Publications (220)
A Closed-Form CLF-CBF Controller for Whole-Body Continuum Soft Robot Collision Avoidance
Kiwan Wong, Maximillian Stölzle, Wei Xiao +1
Safe operation is essential for deploying robots in human-centered 3D environments. Soft continuum manipulators provide passive safety through mechanical compliance, but still requ…
Interpreting Neural Policies with Disentangled Tree Representations
Tsun-Hsuan Wang, Wei Xiao, Tim Seyde +2
The advancement of robots, particularly those functioning in complex human-centric environments, relies on control solutions that are driven by machine learning. Understanding how…
Online Multi-Target Tracking for Maneuvering Vehicles in Dynamic Road Context
Zehui Meng, Qi Heng Ho, Zefan Huang +3
Target detection and tracking provides crucial information for motion planning and decision making in autonomous driving. This paper proposes an online multi-object tracking (MOT)…
BarrierSteer: LLM Safety via Learning Barrier Steering
Thanh Q. Tran, Arun Verma, Kiwan Wong +3
Despite the strong performance of large language models (LLMs) across diverse tasks, their susceptibility to adversarial attacks and unsafe content generation remains a significant…
Robust Place Recognition using an Imaging Lidar
Tixiao Shan, Brendan Englot, Fabio Duarte +2
We propose a methodology for robust, real-time place recognition using an imaging lidar, which yields image-quality high-resolution 3D point clouds. Utilizing the intensity reading…
Design and Control of Modular Soft-Rigid Hybrid Manipulators with Self-Contact
Zach J. Patterson, Emily Sologuren, Cosimo Della Santina +1
Soft robotics focuses on designing robots with highly deformable materials, allowing them to adapt and operate safely and reliably in unstructured and variable environments. While…
Control Barrier Functions for Systems with Multiple Control Inputs
Wei Xiao, Christos G. Cassandras, Calin A. Belta +1
Control Barrier Functions (CBFs) are becoming popular tools in guaranteeing safety for nonlinear systems and constraints, and they can reduce a constrained optimal control problem…
Deep Learning on Home Drone: Searching for the Optimal Architecture
Alaa Maalouf, Yotam Gurfinkel, Barak Diker +3
We suggest the first system that runs real-time semantic segmentation via deep learning on a weak micro-computer such as the Raspberry Pi Zero v2 (whose price was $15) attached to…
Liquid Time-constant Networks
Ramin Hasani, Mathias Lechner, Alexander Amini +2
We introduce a new class of time-continuous recurrent neural network models. Instead of declaring a learning system's dynamics by implicit nonlinearities, we construct networks of…
TransCenter: Transformers with Dense Representations for Multiple-Object Tracking
Yihong Xu, Yutong Ban, Guillaume Delorme +3
Transformers have proven superior performance for a wide variety of tasks since they were introduced. In recent years, they have drawn attention from the vision community in tasks…
Entangled Residual Mappings
Mathias Lechner, Ramin Hasani, Zahra Babaiee +4
Residual mappings have been shown to perform representation learning in the first layers and iterative feature refinement in higher layers. This interplay, combined with their stab…
SoRoMoX: Fast, Differentiable, and Parallelizable Soft Robot Models
Maximilian Stölzle, Solange Gribonval, Daniel Feliu-Talegon +9
Reduced-order models based on Cosserat-rod theory are now well established, and modeling theory is no longer the primary bottleneck in soft-robot control. Their implementations, ho…
The Master Key Filters Hypothesis: Deep Filters Are General
Zahra Babaiee, Peyman M. Kiasari, Daniela Rus +1
This paper challenges the prevailing view that convolutional neural network (CNN) filters become increasingly specialized in deeper layers. Motivated by recent observations of clus…
Oscillatory State-Space Models
T. Konstantin Rusch, Daniela Rus
