End-to-end Reinforcement Learning of Robotic Manipulation with Robust Keypoints Representation

• URL: http://arxiv.org/abs/2202.06027v1
• Date: Sat, 12 Feb 2022 09:58:09 GMT
• Title: End-to-end Reinforcement Learning of Robotic Manipulation with Robust Keypoints Representation
• Authors: Tianying Wang, En Yen Puang, Marcus Lee, Yan Wu, Wei Jing
• Abstract summary: We present an end-to-end Reinforcement Learning framework for robotic manipulation tasks, using a robust and efficient keypoints representation. The proposed method learns keypoints from camera images as the state representation, through a self-supervised autoencoder architecture. We demonstrate the effectiveness of the proposed method on robotic manipulation tasks including grasping and pushing, in different scenarios.
• Score: 7.374994747693731
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present an end-to-end Reinforcement Learning(RL) framework for robotic manipulation tasks, using a robust and efficient keypoints representation. The proposed method learns keypoints from camera images as the state representation, through a self-supervised autoencoder architecture. The keypoints encode the geometric information, as well as the relationship of the tool and target in a compact representation to ensure efficient and robust learning. After keypoints learning, the RL step then learns the robot motion from the extracted keypoints state representation. The keypoints and RL learning processes are entirely done in the simulated environment. We demonstrate the effectiveness of the proposed method on robotic manipulation tasks including grasping and pushing, in different scenarios. We also investigate the generalization capability of the trained model. In addition to the robust keypoints representation, we further apply domain randomization and adversarial training examples to achieve zero-shot sim-to-real transfer in real-world robotic manipulation tasks.

Related papers

• Affordance-Guided Reinforcement Learning via Visual Prompting [51.361977466993345]
  We study rewards shaped by vision-language models (VLMs) to define dense rewards for robotic learning. On a real-world manipulation task specified by natural language description, we find that these rewards improve the sample efficiency of autonomous RL.
  arXiv  Detail & Related papers  (2024-07-14T21:41:29Z)
• Learning Manipulation by Predicting Interaction [85.57297574510507]
  We propose a general pre-training pipeline that learns Manipulation by Predicting the Interaction. The experimental results demonstrate that MPI exhibits remarkable improvement by 10% to 64% compared with previous state-of-the-art in real-world robot platforms.
  arXiv  Detail & Related papers  (2024-06-01T13:28:31Z)
• MOKA: Open-Vocabulary Robotic Manipulation through Mark-Based Visual Prompting [106.53784213239479]
  We present MOKA (Marking Open-vocabulary Keypoint Affordances), an approach that employs vision language models to solve robotic manipulation tasks. At the heart of our approach is a compact point-based representation of affordance and motion that bridges the VLM's predictions on RGB images and the robot's motions in the physical world. We evaluate and analyze MOKA's performance on a variety of manipulation tasks specified by free-form language descriptions.
  arXiv  Detail & Related papers  (2024-03-05T18:08:45Z)
• Human-oriented Representation Learning for Robotic Manipulation [64.59499047836637]
  Humans inherently possess generalizable visual representations that empower them to efficiently explore and interact with the environments in manipulation tasks. We formalize this idea through the lens of human-oriented multi-task fine-tuning on top of pre-trained visual encoders. Our Task Fusion Decoder consistently improves the representation of three state-of-the-art visual encoders for downstream manipulation policy-learning.
  arXiv  Detail & Related papers  (2023-10-04T17:59:38Z)
• Active Exploration for Robotic Manipulation [40.39182660794481]
  This paper proposes a model-based active exploration approach that enables efficient learning in sparse-reward robotic manipulation tasks. We evaluate our proposed algorithm in simulation and on a real robot, trained from scratch with our method.
  arXiv  Detail & Related papers  (2022-10-23T18:07:51Z)
• Masked World Models for Visual Control [90.13638482124567]
  We introduce a visual model-based RL framework that decouples visual representation learning and dynamics learning. We demonstrate that our approach achieves state-of-the-art performance on a variety of visual robotic tasks.
  arXiv  Detail & Related papers  (2022-06-28T18:42:27Z)
• Self-Supervised Learning of Multi-Object Keypoints for Robotic Manipulation [8.939008609565368]
  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.
  arXiv  Detail & Related papers  (2022-05-17T13:15:07Z)
• 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)
• Pose Estimation for Robot Manipulators via Keypoint Optimization and Sim-to-Real Transfer [10.369766652751169]
  Keypoint detection is an essential building block for many robotic applications. Deep learning methods have the ability to detect user-defined keypoints in a marker-less manner. We propose a new and autonomous way to define the keypoint locations that overcomes these challenges.
  arXiv  Detail & Related papers  (2020-10-15T22:38:37Z)

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.