Scalable and Probabilistically Complete Planning for Robotic Spatial Extrusion

• URL: http://arxiv.org/abs/2002.02360v1
• Date: Thu, 6 Feb 2020 17:05:55 GMT
• Title: Scalable and Probabilistically Complete Planning for Robotic Spatial Extrusion
• Abstract summary: We present a rigorous formalization of robotic spatial extrusion planning. We show that, although these constraints often conflict with each other, a greedy backward state-space search guided by a stiffness-aware is able to successfully balance both constraints.
• Score: 0.755972004983746
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: There is increasing demand for automated systems that can fabricate 3D structures. Robotic spatial extrusion has become an attractive alternative to traditional layer-based 3D printing due to a manipulator's flexibility to print large, directionally-dependent structures. However, existing extrusion planning algorithms require a substantial amount of human input, do not scale to large instances, and lack theoretical guarantees. In this work, we present a rigorous formalization of robotic spatial extrusion planning and provide several efficient and probabilistically complete planning algorithms. The key planning challenge is, throughout the printing process, satisfying both stiffness constraints that limit the deformation of the structure and geometric constraints that ensure the robot does not collide with the structure. We show that, although these constraints often conflict with each other, a greedy backward state-space search guided by a stiffness-aware heuristic is able to successfully balance both constraints. We empirically compare our methods on a benchmark of over 40 simulated extrusion problems. Finally, we apply our approach to 3 real-world extrusion problems.