We propose Linear Oscillatory State-Space models (LinOSS) for efficiently learning on long sequences. Inspired by cortical dynamics of biological neural networks, we base our propo…
Rebalancing the Rebalancers: Optimally Routing Vehicles and Drivers in Mobility-on-Demand Systems
Stephen L. Smith, Marco Pavone, Mac Schwager +2
In this paper we study rebalancing strategies for a mobility-on-demand urban transportation system blending customer-driven vehicles with a taxi service. In our system, a customer…
Intention Communication and Hypothesis Likelihood in Game-Theoretic Motion Planning
Makram Chahine, Roya Firoozi, Wei Xiao +2
Game-theoretic motion planners are a potent solution for controlling systems of multiple highly interactive robots. Most existing game-theoretic planners unrealistically assume a p…
Optimal Multi-Robot Path Planning with Temporal Logic Constraints
Alphan Ulusoy, Stephen L. Smith, Xu Chu Ding +2
In this paper we present a method for automatically planning optimal paths for a group of robots that satisfy a common high level mission specification. Each robot's motion in the…
LTL Control in Uncertain Environments with Probabilistic Satisfaction Guarantees
Xu Chu Ding, Stephen L. Smith, Calin Belta +1
We present a method to generate a robot control strategy that maximizes the probability to accomplish a task. The task is given as a Linear Temporal Logic (LTL) formula over a set…
Design of Trimmed Helicoid Soft-Rigid Hybrid Robots
Zach J. Patterson, Emily R. Sologuren, Daniela Rus
As soft robot design matures, researchers have converged to sophisticated design paradigms to enable the development of more suitable platforms. Two such paradigms are soft-rigid h…
Baxter's Homunculus: Virtual Reality Spaces for Teleoperation in Manufacturing
Jeffrey I Lipton, Aidan J Fay, Daniela Rus
Expensive specialized systems have hampered development of telerobotic systems for manufacturing systems. In this paper we demonstrate a telerobotic system which can reduce the cos…
Sparse Flows: Pruning Continuous-depth Models
Lucas Liebenwein, Ramin Hasani, Alexander Amini +1
Continuous deep learning architectures enable learning of flexible probabilistic models for predictive modeling as neural ordinary differential equations (ODEs), and for generative…
Compress to Impress: Efficient LLM Adaptation Using a Single Gradient Step on 100 Samples
Shiva Sreeram, Alaa Maalouf, Pratyusha Sharma +1
Recently, Sharma et al. suggested a method called Layer-SElective-Rank reduction (LASER) which demonstrated that pruning high-order components of carefully chosen LLM's weight matr…
Printed helicoids with embedded air channels make sensorized segments for soft continuum robots
Annan Zhang, Hanna Matusik, Miguel Flores-Acton +3
Soft robots enable safe, adaptive interaction with complex environments but remain difficult to sense and control due to their highly deformable structures. Architected soft materi…
Concept Graph Neural Networks for Surgical Video Understanding
Yutong Ban, Jennifer A. Eckhoff, Thomas M. Ward +4
We constantly integrate our knowledge and understanding of the world to enhance our interpretation of what we see. This ability is crucial in application domains which entail reaso…
Coresets for Vector Summarization with Applications to Network Graphs
Dan Feldman, Sedat Ozer, Daniela Rus
We provide a deterministic data summarization algorithm that approximates the mean of a set of vectors in $\REAL^d$, by a weighted mean…
Incremental Sampling-based Algorithm for Minimum-violation Motion Planning
Luis I. Reyes Castro, Pratik Chaudhari, Jana Tumova +3
This paper studies the problem of control strategy synthesis for dynamical systems with differential constraints to fulfill a given reachability goal while satisfying a set of safe…
LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery
Pingchuan Ma, Tsun-Hsuan Wang, Minghao Guo +5
Large Language Models have recently gained significant attention in scientific discovery for their extensive knowledge and advanced reasoning capabilities. However, they encounter…
