Self-Supervised Learning of Multi-Object Keypoints for Robotic Manipulation

• URL: http://arxiv.org/abs/2205.08316v1
• Date: Tue, 17 May 2022 13:15:07 GMT
• Title: Self-Supervised Learning of Multi-Object Keypoints for Robotic Manipulation
• Authors: Jan Ole von Hartz, Eugenio Chisari, Tim Welschehold and Abhinav Valada
• Abstract summary: In this paper, we demonstrate the efficacy of learning image keypoints via the Dense Correspondence pretext task for downstream policy learning. We evaluate our approach on diverse robot manipulation tasks, compare it to other visual representation learning approaches, and demonstrate its flexibility and effectiveness for sample-efficient policy learning.
• Score: 8.939008609565368
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, policy learning methods using either reinforcement or imitation have made significant progress. However, both techniques still suffer from being computationally expensive and requiring large amounts of training data. This problem is especially prevalent in real-world robotic manipulation tasks, where access to ground truth scene features is not available and policies are instead learned from raw camera observations. In this paper, we demonstrate the efficacy of learning image keypoints via the Dense Correspondence pretext task for downstream policy learning. Extending prior work to challenging multi-object scenes, we show that our model can be trained to deal with important problems in representation learning, primarily scale-invariance and occlusion. We evaluate our approach on diverse robot manipulation tasks, compare it to other visual representation learning approaches, and demonstrate its flexibility and effectiveness for sample-efficient policy learning.

Related papers

• Self-Explainable Affordance Learning with Embodied Caption [63.88435741872204]
  We introduce Self-Explainable Affordance learning (SEA) with embodied caption. SEA enables robots to articulate their intentions and bridge the gap between explainable vision-language caption and visual affordance learning. We propose a novel model to effectively combine affordance grounding with self-explanation in a simple but efficient manner.
  arXiv  Detail & Related papers  (2024-04-08T15:22:38Z)
• End-to-End Affordance Learning for Robotic Manipulation [4.405918052597016]
  Learning to manipulate 3D objects in an interactive environment has been a challenging problem in Reinforcement Learning. Visual affordance has shown great prospects in providing object-centric information priors with effective actionable semantics. In this study, we take advantage of visual affordance by using the contact information generated during the RL training process to predict contact maps of interest.
  arXiv  Detail & Related papers  (2022-09-26T18:24:28Z)
• Visuomotor Control in Multi-Object Scenes Using Object-Aware Representations [25.33452947179541]
  We show the effectiveness of object-aware representation learning techniques for robotic tasks. Our model learns control policies in a sample-efficient manner and outperforms state-of-the-art object techniques.
  arXiv  Detail & Related papers  (2022-05-12T19:48:11Z)
• What Matters in Learning from Offline Human Demonstrations for Robot Manipulation [64.43440450794495]
  We conduct an extensive study of six offline learning algorithms for robot manipulation. Our study analyzes the most critical challenges when learning from offline human data. We highlight opportunities for learning from human datasets.
  arXiv  Detail & Related papers  (2021-08-06T20:48:30Z)
• Visual Adversarial Imitation Learning using Variational Models [60.69745540036375]
  Reward function specification remains a major impediment for learning behaviors through deep reinforcement learning. Visual demonstrations of desired behaviors often presents an easier and more natural way to teach agents. We develop a variational model-based adversarial imitation learning algorithm.
  arXiv  Detail & Related papers  (2021-07-16T00:15:18Z)
• Few-Cost Salient Object Detection with Adversarial-Paced Learning [95.0220555274653]
  This paper proposes to learn the effective salient object detection model based on the manual annotation on a few training images only. We name this task as the few-cost salient object detection and propose an adversarial-paced learning (APL)-based framework to facilitate the few-cost learning scenario.
  arXiv  Detail & Related papers  (2021-04-05T14:15:49Z)
• A Framework for Efficient Robotic Manipulation [79.10407063260473]
  We show that a single robotic arm can learn sparse-reward manipulation policies from pixels. We show that, given only 10 demonstrations, a single robotic arm can learn sparse-reward manipulation policies from pixels.
  arXiv  Detail & Related papers  (2020-12-14T22:18:39Z)
• Visual Imitation Made Easy [102.36509665008732]
  We present an alternate interface for imitation that simplifies the data collection process while allowing for easy transfer to robots. We use commercially available reacher-grabber assistive tools both as a data collection device and as the robot's end-effector. We experimentally evaluate on two challenging tasks: non-prehensile pushing and prehensile stacking, with 1000 diverse demonstrations for each task.
  arXiv  Detail & Related papers  (2020-08-11T17:58:50Z)

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.