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Found 44 papers, 12 papers with code
MetaCropFollow: Few-Shot Adaptation with Meta-Learning for Under-Canopy Navigation
Autonomous under-canopy navigation faces additional challenges compared to over-canopy settings - for example the tight spacing between the crop rows, degraded GPS accuracy and excessive clutter.
Fed-EC: Bandwidth-Efficient Clustering-Based Federated Learning For Autonomous Visual Robot Navigation
The framework addresses the key federated learning challenge of deteriorating model performance of a single global model due to the presence of non-IID data across real-world robots.
AdaCropFollow: Self-Supervised Online Adaptation for Visual Under-Canopy Navigation
A large number of failures of this system can be attributed to the inability of the perception model to adapt to the domain shift encountered during deployment.
MOOSS: Mask-Enhanced Temporal Contrastive Learning for Smooth State Evolution in Visual Reinforcement Learning
In visual Reinforcement Learning (RL), learning from pixel-based observations poses significant challenges on sample efficiency, primarily due to the complexity of extracting informative state representations from high-dimensional data.
PlantTrack: Task-Driven Plant Keypoint Tracking with Zero-Shot Sim2Real Transfer
Tracking plant features is crucial for various agricultural tasks like phenotyping, pruning, or harvesting, but the unstructured, cluttered, and deformable nature of plant environments makes it a challenging task.
Hyperspectral Image Reconstruction for Predicting Chick Embryo Mortality Towards Advancing Egg and Hatchery Industry
This study aims to reconstruct hyperspectral images from RGB images for non-destructive early prediction of chick embryo mortality.
Lessons from Deploying CropFollow++: Under-Canopy Agricultural Navigation with Keypoints
We present a vision-based navigation system for under-canopy agricultural robots using semantic keypoints.
OW-VISCapTor: Abstractors for Open-World Video Instance Segmentation and Captioning
Concretely, we connect a multi-scale visual feature extractor and a large language model (LLM) by developing an object abstractor and an object-to-text abstractor.
DeepSRGM -- Sequence Classification and Ranking in Indian Classical Music with Deep Learning
Our method achieves an accuracy of 88. 1% and 97 % during inference on the Comp Music Carnatic dataset and its 10 Raga subset respectively making it the state-of-the-art for the Raga recognition task.
Gazebo Plants: Simulating Plant-Robot Interaction with Cosserat Rods
Robotic harvesting has the potential to positively impact agricultural productivity, reduce costs, improve food quality, enhance sustainability, and to address labor shortage.
Exploitation-Guided Exploration for Semantic Embodied Navigation
In the recent progress in embodied navigation and sim-to-robot transfer, modular policies have emerged as a de facto framework.
Revealing the Power of Masked Autoencoders in Traffic Forecasting
Our results on traffic benchmarks show that STMAE can largely enhance the forecasting capabilities of various spatial-temporal models.
Online Segmented Recursive Least-Squares for Multipath Doppler Tracking
Underwater communication signals typically suffer from distortion due to motion-induced Doppler.
CoMusion: Towards Consistent Stochastic Human Motion Prediction via Motion Diffusion
Stochastic Human Motion Prediction (HMP) aims to predict multiple possible future human pose sequences from observed ones.
Towards Accurate Human Motion Prediction via Iterative Refinement
To address the problem, in this work we propose FreqMRN, a human motion prediction framework that takes into account both the kinematic structure of the human body and the temporal smoothness nature of motion.
Ranked #2 on
Human Pose Forecasting
on 3DPW
Unmatched uncertainty mitigation through neural network supported model predictive control
We utilize a deep neural network (DNN) as an oracle in the underlying optimization problem of learning based MPC (LBMPC) to estimate unmatched uncertainties.
Deep Model Predictive Control
This paper presents a deep learning based model predictive control algorithm for control affine nonlinear discrete time systems with matched and bounded state-dependent uncertainties of unknown structure.
Reinforcement Learning-Based Air Traffic Deconfliction
Remain Well Clear, keeping the aircraft away from hazards by the appropriate separation distance, is an essential technology for the safe operation of uncrewed aerial vehicles in congested airspace.
Context-Aware Relative Object Queries To Unify Video Instance and Panoptic Segmentation
We evaluate the proposed approach across three challenging tasks: video instance segmentation, multi-object tracking and segmentation, and video panoptic segmentation.
Dynamic Graph Node Classification via Time Augmentation
Node classification on dynamic graphs is challenging for two reasons.
Last-Mile Embodied Visual Navigation
Realistic long-horizon tasks like image-goal navigation involve exploratory and exploitative phases.