Advancing AI Challenges for the United States Department of the Air Force
Christian Prothmann, Vijay Gadepally, Jeremy Kepner +35
The DAF-MIT AI Accelerator is a collaboration between the United States Department of the Air Force (DAF) and the Massachusetts Institute of Technology (MIT). This program pioneers…
LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping
Tixiao Shan, Brendan Englot, Carlo Ratti +1
We propose a framework for tightly-coupled lidar-visual-inertial odometry via smoothing and mapping, LVI-SAM, that achieves real-time state estimation and map-building with high ac…
The Curious Case of In-Training Compression of State Space Models
Makram Chahine, Philipp Nazari, Daniela Rus +1
State Space Models (SSMs), developed to tackle long sequence modeling tasks efficiently, offer both parallelizable training and fast inference. At their core are recurrent dynamica…
Decentralized Vision-Based Autonomous Aerial Wildlife Monitoring
Makram Chahine, William Yang, Alaa Maalouf +6
Wildlife field operations demand efficient parallel deployment methods to identify and interact with specific individuals, enabling simultaneous collective behavioral analysis, and…
Exploring Latent Pathways: Enhancing the Interpretability of Autonomous Driving with a Variational Autoencoder
Anass Bairouk, Mirjana Maras, Simon Herlin +5
Autonomous driving presents a complex challenge, which is usually addressed with artificial intelligence models that are end-to-end or modular in nature. Within the landscape of mo…
Deep Context Maps: Agent Trajectory Prediction using Location-specific Latent Maps
Igor Gilitschenski, Guy Rosman, Arjun Gupta +2
In this paper, we propose a novel approach for agent motion prediction in cluttered environments. One of the main challenges in predicting agent motion is accounting for location a…
PyHopper -- Hyperparameter optimization
Mathias Lechner, Ramin Hasani, Philipp Neubauer +2
Hyperparameter tuning is a fundamental aspect of machine learning research. Setting up the infrastructure for systematic optimization of hyperparameters can take a significant amou…
Road Pricing for Spreading Peak Travel: Modeling and Design
Tichakorn Wongpiromsarn, Nan Xiao, Keyou You +4
A case study of the Singapore road network provides empirical evidence that road pricing can significantly affect commuter trip timing behaviors. In this paper, we propose a model…
Learning Stability Attention in Vision-based End-to-end Driving Policies
Tsun-Hsuan Wang, Wei Xiao, Makram Chahine +3
Modern end-to-end learning systems can learn to explicitly infer control from perception. However, it is difficult to guarantee stability and robustness for these systems since the…
Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition
Lucas Liebenwein, Alaa Maalouf, Oren Gal +2
We present a novel global compression framework for deep neural networks that automatically analyzes each layer to identify the optimal per-layer compression ratio, while simultane…
On Coresets for Support Vector Machines
Murad Tukan, Cenk Baykal, Dan Feldman +1
We present an efficient coreset construction algorithm for large-scale Support Vector Machine (SVM) training in Big Data and streaming applications. A coreset is a small, represent…
Parallelization of Non-linear State-Space Models: Scaling Up Liquid-Resistance Liquid-Capacitance Networks for Efficient Sequence Modeling
Mónika Farsang, Ramin Hasani, Daniela Rus +1
We present LrcSSM, a recurrent model that processes long sequences as fast as today's linear state-space layers. By forcing its Jacobian matrix to be diagonal…
Deep Evidential Regression
Alexander Amini, Wilko Schwarting, Ava Soleimany +1
Deterministic neural networks (NNs) are increasingly being deployed in safety critical domains, where calibrated, robust, and efficient measures of uncertainty are crucial. In this…
