2.4k citations · 2.7k across the 29 of their papers we have counts for
35 papers
Temporal Policy: History-Initialized Action Generation for Robotic Learning from Demonstration
Dylan Miller, Martin Jagersand
By relying on independent couplings from uninformative Gaussian priors, standard diffusion and flow matching models are forced to learn complex, high-cost vector fields to reach th…
Sensorless Four-Channel Control Architecture Using Inverse Dynamics Modeling for Human-Scale Bilateral Teleoperation
Amir Noohian, Dylan Miller, Justin Valentine +2
The four-channel teleoperation architecture is a well-established framework for achieving transparency in bilateral systems. However, its performance in human-scale teleoperation i…
HITL-D: Human In The Loop Diffusion Assisted Shared Control
Riley Zilka, Sergey Khlynovskiy, Allie Wang +1
Autonomous manipulation systems have achieved remarkable capabilities, yet the integration of human expertise with diffusion-based policies in shared control remains relatively une…
Revisiting Sparse Rewards for Goal-Reaching Reinforcement Learning
Gautham Vasan, Yan Wang, Fahim Shahriar +3
Many real-world robot learning problems, such as pick-and-place or arriving at a destination, can be seen as a problem of reaching a goal state as soon as possible. These problems,…
A Simple Decentralized Cross-Entropy Method
Zichen Zhang, Jun Jin, Martin Jagersand +2
Cross-Entropy Method (CEM) is commonly used for planning in model-based reinforcement learning (MBRL) where a centralized approach is typically utilized to update the sampling dist…
Generalizable task representation learning from human demonstration videos: a geometric approach
Jun Jin, Martin Jagersand
We study the problem of generalizable task learning from human demonstration videos without extra training on the robot or pre-recorded robot motions. Given a set of human demonstr…
Analyzing Neural Jacobian Methods in Applications of Visual Servoing and Kinematic Control
Michael Przystupa, Masood Dehghan, Martin Jagersand +1
Designing adaptable control laws that can transfer between different robots is a challenge because of kinematic and dynamic differences, as well as in scenarios where external sens…
A Quantitative Analysis of Activities of Daily Living: Insights into Improving Functional Independence with Assistive Robotics
Laura Petrich, Jun Jin, Masood Dehghan +1
Human assistive robotics have the potential to help the elderly and individuals living with disabilities with their Activities of Daily Living (ADL). Robotics researchers focus on…
Assistive arm and hand manipulation: How does current research intersect with actual healthcare needs?
Laura Petrich, Jun Jin, Masood Dehghan +1
Human assistive robotics have the potential to help the elderly and individuals living with disabilities with their Activities of Daily Living (ADL). Robotics researchers present b…
Boundary-Aware Segmentation Network for Mobile and Web Applications
Xuebin Qin, Deng-Ping Fan, Chenyang Huang +6
Although deep models have greatly improved the accuracy and robustness of image segmentation, obtaining segmentation results with highly accurate boundaries and fine structures is…
U-Net: Going Deeper with Nested U-Structure for Salient Object Detection
Xuebin Qin, Zichen Zhang, Chenyang Huang +3
In this paper, we design a simple yet powerful deep network architecture, U-Net, for salient object detection (SOD). The architecture of our U-Net is a two-level nested U-s…
A Geometric Perspective on Visual Imitation Learning
Jun Jin, Laura Petrich, Masood Dehghan +1
We consider the problem of visual imitation learning without human supervision (e.g. kinesthetic teaching or teleoperation), nor access to an interactive reinforcement learning (RL…
Understanding Contexts Inside Robot and Human Manipulation Tasks through a Vision-Language Model and Ontology System in a Video Stream
Chen Jiang, Masood Dehghan, Martin Jagersand
Manipulation tasks in daily life, such as pouring water, unfold intentionally under specialized manipulation contexts. Being able to process contextual knowledge in these Activitie…
Weakly Supervised Few-shot Object Segmentation using Co-Attention with Visual and Semantic Embeddings
Mennatullah Siam, Naren Doraiswamy, Boris N. Oreshkin +2
Significant progress has been made recently in developing few-shot object segmentation methods. Learning is shown to be successful in few-shot segmentation settings, using pixel-le…
One-Shot Weakly Supervised Video Object Segmentation
Mennatullah Siam, Naren Doraiswamy, Boris N. Oreshkin +2
Conventional few-shot object segmentation methods learn object segmentation from a few labelled support images with strongly labelled segmentation masks. Recent work has shown to p…
Visual Geometric Skill Inference by Watching Human Demonstration
Jun Jin, Laura Petrich, Zichen Zhang +2
We study the problem of learning manipulation skills from human demonstration video by inferring the association relationships between geometric features. Motivation for this work…
Mapless Navigation among Dynamics with Social-safety-awareness: a reinforcement learning approach from 2D laser scans
Jun Jin, Nhat M. Nguyen, Nazmus Sakib +3
We propose a method to tackle the problem of mapless collision-avoidance navigation where humans are present using 2D laser scans. Our proposed method uses ego-safety to measure co…
Bridging Visual Perception with Contextual Semantics for Understanding Robot Manipulation Tasks
Chen Jiang, Martin Jagersand
Understanding manipulation scenarios allows intelligent robots to plan for appropriate actions to complete a manipulation task successfully. It is essential for intelligent robots…
Long range teleoperation for fine manipulation tasks under time-delay network conditions
Jun Jin, Laura Petrich, Shida He +2
We present a coarse-to-fine approach based semi-autonomous teleoperation system using vision guidance. The system is optimized for long range teleoperation tasks under time-delay n…
Evaluation of state representation methods in robot hand-eye coordination learning from demonstration
Jun Jin, Masood Dehghan, Laura Petrich +2
We evaluate different state representation methods in robot hand-eye coordination learning on different aspects. Regarding state dimension reduction: we evaluates how these state r…
Video Object Segmentation using Teacher-Student Adaptation in a Human Robot Interaction (HRI) Setting
Mennatullah Siam, Chen Jiang, Steven Lu +4
Video object segmentation is an essential task in robot manipulation to facilitate grasping and learning affordances. Incremental learning is important for robotics in unstructured…
Robot eye-hand coordination learning by watching human demonstrations: a task function approximation approach
Jun Jin, Laura Petrich, Masood Dehghan +2
We present a robot eye-hand coordination learning method that can directly learn visual task specification by watching human demonstrations. Task specification is represented as a…
Online Object and Task Learning via Human Robot Interaction
Masood Dehghan, Zichen Zhang, Mennatullah Siam +3
This work describes the development of a robotic system that acquires knowledge incrementally through human interaction where new tools and motions are taught on the fly. The robot…
RTSeg: Real-time Semantic Segmentation Comparative Study
Mennatullah Siam, Mostafa Gamal, Moemen Abdel-Razek +2
Semantic segmentation benefits robotics related applications especially autonomous driving. Most of the research on semantic segmentation is only on increasing the accuracy of segm…