Part-Guided 3D RL for Sim2Real Articulated Object Manipulation

• URL: http://arxiv.org/abs/2404.17302v1
• Date: Fri, 26 Apr 2024 10:18:17 GMT
• Title: Part-Guided 3D RL for Sim2Real Articulated Object Manipulation
• Authors: Pengwei Xie, Rui Chen, Siang Chen, Yuzhe Qin, Fanbo Xiang, Tianyu Sun, Jing Xu, Guijin Wang, Hao Su,
• Abstract summary: We propose a part-guided 3D RL framework, which can learn to manipulate articulated objects without demonstrations. We combine the strengths of 2D segmentation and 3D RL to improve the efficiency of RL policy training. A single versatile RL policy can be trained on multiple articulated object manipulation tasks simultaneously in simulation.
• Score: 27.422878372169805
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Manipulating unseen articulated objects through visual feedback is a critical but challenging task for real robots. Existing learning-based solutions mainly focus on visual affordance learning or other pre-trained visual models to guide manipulation policies, which face challenges for novel instances in real-world scenarios. In this paper, we propose a novel part-guided 3D RL framework, which can learn to manipulate articulated objects without demonstrations. We combine the strengths of 2D segmentation and 3D RL to improve the efficiency of RL policy training. To improve the stability of the policy on real robots, we design a Frame-consistent Uncertainty-aware Sampling (FUS) strategy to get a condensed and hierarchical 3D representation. In addition, a single versatile RL policy can be trained on multiple articulated object manipulation tasks simultaneously in simulation and shows great generalizability to novel categories and instances. Experimental results demonstrate the effectiveness of our framework in both simulation and real-world settings. Our code is available at https://github.com/THU-VCLab/Part-Guided-3D-RL-for-Sim2Real-Articulated-Object-Manipulation.

Related papers

• Empowering Embodied Visual Tracking with Visual Foundation Models and Offline RL [19.757030674041037]
  Embodied visual tracking is a vital and challenging skill for embodied agents. Existing methods suffer from inefficient training and poor generalization. We propose a novel framework that combines visual foundation models and offline reinforcement learning.
  arXiv  Detail & Related papers  (2024-04-15T15:12:53Z)
• SUGAR: Pre-training 3D Visual Representations for Robotics [85.55534363501131]
  We introduce a novel 3D pre-training framework for robotics named SUGAR. SUGAR captures semantic, geometric and affordance properties of objects through 3D point clouds. We show that SUGAR's 3D representation outperforms state-of-the-art 2D and 3D representations.
  arXiv  Detail & Related papers  (2024-04-01T21:23:03Z)
• PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm [114.47216525866435]
  We introduce a novel universal 3D pre-training framework designed to facilitate the acquisition of efficient 3D representation. For the first time, PonderV2 achieves state-of-the-art performance on 11 indoor and outdoor benchmarks, implying its effectiveness.
  arXiv  Detail & Related papers  (2023-10-12T17:59:57Z)
• CLIP$^2$: Contrastive Language-Image-Point Pretraining from Real-World Point Cloud Data [80.42480679542697]
  We propose Contrastive Language-Image-Point Cloud Pretraining (CLIP$2$) to learn the transferable 3D point cloud representation in realistic scenarios. Specifically, we exploit naturally-existed correspondences in 2D and 3D scenarios, and build well-aligned and instance-based text-image-point proxies from those complex scenarios.
  arXiv  Detail & Related papers  (2023-03-22T09:32:45Z)
• SAM-RL: Sensing-Aware Model-Based Reinforcement Learning via Differentiable Physics-Based Simulation and Rendering [49.78647219715034]
  We propose a sensing-aware model-based reinforcement learning system called SAM-RL. With the sensing-aware learning pipeline, SAM-RL allows a robot to select an informative viewpoint to monitor the task process. We apply our framework to real world experiments for accomplishing three manipulation tasks: robotic assembly, tool manipulation, and deformable object manipulation.
  arXiv  Detail & Related papers  (2022-10-27T05:30:43Z)
• DeXtreme: Transfer of Agile In-hand Manipulation from Simulation to Reality [64.51295032956118]
  We train a policy that can perform robust dexterous manipulation on an anthropomorphic robot hand. Our work reaffirms the possibilities of sim-to-real transfer for dexterous manipulation in diverse kinds of hardware and simulator setups.
  arXiv  Detail & Related papers  (2022-10-25T01:51:36Z)
• Visual Reinforcement Learning with Self-Supervised 3D Representations [15.991546692872841]
  We present a unified framework for self-supervised learning of 3D representations for motor control. Our method enjoys improved sample efficiency in simulated manipulation tasks compared to 2D representation learning methods.
  arXiv  Detail & Related papers  (2022-10-13T17:59:55Z)
• Unsupervised Learning of Efficient Geometry-Aware Neural Articulated Representations [89.1388369229542]
  We propose an unsupervised method for 3D geometry-aware representation learning of articulated objects. We obviate this need by learning the representations with GAN training. Experiments demonstrate the efficiency of our method and show that GAN-based training enables learning of controllable 3D representations without supervision.
  arXiv  Detail & Related papers  (2022-04-19T12:10:18Z)
• Learning Generalizable Dexterous Manipulation from Human Grasp Affordance [11.060931225148936]
  Dexterous manipulation with a multi-finger hand is one of the most challenging problems in robotics. Recent progress in imitation learning has largely improved the sample efficiency compared to Reinforcement Learning. We propose to learn dexterous manipulation using large-scale demonstrations with diverse 3D objects in a category.
  arXiv  Detail & Related papers  (2022-04-05T16:26:22Z)
• De-rendering 3D Objects in the Wild [21.16153549406485]
  We present a weakly supervised method that is able to decompose a single image of an object into shape. For training, the method only relies on a rough initial shape estimate of the training objects to bootstrap the learning process. In our experiments, we show that the method can successfully de-render 2D images into a 3D representation and generalizes to unseen object categories.
  arXiv  Detail & Related papers  (2022-01-06T23:50:09Z)
• Hindsight for Foresight: Unsupervised Structured Dynamics Models from Physical Interaction [24.72947291987545]
  Key challenge for an agent learning to interact with the world is to reason about physical properties of objects. We propose a novel approach for modeling the dynamics of a robot's interactions directly from unlabeled 3D point clouds and images.
  arXiv  Detail & Related papers  (2020-08-02T11:04:49Z)

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.