Jonathan P. How
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Aerobatic maneuvers in insect-scale flapping-wing aerial robots via deep-learned robust tube model predictive control Open
Aerial insects exhibit agile maneuvers such as sharp braking, saccades, and body flips under disturbances; in contrast, insect-scale aerial robots are limited to tracking smooth trajectories with small acceleration. To achieve similar flig…
Efficient Probabilistic Planning with Maximum-Coverage Distributionally Robust Backward Reachable Trees Open
This paper presents a new multi-query motion planning algorithm for linear Gaussian systems with the goal of reaching a Euclidean ball with high probability. We develop a new formulation for ball-shaped ambiguity sets of Gaussian distribut…
Efficient Navigation in Unknown Indoor Environments with Vision-Language Models Open
We present a novel high-level planning framework that leverages vision-language models (VLMs) to improve autonomous navigation in unknown indoor environments with many dead ends. Traditional exploration methods often take inefficient route…
Distribution Estimation for Global Data Association via Approximate Bayesian Inference Open
Global data association is an essential prerequisite for robot operation in environments seen at different times or by different robots. Repetitive or symmetric data creates significant challenges for existing methods, which typically rely…
Orion: A Micro Satellite Testbed for Formation Flying Open
A revolution in spacecraft guidance, navigation and control technology has started with GPS to autonomously provide spacecraft position, attitude and time information. This new technology is being applied to spacecraft constellations to ac…
Aerobatic maneuvers in insect-scale flapping-wing aerial robots via deep-learned robust tube model predictive control Open
Aerial insects exhibit highly agile maneuvers such as sharp braking, saccades, and body flips under disturbance. In contrast, insect-scale aerial robots are limited to tracking non-aggressive trajectories with small body acceleration. This…
View article: AI Challenge for Satellite Pattern-of-Life Identification: Dataset, Design and Results
AI Challenge for Satellite Pattern-of-Life Identification: Dataset, Design and Results Open
Despite the availability of extensive historical data on Earth-orbiting objects, artificial intelligence (AI) adoption in space domain awareness remains limited. To address this gap, the 2024 MIT ARCLab Prize for AI Innovation in Space cha…
VISTA: Monocular Segmentation-Based Mapping for Appearance and View-Invariant Global Localization Open
Global localization is critical for autonomous navigation, particularly in scenarios where an agent must localize within a map generated in a different session or by another agent, as agents often have no prior knowledge about the correlat…
View article: Language-Grounded Hierarchical Planning and Execution with Multi-Robot 3D Scene Graphs
Language-Grounded Hierarchical Planning and Execution with Multi-Robot 3D Scene Graphs Open
In this paper, we introduce a multi-robot system that integrates mapping, localization, and task and motion planning (TAMP) enabled by 3D scene graphs to execute complex instructions expressed in natural language. Our system builds a share…
Terrain-aware Low Altitude Path Planning Open
In this paper, we study the problem of generating low-altitude path plans for nap-of-the-earth (NOE) flight in real time with only RGB images from onboard cameras and the vehicle pose. We propose a novel training method that combines behav…
ABAMGuid+: An Enhanced Aerocapture Guidance Framework using Augmented Bank Angle Modulation Open
Aerocapture consists of converting a hyperbolic approach trajectory into a captured target orbit utilizing the aerodynamic forces generated via a single pass through the atmosphere. Aerocapture guidance systems must be robust to significan…
DYNUS: Uncertainty-aware Trajectory Planner in Dynamic Unknown Environments Open
This paper introduces DYNUS, an uncertainty-aware trajectory planner designed for dynamic unknown environments. Operating in such settings presents many challenges -- most notably, because the agent cannot predict the ground-truth future p…
Aerocapture Guidance for Augmented Bank Angle Modulation Open
This paper presents an optimal control solution for an aerocapture vehicle with two control inputs, bank angle and angle of attack, referred to as augmented bank angle modulation (ABAM). We derive the optimal control profiles using Pontrya…