Functional Co-Optimization of Articulated Robots
Andrew Spielberg, Brandon Araki, Cynthia Sung +2
We present parametric trajectory optimization, a method for simultaneously computing physical parameters, actuation requirements, and robot motions for more efficient robot designs…
Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds
Cenk Baykal, Lucas Liebenwein, Igor Gilitschenski +2
We present an efficient coresets-based neural network compression algorithm that sparsifies the parameters of a trained fully-connected neural network in a manner that provably app…
Biconvex Optimization for Smooth Minimum-Time Trajectories around Convex Obstacles
Peter Werner, Tobia Marcucci, Daniela Rus
We present a biconvex approach for minimum-time motion planning around convex obstacles that is guaranteed to converge, is anytime, and supports derivative constraints to arbitrary…
Are All Vision Models Created Equal? A Study of the Open-Loop to Closed-Loop Causality Gap
Mathias Lechner, Ramin Hasani, Alexander Amini +3
There is an ever-growing zoo of modern neural network models that can efficiently learn end-to-end control from visual observations. These advanced deep models, ranging from convol…
A GPS Pseudorange Based Cooperative Vehicular Distance Measurement Technique
Daiqin Yang, Fang Zhao, Kai Liu +3
Accurate vehicular localization is important for various cooperative vehicle safety (CVS) applications such as collision avoidance, turning assistant, etc. In this paper, we propos…
The Quest for Universal Master Key Filters in DS-CNNs
Zahra Babaiee, Peyman M. Kiassari, Daniela Rus +1
A recent study has proposed the "Master Key Filters Hypothesis" for convolutional neural network filters. This paper extends this hypothesis by radically constraining its scope to…
Safe Motion Planning and Control Using Predictive and Adaptive Barrier Methods for Autonomous Surface Vessels
Alejandro Gonzalez-Garcia, Wei Xiao, Wei Wang +5
Safe motion planning is essential for autonomous vessel operations, especially in challenging spaces such as narrow inland waterways. However, conventional motion planning approach…
On the Forward Invariance of Neural ODEs
Wei Xiao, Tsun-Hsuan Wang, Ramin Hasani +4
We propose a new method to ensure neural ordinary differential equations (ODEs) satisfy output specifications by using invariance set propagation. Our approach uses a class of cont…
Learning to Dissipate Energy in Oscillatory State-Space Models
Jared Boyer, T. Konstantin Rusch, Daniela Rus
State-space models (SSMs) are a class of networks for sequence learning that benefit from fixed state size and linear complexity with respect to sequence length, contrasting the qu…
Capsa: A Unified Framework for Quantifying Risk in Deep Neural Networks
Sadhana Lolla, Iaroslav Elistratov, Alejandro Perez +3
The modern pervasiveness of large-scale deep neural networks (NNs) is driven by their extraordinary performance on complex problems but is also plagued by their sudden, unexpected,…
ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics
Yuanming Hu, Jiancheng Liu, Andrew Spielberg +5
Physical simulators have been widely used in robot planning and control. Among them, differentiable simulators are particularly favored, as they can be incorporated into gradient-b…
Local Non-Cooperative Games with Principled Player Selection for Scalable Motion Planning
Makram Chahine, Roya Firoozi, Wei Xiao +2
Game-theoretic motion planners are a powerful tool for the control of interactive multi-agent robot systems. Indeed, contrary to predict-then-plan paradigms, game-theoretic planner…
Directly 3D Printed, Pneumatically Actuated Multi-Material Robotic Hand
Hanna Matusik, Chao Liu, Daniela Rus
Soft robotic manipulators with many degrees of freedom can carry out complex tasks safely around humans. However, manufacturing of soft robotic hands with several degrees of freedo…
Can a Compact Neuronal Circuit Policy be Re-purposed to Learn Simple Robotic Control?
