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Found 26 papers, 2 papers with code
Beyond Data Scarcity: A Frequency-Driven Framework for Zero-Shot Forecasting
Time series forecasting is critical in numerous real-world applications, requiring accurate predictions of future values based on observed patterns.
Gradient-Free Neural Network Training on the Edge
Training neural networks is computationally heavy and energy-intensive.
Robot Instance Segmentation with Few Annotations for Grasping
The ability of robots to manipulate objects relies heavily on their aptitude for visual perception.
Ranked #1 on
Instance Segmentation
on ARMBench
Towards Natural Language-Driven Assembly Using Foundation Models
The main objective of these methods is to develop a generalist policy that can control robots with various embodiments.
Radar Spectra-Language Model for Automotive Scene Parsing
Finally, we explore the benefit of the learned representation for scene retrieval using radar spectra only, and obtain improvements in free space segmentation and object detection merely by injecting the spectra embedding into a baseline model.
ISCUTE: Instance Segmentation of Cables Using Text Embedding
In the field of robotics and automation, conventional object recognition and instance segmentation methods face a formidable challenge when it comes to perceiving Deformable Linear Objects (DLOs) like wires, cables, and flexible tubes.
Generative Modeling of Graphs via Joint Diffusion of Node and Edge Attributes
Graph generation is integral to various engineering and scientific disciplines.
An Axiomatic Approach to Model-Agnostic Concept Explanations
Concept explanation is a popular approach for examining how human-interpretable concepts impact the predictions of a model.
Offline Skill Graph (OSG): A Framework for Learning and Planning using Offline Reinforcement Learning Skills
We demonstrate our approach by testing it on a robotic arm that is required to solve complex tasks.
CONFIDE: Contextual Finite Differences Modelling of PDEs
We introduce a method for inferring an explicit PDE from a data sample generated by previously unseen dynamics, based on a learned context.
GraphVid: It Only Takes a Few Nodes to Understand a Video
We propose a concise representation of videos that encode perceptually meaningful features into graphs.
Towards Autonomous Grading In The Real World
As the heuristics are capable of successfully solving the task in the simulated environment, we show they can be leveraged to guide a learning agent which can generalize and solve the task both in simulation and in a scaled prototype environment.
InsertionNet 2.0: Minimal Contact Multi-Step Insertion Using Multimodal Multiview Sensory Input
We address the problem of devising the means for a robot to rapidly and safely learn insertion skills with just a few human interventions and without hand-crafted rewards or demonstrations.
AGPNet -- Autonomous Grading Policy Network
In this work, we establish heuristics and learning strategies for the autonomous control of a dozer grading an uneven area studded with sand piles.
LSP : Acceleration and Regularization of Graph Neural Networks via Locality Sensitive Pruning of Graphs
In this paper, we take a further step towards demystifying this phenomenon and propose a systematic method called Locality-Sensitive Pruning (LSP) for graph pruning based on Locality-Sensitive Hashing.
A Hybrid Approach for Learning to Shift and Grasp with Elaborate Motion Primitives
Many possible fields of application of robots in real world settings hinge on the ability of robots to grasp objects.
Sim and Real: Better Together
Simulation is used extensively in autonomous systems, particularly in robotic manipulation.
BIDCD -- Bosch Industrial Depth Completion Dataset
We introduce BIDCD -- the Bosch Industrial Depth Completion Dataset.
Vision-Guided Forecasting -- Visual Context for Multi-Horizon Time Series Forecasting
We examine the contribution of vision features, and find that a model fed with vision features achieves an error that is 56. 6% and 66. 9% of the error of a model that doesn't use those features, on the Udacity and Comma2k19 datasets respectively.
InsertionNet -- A Scalable Solution for Insertion
Complicated assembly processes can be described as a sequence of two main activities: grasping and insertion.
SOLO: Search Online, Learn Offline for Combinatorial Optimization Problems
This method can equally be applied to both the offline, as well as online, variants of the combinatorial problem, in which the problem components (e. g., jobs in scheduling problems) are not known in advance, but rather arrive during the decision-making process.
Depth Completion with RGB Prior
Depth cameras are a prominent perception system for robotics, especially when operating in natural unstructured environments.
Practical Risk Measures in Reinforcement Learning
Practical application of Reinforcement Learning (RL) often involves risk considerations.
One-Shot Session Recommendation Systems with Combinatorial Items
Our optimization problem is formulated as an MDP where the action space is of a combinatorial nature as we recommend in each round, multiple items.
Contextual Markov Decision Processes
The objective is to learn a strategy that maximizes the accumulated reward across all contexts.
Policy Gradients with Variance Related Risk Criteria
In this paper we devise a framework for local policy gradient style algorithms for reinforcement learning for variance related criteria.