View article: Principles and Guidelines for Evaluating Social Robot Navigation Algorithms
Principles and Guidelines for Evaluating Social Robot Navigation Algorithms Open
A major challenge to deploying robots widely is navigation in human-populated environments, commonly referred to as social robot navigation . While the field of social navigation has advanced tremendously in recent years, the fair evaluati…
GRAM: Generalization in Deep RL with a Robust Adaptation Module Open
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-distribu…
REVISE: Robust Probabilistic Motion Planning in a Gaussian Random Field Open
This paper presents Robust samplE-based coVarIance StEering (REVISE), a multi-query algorithm that generates robust belief roadmaps for dynamic systems navigating through spatially dependent disturbances modeled as a Gaussian random field.…
ROMAN: Open-Set Object Map Alignment for Robust View-Invariant Global Localization Open
Global localization is a fundamental capability required for long-term and drift-free robot navigation. However, current methods fail to relocalize when faced with significantly different viewpoints. We present ROMAN (Robust Object Map Ali…
Safe Autonomy for Uncrewed Surface Vehicles Using Adaptive Control and Reachability Analysis Open
Marine robots must maintain precise control and ensure safety during tasks like ocean monitoring, even when encountering unpredictable disturbances that affect performance. Designing algorithms for uncrewed surface vehicles (USVs) requires…
An Overview of the Burer-Monteiro Method for Certifiable Robot Perception Open
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. BM is often used to solve semidefinite programming relaxations,…
Constraint-Aware Refinement for Safety Verification of Neural Feedback Loops Open
Neural networks (NNs) are becoming increasingly popular in the design of control pipelines for autonomous systems. However, since the performance of NNs can degrade in the presence of out-of-distribution data or adversarial attacks, system…
PIETRA: Physics-Informed Evidential Learning for Traversing Out-of-Distribution Terrain Open
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. Existing methods utilize techniques like evidential deep lear…
SDP Synthesis of Distributionally Robust Backward Reachable Trees for Probabilistic Planning Open
The paper presents Maximal Ellipsoid Backward Reachable Trees MAXELLIPSOID BRT, which is a multi-query algorithm for planning of dynamic systems under stochastic motion uncertainty and constraints on the control input. In contrast to exist…
MURP: Multi-Agent Ultra-Wideband Relative Pose Estimation With Constrained Communications in 3D Environments Open
Inter-agent relative localization is critical for many multi-robot systems operating in the absence of external positioning infrastructure or prior environmental knowledge. We propose a novel inter-agent relative 3D pose estimation system …
PRIMER: Perception-Aware Robust Learning-based Multiagent Trajectory Planner Open
In decentralized multiagent trajectory planners, agents need to communicate and exchange their positions to generate collision-free trajectories. However, due to localization errors/uncertainties, trajectory deconfliction can fail even if …
TCAFF: Temporal Consistency for Robot Frame Alignment Open
In the field of collaborative robotics, the ability to communicate spatial information like planned trajectories and shared environment information is crucial. When no global position information is available (e.g., indoor or GPS-denied en…
Out-of-Distribution Adaptation in Offline RL: Counterfactual Reasoning via Causal Normalizing Flows Open
Despite notable successes of Reinforcement Learning (RL), the prevalent use of an online learning paradigm prevents its widespread adoption, especially in hazardous or costly scenarios. Offline RL has emerged as an alternative solution, le…
CGD: Constraint-Guided Diffusion Policies for UAV Trajectory Planning Open
Traditional optimization-based planners, while effective, suffer from high computational costs, resulting in slow trajectory generation. A successful strategy to reduce computation time involves using Imitation Learning (IL) to develop fas…
Tube-NeRF: Efficient Imitation Learning of Visuomotor Policies From MPC via Tube-Guided Data Augmentation and NeRFs Open
Imitation learning (IL) can train computationally-efficient sensorimotor policies from a resource-intensive model predictive controller (MPC), but it often requires many samples, leading to long training times or limited robustness. To add…