Ramin Hasani, Mathias Lechner, Alexander Amini +2
We propose a neural information processing system which is obtained by re-purposing the function of a biological neural circuit model, to govern simulated and real-world control ta…
Contact-Aware Safety in Soft Robots Using High-Order Control Barrier and Lyapunov Functions
Kiwan Wong, Maximilian Stölzle, Wei Xiao +3
Robots operating alongside people, particularly in sensitive scenarios such as aiding the elderly with daily tasks or collaborating with workers in manufacturing, must guarantee sa…
Shape Control of a Planar Hyper-Redundant Robot via Hybrid Kinematics-Informed and Learning-based Approach
Yuli Song, Wenbo Li, Wenci Xin +3
Hyper-redundant robots offer high dexterity, making them good at operating in confined and unstructured environments. To extend the reachable workspace, we built a multi-segment fl…
BarrierNet: A Safety-Guaranteed Layer for Neural Networks
Wei Xiao, Ramin Hasani, Xiao Li +1
This paper introduces differentiable higher-order control barrier functions (CBF) that are end-to-end trainable together with learning systems. CBFs are usually overly conservative…
An Experimental Study of Model-based Control for Planar Handed Shearing Auxetics Robots
Maximilian Stölzle, Daniela Rus, Cosimo Della Santina
Parallel robots based on Handed Shearing Auxetics (HSAs) can implement complex motions using standard electric motors while maintaining the complete softness of the structure, than…
Incremental Temporal Logic Synthesis of Control Policies for Robots Interacting with Dynamic Agents
Tichakorn Wongpiromsarn, Alphan Ulusoy, Calin Belta +2
We consider the synthesis of control policies from temporal logic specifications for robots that interact with multiple dynamic environment agents. Each environment agent is modele…
SAFe-Copilot: Unified Shared Autonomy Framework
Phat Nguyen, Erfan Aasi, Shiva Sreeram +4
Autonomous driving systems remain brittle in rare, ambiguous, and out-of-distribution scenarios, where human driver succeed through contextual reasoning. Shared autonomy has emerge…
Flex: End-to-End Text-Instructed Visual Navigation from Foundation Model Features
Makram Chahine, Alex Quach, Alaa Maalouf +2
End-to-end learning directly maps sensory inputs to actions, creating highly integrated and efficient policies for complex robotics tasks. However, such models often struggle to ge…
Solving Continuous Control via Q-learning
Tim Seyde, Peter Werner, Wilko Schwarting +4
While there has been substantial success for solving continuous control with actor-critic methods, simpler critic-only methods such as Q-learning find limited application in the as…
Superfast Configuration-Space Convex Set Computation on GPUs for Online Motion Planning
Peter Werner, Richard Cheng, Tom Stewart +2
In this work, we leverage GPUs to construct probabilistically collision-free convex sets in robot configuration space on the fly. This extends the use of modern motion planning alg…
Data value estimation on private gradients
Zijian Zhou, Xinyi Xu, Daniela Rus +1
For gradient-based machine learning (ML) methods commonly adopted in practice such as stochastic gradient descent, the de facto differential privacy (DP) technique is perturbing th…
Stochastic Dynamic Games in Belief Space
Wilko Schwarting, Alyssa Pierson, Sertac Karaman +1
Information gathering while interacting with other agents under sensing and motion uncertainty is critical in domains such as driving, service robots, racing, or surveillance. The…
A Portable, 3D-Printing Enabled Multi-Vehicle Platform for Robotics Research and Education
Jingjin Yu, Shuai D Han, Wei N Tang +1
microMVP is an affordable, portable, and open source micro-scale mobile robot platform designed for robotics research and education. As a complete and unique multi-vehicle platform…
Minimum-violation LTL Planning with Conflicting Specifications
Jana Tumova, Luis I. Reyes Castro, Sertac Karaman +2
We consider the problem of automatic generation of control strategies for robotic vehicles given a set of high-level mission specifications, such as "Vehicle x must eventually visi…
DataS^3: Dataset Subset Selection for Specialization
Neha Hulkund, Alaa Maalouf, Levi Cai +15
In many real-world machine learning (ML) applications (e.g. detecting broken bones in x-ray images, detecting species in camera traps), in practice models need to perform well on s…
Growing Q-Networks: Solving Continuous Control Tasks with Adaptive Control Resolution
Tim Seyde, Peter Werner, Wilko Schwarting +2
Recent reinforcement learning approaches have shown surprisingly strong capabilities of bang-bang policies for solving continuous control benchmarks. The underlying coarse action s…