Multimedia Generative Script Learning for Task Planning
Goal-oriented generative script learning aims to generate subsequent steps to reach a particular goal, which is an essential task to assist robots or humans in performing stereotypical activities.
Proactive Anomaly Detection for Robot Navigation with Multi-Sensor Fusion
The ability to detect such anomalous behaviors is a key component in modern robots to achieve high-levels of autonomy.
WayFAST: Navigation with Predictive Traversability in the Field
We present a self-supervised approach for learning to predict traversable paths for wheeled mobile robots that require good traction to navigate.
Visual Servoing for Pose Control of Soft Continuum Arm in a Structured Environment
For soft continuum arms, visual servoing is a popular control strategy that relies on visual feedback to close the control loop.
Distributed State Estimation with Deep Neural Networks for Uncertain Nonlinear Systems under Event-Triggered Communication
Distributed state estimation is examined for a sensor network tasked with reconstructing a system's state through the use of a distributed and event-triggered observer.
Stochastic Deep Model Reference Adaptive Control
In this paper, we present a Stochastic Deep Neural Network-based Model Reference Adaptive Control.
Learned Visual Navigation for Under-Canopy Agricultural Robots
We describe a system for visually guided autonomous navigation of under-canopy farm robots.
Forced Variational Integrator Networks for Prediction and Control of Mechanical Systems
As deep learning becomes more prevalent for prediction and control of real physical systems, it is important that these overparameterized models are consistent with physically plausible dynamics.
High Throughput Soybean Pod-Counting with In-Field Robotic Data Collection and Machine-Vision Based Data Analysis
We report a correlation of 0. 67 between our automated pod counts and soybean yield.
Adaptive Policy Transfer in Reinforcement Learning
Efficient and robust policy transfer remains a key challenge for reinforcement learning to become viable for real-wold robotics.
A Self-Learning Disturbance Observer for Nonlinear Systems in Feedback-Error Learning Scheme
This paper represents a novel online self-learning disturbance observer (SLDO) by benefiting from the combination of a type-2 neuro-fuzzy structure (T2NFS), feedback-error learning scheme and sliding mode control (SMC) theory.
High precision control and deep learning-based corn stand counting algorithms for agricultural robot
The robot, termed TerraSentia, is designed to automate the measurement of plant traits for efficient phenotyping as an alternative to manual measurements.
Assignment-Space-Based Multi-Object Tracking and Segmentation
In contrast, we formulate a global method for MOTS over the space of assignments rather than detections: First, we find all top-k assignments of objects detected and segmented between any two consecutive frames and develop a structured prediction formulation to score assignment sequences across any number of consecutive frames.
Multi-Object Tracking
Multi-Object Tracking and Segmentation
+4
Multi-Modal Anomaly Detection for Unstructured and Uncertain Environments
To achieve high-levels of autonomy, modern robots require the ability to detect and recover from anomalies and failures with minimal human supervision.
Towards Principled Objectives for Contrastive Disentanglement
%In fact, contrastive disentanglement and unsupervised recovery are often combined in that we seek additional variations that exhibit salient factors/properties.
Adapt-to-Learn: Policy Transfer in Reinforcement Learning
Efficient and robust policy transfer remains a key challenge in reinforcement learning.
Deep Model Reference Adaptive Control
We present a new neuroadaptive architecture: Deep Neural Network based Model Reference Adaptive Control (DMRAC).
Learning to Cope with Adversarial Attacks
Hence, it is imperative that RL agents deployed in real-life applications have the capability to detect and mitigate adversarial attacks in an online fashion.
Cross-Domain Transfer in Reinforcement Learning using Target Apprentice
Many of the available techniques approach the transfer architecture as a method of speeding up the target task learning.
Robust Deep Reinforcement Learning with Adversarial Attacks
This paper proposes adversarial attacks for Reinforcement Learning (RL) and then improves the robustness of Deep Reinforcement Learning algorithms (DRL) to parameter uncertainties with the help of these attacks.
Hierarchic Kernel Recursive Least-Squares
We present a new kernel-based algorithm for modeling evenly distributed multidimensional datasets that does not rely on input space sparsification.
Kernel Observers: Systems-Theoretic Modeling and Inference of Spatiotemporally Evolving Processes
We consider the problem of estimating the latent state of a spatiotemporally evolving continuous function using very few sensor measurements.
Can Co-robots Learn to Teach?
We explore beyond existing work on learning from demonstration by asking the question: Can robots learn to teach?, that is, can a robot autonomously learn an instructional policy from expert demonstration and use it to instruct or collaborate with humans in executing complex tasks in uncertain environments?
