Motion Planning on Visual Manifolds

• URL: http://arxiv.org/abs/2210.04047v2
• Date: Mon, 19 Feb 2024 06:46:22 GMT
• Title: Motion Planning on Visual Manifolds
• Authors: M Seetha Ramaiah
• Abstract summary: We propose an alternative characterization of the notion of Configuration Space, which we call Visual Configuration Space (VCS) This new characterization allows an embodied agent (e.g., a robot) to discover its own body structure and plan obstacle-free motions in its peripersonal space using a set of its own images in random poses. We demonstrate the usefulness of VCS in (a) building and working with geometry-free models for robot motion planning, (b) explaining how a human baby might learn to reach objects in its peripersonal space through motor babbling, and (c) automatically generating natural looking head
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this thesis, we propose an alternative characterization of the notion of Configuration Space, which we call Visual Configuration Space (VCS). This new characterization allows an embodied agent (e.g., a robot) to discover its own body structure and plan obstacle-free motions in its peripersonal space using a set of its own images in random poses. Here, we do not assume any knowledge of geometry of the agent, obstacles or the environment. We demonstrate the usefulness of VCS in (a) building and working with geometry-free models for robot motion planning, (b) explaining how a human baby might learn to reach objects in its peripersonal space through motor babbling, and (c) automatically generating natural looking head motion animations for digital avatars in virtual environments. This work is based on the formalism of manifolds and manifold learning using the agent's images and hence we call it Motion Planning on Visual Manifolds.

Related papers

• AvatarGO: Zero-shot 4D Human-Object Interaction Generation and Animation [60.5897687447003]
  AvatarGO is a novel framework designed to generate realistic 4D HOI scenes from textual inputs. Our framework not only generates coherent compositional motions, but also exhibits greater robustness in handling issues. As the first attempt to synthesize 4D avatars with object interactions, we hope AvatarGO could open new doors for human-centric 4D content creation.
  arXiv  Detail & Related papers  (2024-10-09T17:58:56Z)
• PlaMo: Plan and Move in Rich 3D Physical Environments [68.75982381673869]
  We present PlaMo, a scene-aware path planner and a robust physics-based controller. The planner produces a sequence of motion paths, considering the various limitations the scene imposes on the motion. Our control policy generates rich and realistic physical motion adhering to the plan.
  arXiv  Detail & Related papers  (2024-06-26T10:41:07Z)
• AgentAvatar: Disentangling Planning, Driving and Rendering for Photorealistic Avatar Agents [16.544688997764293]
  Our framework harnesses LLMs to produce a series of detailed text descriptions of the avatar agents' facial motions. These descriptions are processed by our task-agnostic driving engine into continuous motion embeddings. Our framework adapts to a variety of non-verbal avatar interactions, both monadic and dyadic.
  arXiv  Detail & Related papers  (2023-11-29T09:13:00Z)
• Universal Humanoid Motion Representations for Physics-Based Control [71.46142106079292]
  We present a universal motion representation that encompasses a comprehensive range of motor skills for physics-based humanoid control. We first learn a motion imitator that can imitate all of human motion from a large, unstructured motion dataset. We then create our motion representation by distilling skills directly from the imitator.
  arXiv  Detail & Related papers  (2023-10-06T20:48:43Z)
• High-Degrees-of-Freedom Dynamic Neural Fields for Robot Self-Modeling and Motion Planning [6.229216953398305]
  A robot self-model is a representation of the robot's physical morphology that can be used for motion planning tasks. We propose a new encoder-based neural density field architecture for dynamic object-centric scenes conditioned on high numbers of degrees of freedom. In a 7-DOF robot test setup, the learned self-model achieves a Chamfer-L2 distance of 2% of the robot's dimension workspace.
  arXiv  Detail & Related papers  (2023-10-05T16:01:29Z)
• AvatarGen: a 3D Generative Model for Animatable Human Avatars [108.11137221845352]
  AvatarGen is the first method that enables not only non-rigid human generation with diverse appearance but also full control over poses and viewpoints. To model non-rigid dynamics, it introduces a deformation network to learn pose-dependent deformations in the canonical space. Our method can generate animatable human avatars with high-quality appearance and geometry modeling, significantly outperforming previous 3D GANs.
  arXiv  Detail & Related papers  (2022-08-01T01:27:02Z)
• 3D Neural Scene Representations for Visuomotor Control [78.79583457239836]
  We learn models for dynamic 3D scenes purely from 2D visual observations. A dynamics model, constructed over the learned representation space, enables visuomotor control for challenging manipulation tasks.
  arXiv  Detail & Related papers  (2021-07-08T17:49:37Z)
• Task-Generic Hierarchical Human Motion Prior using VAEs [44.356707509079044]
  A deep generative model that describes human motions can benefit a wide range of fundamental computer vision and graphics tasks. We present a method for learning complex human motions independent of specific tasks using a combined global and local latent space. We demonstrate the effectiveness of our hierarchical motion variational autoencoder in a variety of tasks including video-based human pose estimation.
  arXiv  Detail & Related papers  (2021-06-07T23:11:42Z)
• Latent Space Roadmap for Visual Action Planning of Deformable and Rigid Object Manipulation [74.88956115580388]
  Planning is performed in a low-dimensional latent state space that embeds images. Our framework consists of two main components: a Visual Foresight Module (VFM) that generates a visual plan as a sequence of images, and an Action Proposal Network (APN) that predicts the actions between them.
  arXiv  Detail & Related papers  (2020-03-19T18:43:26Z)
• 3D Dynamic Scene Graphs: Actionable Spatial Perception with Places, Objects, and Humans [27.747241700017728]
  We present a unified representation for actionable spatial perception: 3D Dynamic Scene Graphs. 3D Dynamic Scene Graphs can have a profound impact on planning and decision-making, human-robot interaction, long-term autonomy, and scene prediction.
  arXiv  Detail & Related papers  (2020-02-15T00:46:32Z)

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.