Secure -ish Nearest Neighbors Classifier
Hayim Shaul, Dan Feldman, Daniela Rus
In machine learning, classifiers are used to predict a class of a given query based on an existing (classified) database. Given a database S of n d-dimensional points and a d-dimen…
Modeling and Control of Intrinsically Elasticity Coupled Soft-Rigid Robots
Zach J. Patterson, Cosimo Della Santina, Daniela Rus
While much work has been done recently in the realm of model-based control of soft robots and soft-rigid hybrids, most works examine robots that have an inherently serial structure…
Fluidically Innervated Lattices Make Versatile and Durable Tactile Sensors
Annan Zhang, Miguel Flores-Acton, Andy Yu +3
Tactile sensing plays a fundamental role in enabling robots to navigate dynamic and unstructured environments, particularly in applications such as delicate object manipulation, su…
Uncertainty-aware Language Modeling for Selective Question Answering
Qi Yang, Shreya Ravikumar, Fynn Schmitt-Ulms +9
We present an automatic large language model (LLM) conversion approach that produces uncertainty-aware LLMs capable of estimating uncertainty with every prediction. Our approach is…
Variational End-to-End Navigation and Localization
Alexander Amini, Guy Rosman, Sertac Karaman +1
Deep learning has revolutionized the ability to learn "end-to-end" autonomous vehicle control directly from raw sensory data. While there have been recent extensions to handle form…
Liquid Time-constant Recurrent Neural Networks as Universal Approximators
Ramin M. Hasani, Mathias Lechner, Alexander Amini +2
In this paper, we introduce the notion of liquid time-constant (LTC) recurrent neural networks (RNN)s, a subclass of continuous-time RNNs, with varying neuronal time-constant reali…
Toward a Science of Autonomy for Physical Systems
Gregory D. Hager, Daniela Rus, Vijay Kumar +1
Our lives have been immensely improved by decades of automation research -- we are more comfortable, more productive and safer than ever before. Just imagine a world where familiar…
Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Space
Wilko Schwarting, Tim Seyde, Igor Gilitschenski +4
Learning competitive behaviors in multi-agent settings such as racing requires long-term reasoning about potential adversarial interactions. This paper presents Deep Latent Competi…
Perception-Aware Multimodal Spatial Reasoning from Monocular Images
Yanchun Cheng, Rundong Wang, Xulei Yang +4
Spatial reasoning from monocular images is essential for autonomous driving, yet current Vision-Language Models (VLMs) still struggle with fine-grained geometric perception, partic…
Controlling diverse robots by inferring Jacobian fields with deep networks
Sizhe Lester Li, Annan Zhang, Boyuan Chen +4
Mirroring the complex structures and diverse functions of natural organisms is a long-standing challenge in robotics. Modern fabrication techniques have greatly expanded the feasib…
Persistent Robotic Tasks: Monitoring and Sweeping in Changing Environments
Stephen L. Smith, Mac Schwager, Daniela Rus
We present controllers that enable mobile robots to persistently monitor or sweep a changing environment. The changing environment is modeled as a field which grows in locations th…
On-Off Center-Surround Receptive Fields for Accurate and Robust Image Classification
Zahra Babaiee, Ramin Hasani, Mathias Lechner +2
Robustness to variations in lighting conditions is a key objective for any deep vision system. To this end, our paper extends the receptive field of convolutional neural networks w…
SafeDiffuser: Safe Planning with Diffusion Probabilistic Models
Wei Xiao, Tsun-Hsuan Wang, Chuang Gan +1
Diffusion model-based approaches have shown promise in data-driven planning, but there are no safety guarantees, thus making it hard to be applied for safety-critical applications.…
PoSafeNet: Safe Learning with Poset-Structured Neural Nets
Kiwan Wong, Wei Xiao, Daniela Rus
Safe learning is essential for deploying learningbased controllers in safety-critical robotic systems, yet existing approaches often enforce multiple safety constraints uniformly o…
DiffuseBot: Breeding Soft Robots With Physics-Augmented Generative Diffusion Models
Tsun-Hsuan Wang, Juntian Zheng, Pingchuan Ma +6
Nature evolves creatures with a high complexity of morphological and behavioral intelligence, meanwhile computational methods lag in approaching that diversity and efficacy. Co-opt…
MeMo: Memory as a Model
Ryan Wei Heng Quek, Sanghyuk Lee, Alfred Wei Lun Leong +6
Large language models (LLMs) achieve strong performance across a wide range of tasks, but remain frozen after pretraining until subsequent updates. Many real-world applications req…
