Learning From Physical Human Feedback An Object Centric One Shot Adaptation Method

June 2, 2026
Media Summary: Experiment demonstrations of the proposed latent Adeli, H., Ahn, S., & Zelinsky, G. (2021). Recurrent Attention Models with In this video, we propose a fast learner grasping pipeline able to grasp

Learning From Physical Human Feedback An Object Centric One Shot Adaptation Method - Detailed Analysis & Overview

Experiment demonstrations of the proposed latent Adeli, H., Ahn, S., & Zelinsky, G. (2021). Recurrent Attention Models with In this video, we propose a fast learner grasping pipeline able to grasp IEEE Transactions on Robotics (Volume: 34, Issue: This work is part of the PacMan project. Work primarily by Marek Kopicki, Jeremy Wyatt and Renaud Detry. Great contributions ... Authors: Alessandro Bonardi, Stephen James, Andrew J. Davison Dyson Robotics Lab, Imperial College London.

For traditional robots, it usually takes a significant amount of The lack of large robotics datasets is arguably the most important obstacle in front of robot Whether it's a robot programmed to perform Jay K. Shah, Aakash Yadav, Sarah K. Hopko, Prabhakar R. Pagilla, Ranjana K. Mehta IEEE RO-MAN 2023 For more updates, ...

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Learning from Physical Human Feedback: An Object-Centric One-Shot Adaptation Method
Learning Dynamic Object-centric Representations for Robotic Manipulation
Unsupervised object-centric video generation and decomposition in 3D (NeurIPS '20)
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CoRL 2020, Spotlight Talk 176: Learning to Compose Hierarchical Object-Centric Controllers for Ro...
Reasoning About Physical Interactions with Object-Centric Models, ICLR 2019
Easy Grasping Location Learning From One shot Demonstration
Trajectory Deformations from Physical Human-Robot Interaction
One Shot Grasp Learning, Selection and Adaptation for Novel Objects
Learning One-Shot Imitation from Humans without Humans
One-Shot Affordance Learning (OSAL): Learning to Manipulate Articulated Objects by Observing Once
Preference Learning from Minimal Human Feedback for Interactive

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Learning from Physical Human Feedback: An Object-Centric One-Shot Adaptation Method

Learning from Physical Human Feedback: An Object-Centric One-Shot Adaptation Method

Paper, supplementary material, code: https://alvinosaur.github.io/AboutMe/projects/opa/

Learning Dynamic Object-centric Representations for Robotic Manipulation

Learning Dynamic Object-centric Representations for Robotic Manipulation

Experiment demonstrations of the proposed latent

Unsupervised object-centric video generation and decomposition in 3D (NeurIPS '20)

Unsupervised object-centric video generation and decomposition in 3D (NeurIPS '20)

Presentation for the paper "Unsupervised

Learning object centric attention using a recurrent dorsal ventral neural Network

Learning object centric attention using a recurrent dorsal ventral neural Network

Adeli, H., Ahn, S., & Zelinsky, G. (2021). Recurrent Attention Models with

CoRL 2020, Spotlight Talk 176: Learning to Compose Hierarchical Object-Centric Controllers for Ro...

CoRL 2020, Spotlight Talk 176: Learning to Compose Hierarchical Object-Centric Controllers for Ro...

"**

Reasoning About Physical Interactions with Object-Centric Models, ICLR 2019

Reasoning About Physical Interactions with Object-Centric Models, ICLR 2019

Video for the paper "Reasoning About

Easy Grasping Location Learning From One shot Demonstration

Easy Grasping Location Learning From One shot Demonstration

In this video, we propose a fast learner grasping pipeline http://hdl.handle.net/10985/17102 able to grasp

Trajectory Deformations from Physical Human-Robot Interaction

Trajectory Deformations from Physical Human-Robot Interaction

IEEE Transactions on Robotics (Volume: 34, Issue:

One Shot Grasp Learning, Selection and Adaptation for Novel Objects

One Shot Grasp Learning, Selection and Adaptation for Novel Objects

This work is part of the PacMan project. Work primarily by Marek Kopicki, Jeremy Wyatt and Renaud Detry. Great contributions ...

Learning One-Shot Imitation from Humans without Humans

Learning One-Shot Imitation from Humans without Humans

Authors: Alessandro Bonardi, Stephen James, Andrew J. Davison Dyson Robotics Lab, Imperial College London.

One-Shot Affordance Learning (OSAL): Learning to Manipulate Articulated Objects by Observing Once

One-Shot Affordance Learning (OSAL): Learning to Manipulate Articulated Objects by Observing Once

For traditional robots, it usually takes a significant amount of

Preference Learning from Minimal Human Feedback for Interactive

Preference Learning from Minimal Human Feedback for Interactive

The lack of large robotics datasets is arguably the most important obstacle in front of robot

How Robots Sense, Plan, and Act

How Robots Sense, Plan, and Act

Whether it's a robot programmed to perform

Reinforcement Learning-based Grasping via One-Shot Affordance Localization

Reinforcement Learning-based Grasping via One-Shot Affordance Localization

Title: Reinforcement

Mutual Adaptation in Human-Robot Co Transportation with Human Preference Uncertainty

Mutual Adaptation in Human-Robot Co Transportation with Human Preference Uncertainty

Paper: https://arxiv.org/abs/2503.08895 Mutual

TO3: Using Object centric Trajectories as input for Grasp planning

TO3: Using Object centric Trajectories as input for Grasp planning

Backup video for TO3.

Object Pose and Shape Estimation for Grasping: Does it Work?

Object Pose and Shape Estimation for Grasping: Does it Work?

The problem of

Learning One-Shot Imitation from Humans without Humans

Learning One-Shot Imitation from Humans without Humans

Authors: Alessandro Bonardi, Stephen James, Andrew J. Davison Dyson Robotics Lab, Imperial College London.

Robot Adaptation Under Operator Cognitive Fatigue Using Reinforcement Learning

Robot Adaptation Under Operator Cognitive Fatigue Using Reinforcement Learning

Jay K. Shah, Aakash Yadav, Sarah K. Hopko, Prabhakar R. Pagilla, Ranjana K. Mehta IEEE RO-MAN 2023 For more updates, ...

SORNet: Spatial Object-Centric Representations for Sequential Manipulation

SORNet: Spatial Object-Centric Representations for Sequential Manipulation

We propose SORNet (Spatial