent actively interacts with a second agent with potentially adversarial behaviors.
CLEAR: A Consistent Lifting, Embedding, and Alignment Rectification Algorithm for Multi-View Data Association
Many robotics applications require alignment and fusion of observations obtained at multiple views to form a global model of the environment.
Safe Reinforcement Learning with Model Uncertainty Estimates
The importance of predictions that are robust to this distributional shift is evident for safety-critical applications, such as collision avoidance around pedestrians.
Real-Time Planning with Multi-Fidelity Models for Agile Flights in Unknown Environments
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.
Robotics
Efficient Constellation-Based Map-Merging for Semantic SLAM
Data association in SLAM is fundamentally challenging, and handling ambiguity well is crucial to achieve robust operation in real-world environments.
Robotics
Context-Aware Pedestrian Motion Prediction In Urban Intersections
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.
A Transferable Pedestrian Motion Prediction Model for Intersections with Different Geometries
This paper presents a novel framework for accurate pedestrian intent prediction at intersections.
Learning to Teach in Cooperative Multiagent Reinforcement Learning
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.
Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning
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.
Transferable Pedestrian Motion Prediction Models at Intersections
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.
Crossmodal Attentive Skill Learner
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.
Complexity Analysis and Efficient Measurement Selection Primitives for High-Rate Graph SLAM
To demonstrate why, we propose a quantitative metric called elimination complexity (EC) that bridges the existing analytic gap between graph structure and computation.
Robotics
Learning for Multi-robot Cooperation in Partially Observable Stochastic Environments with Macro-actions
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.
Socially Aware Motion Planning with Deep Reinforcement Learning
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).
Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability
Many real-world tasks involve multiple agents with partial observability and limited communication.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Improving PAC Exploration Using the Median Of Means
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
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
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.
Hierarchical Bayesian Noise Inference for Robust Real-time Probabilistic Object Classification
This paper presents an approach for filtering sequences of object classification probabilities using online modeling of the noise characteristics of the classifier outputs.
Efficient Global Point Cloud Alignment using Bayesian Nonparametric Mixtures
Point cloud alignment is a common problem in computer vision and robotics, with applications ranging from 3D object recognition to reconstruction.
Streaming, Distributed Variational Inference for Bayesian Nonparametrics
This paper presents a methodology for creating streaming, distributed inference algorithms for Bayesian nonparametric (BNP) models.
Stick-Breaking Policy Learning in Dec-POMDPs
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
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).
Real-Time Predictive Modeling and Robust Avoidance of Pedestrians with Uncertain, Changing Intentions
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
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.
Planning for Decentralized Control of Multiple Robots Under Uncertainty
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
We consider the sensor selection problem on multivariate Gaussian distributions where only a \emph{subset} of latent variables is of inferential interest.
Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture
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.
