Manifold-Guided Motion Planning for Tight Assemblies
- URL: http://arxiv.org/abs/2607.17898v1
- Date: Mon, 20 Jul 2026 12:46:27 GMT
- Title: Manifold-Guided Motion Planning for Tight Assemblies
- Abstract summary: Critical-Manifold Guided RRT (CMG-RRT) is a sampling-based planner designed specifically for tight assembly problems.<n>Our key observation is that in tight assemblies, valid solution paths lie on or near a critical manifold.<n>CMG-RRT guides exploration by adaptively biasing sampling toward neighborhoods of the critical manifold.
- Score: 5.967279020820772
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Motion planning for rigid-body assembly poses a fundamental challenge in robotics due to tight geometric constraints. In such scenarios, feasible motions often require passing through (near-)zero clearance configurations in which the parts are tightly constrained by contact. In this work, we introduce Critical-Manifold Guided RRT (CMG-RRT), a sampling-based planner designed specifically for tight assembly problems. Our key observation is that in tight assemblies, valid solution paths lie on or near a critical manifold: the subset of configuration space consisting of poses with at least one contact point between parts. CMG-RRT guides exploration by adaptively biasing sampling toward neighborhoods of the critical manifold using a hierarchical subdivision of the configuration space. We prove that CMG-RRT is probabilistically complete under standard clearance assumptions. Empirical evaluation on challenging rotational assembly benchmarks demonstrates a 100% success rate across all tested instances, including, to the best of our knowledge, the first fully automatic solution of the Elk disentanglement puzzle. Our open source software is available through our project page: https://www.cgl.cs.tau.ac.il/projects/tight-assembly-planning.
Related papers
- CosmoH2G: A Hand-to-Gripper Transfer Dataset and Baseline Method for Object Manipulation with Complex Spatial Movements [63.40365657068892]
Transferring human hand demonstrations to robotic grippers has recently emerged as a cost-effective solution for robot learning.<n>Motivated by this gap, we adopt an implicit, data-driven approach guided by fine-grained hand-pose motions.<n>In both simulation and real-robot experiments, our framework enables stable and precise hand-to-gripper transfer of complex spatial manipulations.
arXiv Detail & Related papers (2026-09-07T13:47:22Z) - Coupled Routing and Configuration Optimization for Multi-Viewpoint Robotic Inspection [4.164290233214403]
We present a unified framework that turns a set of 6-DoF inspection viewpoints into a time-optimal, collision-free route for a 9-DoF robotic system.<n>Unlike modular pipelines that fix a single inverse-kinematics (IK) configuration per viewpoint, build an all-pairs travel-time map, and then route, our method jointly optimize the visiting order and the per-viewpoint configuration in a single global search.
arXiv Detail & Related papers (2026-06-17T17:14:54Z) - Connectivity-Aware Representations for Constrained Motion Planning via Multi-Scale Contrastive Learning [6.0393663164774125]
The objective of constrained motion planning is to connect start and goal configurations while satisfying task-specific constraints.<n>This paper addresses these challenges by learning a connectivity-aware representation for selecting start and goal configurations prior to planning.
arXiv Detail & Related papers (2026-03-26T10:44:21Z) - LiveClawBench: Benchmarking LLM Agents on Complex, Real-World Assistant Tasks [58.3639630490749]
We introduce LiveClawBench, a benchmark to evaluate LLM agents on real-world assistant tasks.<n>Based on an analysis of various realClaw usage cases, we derive a Triple-Axis Complexity Framework.<n>We construct a pilot benchmark with explicit complexity-factor annotations, covering real-world assistant tasks.
arXiv Detail & Related papers (2026-03-20T16:08:21Z) - TangramPuzzle: Evaluating Multimodal Large Language Models with Compositional Spatial Reasoning [104.66714520975837]
We introduce a geometry-grounded benchmark designed to evaluate compositional spatial reasoning through the lens of the classic Tangram game.<n>We propose the Tangram Construction Expression (TCE), a symbolic geometric framework that grounds tangram assemblies in exact, machine-verifiable coordinate specifications.<n>We conduct extensive evaluation experiments on advanced open-source and proprietary models, revealing an interesting insight: MLLMs tend to prioritize matching the target silhouette while neglecting geometric constraints.
arXiv Detail & Related papers (2026-01-23T07:35:05Z) - Towards Zero-Shot Point Cloud Registration Across Diverse Scales, Scenes, and Sensor Setups [41.033896572767496]
We present BUFFER-X, a training-free registration framework that achieves zero-shot generalization.<n>Our approach employs hierarchical multi-scale matching to extract correspondences across local, middle, and global receptive fields.<n>For efficiency-critical applications, we introduce BUFFER-X-Lite, which reduces total time by 43%.
arXiv Detail & Related papers (2026-01-06T06:51:24Z) - Benchmarking LLMs' Swarm intelligence [51.648605206159125]
Large Language Models (LLMs) show potential for complex reasoning, yet their capacity for emergent coordination in Multi-Agent Systems (MAS) remains largely unexplored.<n>We introduce SwarmBench, a novel benchmark designed to systematically evaluate tasks of LLMs acting as decentralized agents.<n>We propose metrics for coordination effectiveness and analyze emergent group dynamics.
arXiv Detail & Related papers (2025-05-07T12:32:01Z) - Reinforcement learning with combinatorial actions for coupled restless bandits [62.89013331120493]
We propose SEQUOIA, an RL algorithm that directly optimize for long-term reward over the feasible action space.<n>We empirically validate SEQUOIA on four novel restless bandit problems with constraints: multiple interventions, path constraints, bipartite matching, and capacity constraints.
arXiv Detail & Related papers (2025-03-01T21:25:21Z) - Disentangled Representation Learning with the Gromov-Monge Gap [65.73194652234848]
Learning disentangled representations from unlabelled data is a fundamental challenge in machine learning.<n>We introduce a novel approach to disentangled representation learning based on quadratic optimal transport.<n>We demonstrate the effectiveness of our approach for quantifying disentanglement across four standard benchmarks.
arXiv Detail & Related papers (2024-07-10T16:51:32Z) - Temporal Perceiver: A General Architecture for Arbitrary Boundary
Detection [48.33132632418303]
Generic Boundary Detection (GBD) aims at locating general boundaries that divide videos into semantically coherent and taxonomy-free units.
Previous research separately handle these different-level generic boundaries with specific designs of complicated deep networks from simple CNN to LSTM.
We present Temporal Perceiver, a general architecture with Transformers, offering a unified solution to the detection of arbitrary generic boundaries.
arXiv Detail & Related papers (2022-03-01T09:31:30Z)
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.