Tell me what this is: Few-Shot Incremental Object Learning by a Robot
- URL: http://arxiv.org/abs/2008.00819v1
- Date: Wed, 15 Jul 2020 04:42:14 GMT
- Title: Tell me what this is: Few-Shot Incremental Object Learning by a Robot
- Authors: Ali Ayub, Alan R. Wagner
- Abstract summary: This paper presents a system for incrementally training a robot to recognize different object categories.
The paper uses a recently developed state-of-the-art method for few-shot incremental learning of objects.
- Score: 22.387008072671005
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: For many applications, robots will need to be incrementally trained to
recognize the specific objects needed for an application. This paper presents a
practical system for incrementally training a robot to recognize different
object categories using only a small set of visual examples provided by a
human. The paper uses a recently developed state-of-the-art method for few-shot
incremental learning of objects. After learning the object classes
incrementally, the robot performs a table cleaning task organizing objects into
categories specified by the human. We also demonstrate the system's ability to
learn arrangements of objects and predict missing or incorrectly placed
objects. Experimental evaluations demonstrate that our approach achieves nearly
the same performance as a system trained with all examples at one time (batch
training), which constitutes a theoretical upper bound.
Related papers
- Learning Object Properties Using Robot Proprioception via Differentiable Robot-Object Interaction [52.12746368727368]
Differentiable simulation has become a powerful tool for system identification.
Our approach calibrates object properties by using information from the robot, without relying on data from the object itself.
We demonstrate the effectiveness of our method on a low-cost robotic platform.
arXiv Detail & Related papers (2024-10-04T20:48:38Z) - Kinematic-aware Prompting for Generalizable Articulated Object
Manipulation with LLMs [53.66070434419739]
Generalizable articulated object manipulation is essential for home-assistant robots.
We propose a kinematic-aware prompting framework that prompts Large Language Models with kinematic knowledge of objects to generate low-level motion waypoints.
Our framework outperforms traditional methods on 8 categories seen and shows a powerful zero-shot capability for 8 unseen articulated object categories.
arXiv Detail & Related papers (2023-11-06T03:26:41Z) - Few-Shot In-Context Imitation Learning via Implicit Graph Alignment [15.215659641228655]
We formulate imitation learning as a conditional alignment problem between graph representations of objects.
We show that this conditioning allows for in-context learning, where a robot can perform a task on a set of new objects immediately after the demonstrations.
arXiv Detail & Related papers (2023-10-18T18:26:01Z) - You Only Look at One: Category-Level Object Representations for Pose
Estimation From a Single Example [26.866356430469757]
We present a method for achieving category-level pose estimation by inspection of just a single object from a desired category.
We demonstrate that our method runs in real-time, enabling a robot manipulator equipped with an RGBD sensor to perform online 6D pose estimation for novel objects.
arXiv Detail & Related papers (2023-05-22T01:32:24Z) - Learning Reward Functions for Robotic Manipulation by Observing Humans [92.30657414416527]
We use unlabeled videos of humans solving a wide range of manipulation tasks to learn a task-agnostic reward function for robotic manipulation policies.
The learned rewards are based on distances to a goal in an embedding space learned using a time-contrastive objective.
arXiv Detail & Related papers (2022-11-16T16:26:48Z) - Lifelong Ensemble Learning based on Multiple Representations for
Few-Shot Object Recognition [6.282068591820947]
We present a lifelong ensemble learning approach based on multiple representations to address the few-shot object recognition problem.
To facilitate lifelong learning, each approach is equipped with a memory unit for storing and retrieving object information instantly.
We have performed extensive sets of experiments to assess the performance of the proposed approach in offline, and open-ended scenarios.
arXiv Detail & Related papers (2022-05-04T10:29:10Z) - DemoGrasp: Few-Shot Learning for Robotic Grasping with Human
Demonstration [42.19014385637538]
We propose to teach a robot how to grasp an object with a simple and short human demonstration.
We first present a small sequence of RGB-D images displaying a human-object interaction.
This sequence is then leveraged to build associated hand and object meshes that represent the interaction.
arXiv Detail & Related papers (2021-12-06T08:17:12Z) - Lifelong Robotic Reinforcement Learning by Retaining Experiences [61.79346922421323]
Many multi-task reinforcement learning efforts assume the robot can collect data from all tasks at all times.
In this work, we study a practical sequential multi-task RL problem motivated by the practical constraints of physical robotic systems.
We derive an approach that effectively leverages the data and policies learned for previous tasks to cumulatively grow the robot's skill-set.
arXiv Detail & Related papers (2021-09-19T18:00:51Z) - Simultaneous Multi-View Object Recognition and Grasping in Open-Ended
Domains [0.0]
We propose a deep learning architecture with augmented memory capacities to handle open-ended object recognition and grasping simultaneously.
We demonstrate the ability of our approach to grasp never-seen-before objects and to rapidly learn new object categories using very few examples on-site in both simulation and real-world settings.
arXiv Detail & Related papers (2021-06-03T14:12:11Z) - What Can I Do Here? Learning New Skills by Imagining Visual Affordances [128.65223577406587]
We show how generative models of possible outcomes can allow a robot to learn visual representations of affordances.
In effect, prior data is used to learn what kinds of outcomes may be possible, such that when the robot encounters an unfamiliar setting, it can sample potential outcomes from its model.
We show that visuomotor affordance learning (VAL) can be used to train goal-conditioned policies that operate on raw image inputs.
arXiv Detail & Related papers (2021-06-01T17:58:02Z) - Reactive Human-to-Robot Handovers of Arbitrary Objects [57.845894608577495]
We present a vision-based system that enables human-to-robot handovers of unknown objects.
Our approach combines closed-loop motion planning with real-time, temporally-consistent grasp generation.
We demonstrate the generalizability, usability, and robustness of our approach on a novel benchmark set of 26 diverse household objects.
arXiv Detail & Related papers (2020-11-17T21:52:22Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.