Multi-Scale Feature Aggregation by Cross-Scale Pixel-to-Region Relation Operation for Semantic Segmentation
Yechao Bai, Ziyuan Huang, Lyuyu Shen +3
Exploiting multi-scale features has shown great potential in tackling semantic segmentation problems. The aggregation is commonly done with sum or concatenation (concat) followed b…
Spatial Uncertainty Sampling for End-to-End Control
Alexander Amini, Ava Soleimany, Sertac Karaman +1
End-to-end trained neural networks (NNs) are a compelling approach to autonomous vehicle control because of their ability to learn complex tasks without manual engineering of rule-…
A Model-Based Decoupling Strategy for Proprioception and Contact Sensing in an Architected Soft Manipulator
Francesco Stella, Annan Zhang, Cosimo Della Santina +2
Soft continuum robots require embedded sensing for proprioception and contact detection, yet integrating sensors into sparse, highly deformable architected structures remains chall…
When Sensors Fail: Temporal Sequence Models for Robust PPO under Sensor Drift
Kevin Vogt-Lowell, Theodoros Tsiligkaridis, Rodney Lafuente-Mercado +4
Real-world reinforcement learning systems must operate under distributional drift in their observation streams, yet most policy architectures implicitly assume fully observed and n…
The Role of Robotics in Infectious Disease Crises
Gregory Hager, Vijay Kumar, Robin Murphy +2
The recent coronavirus pandemic has highlighted the many challenges faced by the healthcare, public safety, and economic systems when confronted with a surge in patients that requi…
ActiveDPO: Active Direct Preference Optimization for Sample-Efficient Alignment
Xiaoqiang Lin, Arun Verma, Zhongxiang Dai +3
The recent success in using human preferences to align large language models (LLMs) has significantly improved their performance in various downstream tasks, such as question answe…
Estimating the State of Epidemics Spreading with Graph Neural Networks
Abhishek Tomy, Matteo Razzanelli, Francesco Di Lauro +2
When an epidemic spreads into a population, it is often unpractical or impossible to have a continuous monitoring of all subjects involved. As an alternative, algorithmic solutions…
A Roadmap for Climate-Relevant Robotics Research
Alan Papalia, Charles Dawson, Laurentiu L. Anton +25
Climate change is one of the defining challenges of the 21st century, and many in the robotics community are looking for ways to contribute. This paper presents a roadmap for clima…
Multi-Abstractive Neural Controller: An Efficient Hierarchical Control Architecture for Interactive Driving
Xiao Li, Igor Gilitschenski, Guy Rosman +2
As learning-based methods make their way from perception systems to planning/control stacks, robot control systems have started to enjoy the benefits that data-driven methods provi…
Free-Space Ellipsoid Graphs for Multi-Agent Target Monitoring
Aaron Ray, Alyssa Pierson, Daniela Rus
We apply a novel framework for decomposing and reasoning about free space in an environment to a multi-agent persistent monitoring problem. Our decomposition method represents free…
Learning to Plan Optimistically: Uncertainty-Guided Deep Exploration via Latent Model Ensembles
Tim Seyde, Wilko Schwarting, Sertac Karaman +1
Learning complex robot behaviors through interaction requires structured exploration. Planning should target interactions with the potential to optimize long-term performance, whil…
In-Network Distributed Solar Current Prediction
Elizabeth Basha, Raja Jurdak, Daniela Rus
Long-term sensor network deployments demand careful power management. While managing power requires understanding the amount of energy harvestable from the local environment, curre…
Surgical Foundation Model Leveraging Compression and Entropy Maximization for Image-Guided Surgical Assistance
Lianhao Yin, Ozanan Meireles, Guy Rosman +1
Real-time video understanding is critical to guide procedures in minimally invasive surgery (MIS). However, supervised learning approaches require large, annotated datasets that ar…
Physical Human-Robot Interaction for Grasping in Augmented Reality via Rigid-Soft Robot Synergy
Huishi Huang, Jack Klusmann, Haozhe Wang +9
Hybrid rigid-soft robots combine the precision of rigid manipulators with the compliance and adaptability of soft arms, offering a promising approach for versatile grasping in unst…
Towards Cooperative Flight Control Using Visual-Attention
Lianhao Yin, Makram Chahine, Tsun-Hsuan Wang +5
The cooperation of a human pilot with an autonomous agent during flight control realizes parallel autonomy. We propose an air-guardian system that facilitates cooperation between a…