Related papers

• Design Optimization for Large High-Force Soft Robot Manipulators Under Gravitational Loads [0.037475012732344194]
  This manuscript introduces a method for optimizing the geometry of a soft robot limb.<n>We demonstrate that an explicit solution exists to the proposed optimization problem under certain assumptions.<n> Experiments with three geometries of a large, soft, pneumatically-actuated manipulator demonstrate that the method correctly predicts which designs meet constraints.
  arXiv  Detail & Related papers  (2026-08-17T15:42:34Z)
• Reinforcement learning in linear embedding space unlocks generalizable control across soft robot configurations [60.42735420750131]
  Soft-bodied organisms such as octopuses and elephant trunks exhibit remarkable morphological adaptability.<n>Various soft robots have emerged in recent decades, featuring diverse materials, stiffnesses, and morphologies tailored to specific tasks.<n>Here, we introduce a generalizable control system that enables rapid adaptation across diverse soft robot configurations.
  arXiv  Detail & Related papers  (2026-06-06T11:09:46Z)
• Vectorizing Projection in Manifold-Constrained Motion Planning for Real-Time Whole-Body Control [2.940492773889387]
  We present a CPU SIMD-accelerated manifold-constrained motion planner that revisits projection-based constraint satisfaction through the lens of parallelization.<n>Our approach achieves up to 100-1000x speed-ups over the state-of-the-art, making real-time constrained motion planning feasible for the first time.
  arXiv  Detail & Related papers  (2026-04-14T22:00:53Z)
• Generalizable Geometric Prior and Recurrent Spiking Feature Learning for Humanoid Robot Manipulation [90.90219129619344]
  This paper presents a novel R-prior-S, Recurrent Geometric-priormodal Policy with Spiking features.<n>To ground high-level reasoning in physical reality, we leverage lightweight 2D geometric inductive biases.<n>For the data efficiency issue in robotic action generation, we introduce a Recursive Adaptive Spiking Network.
  arXiv  Detail & Related papers  (2026-01-13T23:36:30Z)
• OmniEVA: Embodied Versatile Planner via Task-Adaptive 3D-Grounded and Embodiment-aware Reasoning [50.45036742963495]
  We introduce OmniEVA, an embodied versatile planner that enables advanced embodied reasoning and task planning.<n>A Task-Adaptive 3D Grounding mechanism enables context-aware 3D grounding for diverse embodied tasks.<n>An Embodiment-Aware Reasoning framework incorporates task goals and embodiment constraints into the reasoning loop, resulting in planning decisions that are both goal-directed and executable.
  arXiv  Detail & Related papers  (2025-09-11T10:32:22Z)
• Autoencoder-based non-intrusive model order reduction in continuum mechanics [0.0]
  We propose a non-intrusive, Autoencoder-based framework for reduced-order modeling in continuum mechanics.<n>Our method integrates three stages: (i) an unsupervised Autoencoder compresses high-dimensional finite element solutions into a compact latent space, (ii) a supervised regression network maps problem parameters to latent codes, and (iii) an end-to-end surrogate reconstructs full-field solutions directly from input parameters.
  arXiv  Detail & Related papers  (2025-09-02T12:05:00Z)
• Discrete-Guided Diffusion for Scalable and Safe Multi-Robot Motion Planning [56.240199425429445]
  Multi-Robot Motion Planning (MPMP) involves generating trajectories for multiple robots operating in a shared continuous workspace.<n>While discrete multi-agent finding (MAPF) methods are broadly adopted due to their scalability, their coarse discretization trajectory quality.<n>This paper tackles limitations of two approaches by introducing discrete MAPF solvers with constrained generative diffusion models.
  arXiv  Detail & Related papers  (2025-08-27T17:59:36Z)
• RoomCraft: Controllable and Complete 3D Indoor Scene Generation [51.19602078504066]
  RoomCraft is a multi-stage pipeline that converts real images, sketches, or text descriptions into coherent 3D indoor scenes.<n>Our approach combines a scene generation pipeline with a constraint-driven optimization framework.<n>RoomCraft significantly outperforms existing methods in generating realistic, semantically coherent, and visually appealing room layouts.
  arXiv  Detail & Related papers  (2025-06-27T15:03:17Z)
• Constrained Stein Variational Gradient Descent for Robot Perception, Planning, and Identification [11.126853736828984]
  We present two novel frameworks for applying principles of constrained optimization to the new variational inference Stein variational descent.<n>We show that we can build distributions of: robot motion plans that exactly avoid collisions, robot arm joint angles on the SE(3) manifold with exact table placement constraints, and object poses from point clouds with table placement constraints.
  arXiv  Detail & Related papers  (2025-05-31T14:52:34Z)
• Regularized interpolation in 4D neural fields enables optimization of 3D printed geometries [0.0]
  3D printing is marked by exceptional design freedom and complexity. It is also prone to geometric and other defects that must be resolved for it to reach its full potential.
  arXiv  Detail & Related papers  (2025-02-03T16:50:57Z)
• Flatten Anything: Unsupervised Neural Surface Parameterization [76.4422287292541]
  We introduce the Flatten Anything Model (FAM), an unsupervised neural architecture to achieve global free-boundary surface parameterization. Compared with previous methods, our FAM directly operates on discrete surface points without utilizing connectivity information. Our FAM is fully-automated without the need for pre-cutting and can deal with highly-complex topologies.
  arXiv  Detail & Related papers  (2024-05-23T14:39:52Z)
• RoboScript: Code Generation for Free-Form Manipulation Tasks across Real and Simulation [77.41969287400977]
  This paper presents textbfRobotScript, a platform for a deployable robot manipulation pipeline powered by code generation. We also present a benchmark for a code generation benchmark for robot manipulation tasks in free-form natural language. We demonstrate the adaptability of our code generation framework across multiple robot embodiments, including the Franka and UR5 robot arms.
  arXiv  Detail & Related papers  (2024-02-22T15:12:00Z)
• Fast Kinodynamic Planning on the Constraint Manifold with Deep Neural Networks [29.239926645660823]
  This paper introduces a novel learning-to-plan framework that exploits the concept of constraint manifold. Our approach generates plans satisfying an arbitrary set of constraints and computes them in a short constant time, namely the inference time of a neural network. We validate our approach on two simulated tasks and in a demanding real-world scenario, where we use a Kuka LBR Iiwa 14 robotic arm to perform the hitting movement in robotic Air Hockey.
  arXiv  Detail & Related papers  (2023-01-11T06:54:11Z)
• Planning with Spatial-Temporal Abstraction from Point Clouds for Deformable Object Manipulation [64.00292856805865]
  We propose PlAnning with Spatial-Temporal Abstraction (PASTA), which incorporates both spatial abstraction and temporal abstraction. Our framework maps high-dimension 3D observations into a set of latent vectors and plans over skill sequences on top of the latent set representation. We show that our method can effectively perform challenging deformable object manipulation tasks in the real world.
  arXiv  Detail & Related papers  (2022-10-27T19:57:04Z)
• Graph-based Reinforcement Learning meets Mixed Integer Programs: An application to 3D robot assembly discovery [34.25379651790627]
  We tackle the problem of building arbitrary, predefined target structures entirely from scratch using a set of Tetris-like building blocks and a robotic manipulator. Our novel hierarchical approach aims at efficiently decomposing the overall task into three feasible levels that benefit mutually from each other.
  arXiv  Detail & Related papers  (2022-03-08T14:44:51Z)
• Large Scale Distributed Collaborative Unlabeled Motion Planning with Graph Policy Gradients [122.85280150421175]
  We present a learning method to solve the unlabelled motion problem with motion constraints and space constraints in 2D space for a large number of robots. We employ a graph neural network (GNN) to parameterize policies for the robots.
  arXiv  Detail & Related papers  (2021-02-11T21:57:43Z)
• Learning Constrained Distributions of Robot Configurations with Generative Adversarial Network [15.962033896896385]
  In high dimensional robotic system, the manifold of the valid configuration space often has a complex shape. We propose a generative adversarial network approach to learn the distribution of valid robot configurations under such constraints.
  arXiv  Detail & Related papers  (2020-11-11T11:43:54Z)
• First Steps: Latent-Space Control with Semantic Constraints for Quadruped Locomotion [73.37945453998134]
  Traditional approaches to quadruped control employ simplified, hand-derived models. This significantly reduces the capability of the robot since its effective kinematic range is curtailed. In this work, these challenges are addressed by framing quadruped control as optimisation in a structured latent space. A deep generative model captures a statistical representation of feasible joint configurations, whilst complex dynamic and terminal constraints are expressed via high-level, semantic indicators. We validate the feasibility of locomotion trajectories optimised using our approach both in simulation and on a real-worldmal quadruped.
  arXiv  Detail & Related papers  (2020-07-03T07:04:18Z)
• An Integer Linear Programming Framework for Mining Constraints from Data [81.60135973848125]
  We present a general framework for mining constraints from data. In particular, we consider the inference in structured output prediction as an integer linear programming (ILP) problem. We show that our approach can learn to solve 9x9 Sudoku puzzles and minimal spanning tree problems from examples without providing the underlying rules.
  arXiv  Detail & Related papers  (2020-06-18T20:09:53Z)
• Combinatorics of a Discrete Trajectory Space for Robot Motion Planning [4.477410849696538]
  complexity of the problem is directly related to the dimension of the robot's configuration space. We present a discrete robot model based on the fundamental hardware specifications of a robot.
  arXiv  Detail & Related papers  (2020-05-25T12:14:20Z)
• Diversity-based Design Assist for Large Legged Robots [4.505477982701834]
  We explore the design space of a class of large legged robots, which stand at around 2m tall and whose design and construction is not well-studied. A novel robot encoding allows for bio-inspired features such as legs scaling along the length of the body.
  arXiv  Detail & Related papers  (2020-04-17T03:59:17Z)

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.