arXiv AI
Aug 11
The Field Knows: Cross-Dimensional Geometry from Navigation to Black Holes
arXiv:2608. 07566v1 Announce Type: new Abstract: We introduce a continuous metric field framework trained by a single causal contrastive loss.
Space Is Intelligence: Neural Semigroup Superposition for Riemannian Metric Generation
arXiv:2606. 18828v1 Announce Type: cross Abstract: Traditional approaches place intelligence in the agent, whether as a learned policy or a search procedure.
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arXiv Machine Learning
Jun 3
Neural Navigation Functions for Zero-Shot Generalizable Motion Planning
arXiv:2606. 03756v1 Announce Type: cross Abstract: We introduce Neural Navigation Functions (Neural-NF), a learned reactive navigation function capable of zero-shot transfer across unseen environment geometries.
arXiv AI
Jun 9
Riemannian-Manifold Steering: Geometry-Aware Generative Autoencoders for Label-Free Steering
arXiv:2605. 24942v2 Announce Type: replace-cross Abstract: Steering a language model - intervening on its internal activations to change downstream behaviour - has recently expanded beyond linear interpolation to nonlinear methods such as angular and kernelized steering, which define intervention transformations without learning an explicit geometry over paths in activation space.
arXiv Machine Learning
Sep 11
ObstaDiff: Generalizable Diffusion Policy Learning via Obstacle-aware Representations
ObstaDiff is a diffusion-policy framework that introduces a lightweight obstacle-aware visual encoder to generate structured representations of targets, obstacles, and background. By aligning these representations, the policy produces end-effector trajectories that focus on a target-centered bottleneck pose while accounting for surrounding obstacles. In real-robot greenhouse trials, ObstaDiff achieved a 75.41% task success rate and an 8.20% obstacle collision rate, outperforming existing imitation-learning baselines in cluttered agricultural settings.
arXiv Machine Learning
1d ago
Training-Free Diffusion Planning with Analytical Local Scores
The paper presents a training-free diffusion-based motion planner that replaces learned global trajectory scores with analytical local scores derived from obstacle, smoothness, velocity, and inter-agent feasibility terms. By reconstructing trajectory scores through local interactions between neighboring waypoints and nearby constraints, the method decomposes the denoising process while preserving the optimization structure of classical trajectory methods. Experiments demonstrate that this approach generates smooth, feasible trajectories for large multi-agent tasks in complex environments quickly, outperforming learning-based and optimization baselines without requiring training data.
Hugging Face Trending Papers
Sep 21
SE(3) Neural Potential Fields for 6-DoF Trajectory Planning Directly from Images Without Explicit 3D Reconstruction
Reaching a 6-DoF grasp pose in clutter requires a collision-free trajectory, conventionally obtained by reconstructing the scene in 3D and planning inside that reconstruction, at the cost of its accur...
arXiv AI
Sep 23
SE(3) Neural Potential Fields for 6-DoF Trajectory Planning Directly from Images Without Explicit 3D Reconstruction
The paper introduces SE(3) neural potential fields that learn collision‑free 6‑DoF trajectory planning directly from posed RGB images, eliminating the need for explicit 3D reconstruction. By supervising the field with a navigation function based on geodesic distances to the grasp, the method avoids the classic pitfalls of artificial potential fields, achieving near‑goal convergence within 3 cm from any start and producing collision‑free paths on a UR10 robot. Experiments on two tabletop scenes show significant improvements in clearance, reduced arm‑link contacts, and a 90 % grasp success rate, while planning time drops from over a minute to about 2 seconds compared to RRT* on a reconstructed scene.
arXiv Computer Vision
Sep 16
EgoPathBench: Evaluating Zero-Shot Egocentric Waypoint Decision-Making in Vision-Language Models
EgoPathBench is a new dataset and benchmark that tests zero‑shot egocentric waypoint decision‑making in vision‑language models. Each task presents an egocentric RGB image, a natural‑language goal, and numbered visible waypoints, and models must return traversable candidates or an ordered route. The benchmark evaluates candidate feasibility, edge legality, and goal arrival under point‑agent or embodied geometry, covering 31,852 training, 1,345 validation, and 1,111 benchmark questions. "whyItMatters":"The benchmark reveals that current VLMs perform poorly on integrated navigation tasks, highlighting a gap in spatial intelligence that can be addressed by fine‑tuning with the released training data."
arXiv Machine Learning
Jun 18
Riemannian MeanFlow for One-Step Generation on Manifolds
arXiv:2603. 10718v3 Announce Type: replace Abstract: Flow Matching enables simulation-free training of generative models on Riemannian manifolds, yet sampling typically still relies on numerically integrating a probability-flow ODE.
arXiv AI
4d ago
Building Transformation Layers for Riemannian Neural Networks
arXiv:2609.35436v2 Announce Type: replace Abstract: Recently, deep neural networks on manifold-valued representations have garnered significant attention across various machine learning applications....
arXiv Machine Learning
Jul 30
Equivariant Eikonal Neural Networks: Grid-Free, Scalable Travel-Time Prediction on Homogeneous Spaces
arXiv:2505. 16035v3 Announce Type: replace Abstract: We introduce Equivariant Neural Eikonal Solvers, a novel framework that integrates Equivariant Neural Fields (ENFs) with Neural Eikonal Solvers.
arXiv Machine Learning
Jun 17
Can Vision Foundation Models Navigate? Zero-Shot Real-World Evaluation and Lessons Learned
arXiv:2603. 25937v2 Announce Type: replace-cross Abstract: Visual Navigation Models (VNMs) promise generalizable, robot navigation by learning from large-scale visual demonstrations.