AI News Archive: July 13, 2026 — Part 16
Sourced from 500+ daily AI sources, scored by relevance.
- Stop to Decide: Latency-Aware Proprioceptive Navigation Primitives for Mapping-Free Quadruped Inspection
Compute-constrained quadrupeds often run their navigation loop far below the controller's design rate: sharing the onboard Jetson Orin with the vision pipeline slows our stair loop to about 15 Hz. This latency breaks a standard proprioceptive pattern: declaring stair-summit arrival from the body-pit...
- GeoGS-SLAM: Online Monocular Reconstruction Using Gaussian Splatting with Geometric Priors
SLAM methods based on 3D Gaussian Splatting (3DGS) have demonstrated impressive tracking and mapping performance, but typically require additional geometric information from external depth sensors. Meanwhile, recent SLAM systems that leverage geometric priors from pre-trained feed-forward models ena...
- Pix2Act: Image-Space Manipulation Policies with Equivariant Augmentation
Representing manipulation actions as 2D trajectories in the camera plane provides a compact and interpretable basis for learning complex 3D manipulation policies. However, it also creates challenges from out-of-frame trajectories and limited precision. We propose Pix2Act, an imitation learning metho...
- Comparison-Based Ordinal Learning for Proactive Driving Risk Assessment
Real-time driving risk assessment provides an essential basis for proactive safety by identifying and quantifying the danger of ongoing road interactions before adverse outcomes occur. However, due to the scarcity of collision data and frame-level risk labels, existing driving risk assessment method...
- VIA: Visual Interface Agent for Robot Control
Robot manipulation is a complex task that requires visual understanding, physical reasoning, planning, and closed-loop control. General-purpose foundation models (FMs) have grown remarkably capable of some of these, especially vision and reasoning. To leverage this for generalist robot policies, cur...
- Kohtaa
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- Desc++: Efficient Descriptor Enhancement for Data Association in Existing Visual SLAM Systems
Reliable visual data association is fundamental to visual SLAM (V-SLAM), as it directly determines the quality of the camera pose estimation and map consistency. However, the handcrafted descriptors used by most mature real-time systems degrade under illumination and viewpoint changes, while learnin...
- PAKE: Learning Whole-Body Loco-Manipulation with Partial Kinematic Embeddings
Loco-manipulation has recently shown promising capabilities; however, achieving high-precision control, managing the high-dimensional action space induced by many degrees of freedom (DoFs), and fully exploiting the inherent redundancy of whole-body systems remain challenging. In this paper, we propo...
- GraspGraphNet: Graph-Structured Multi-Embodiment Dexterous Grasp Generation
Dexterous grasp generation across robot hands is challenging because hands differ in kinematic topology, actuation dimensions, and native command spaces. We introduce GraspGraphNet, a topology-aware grasp generation framework that represents each hand as a URDF-derived kinematic graph and directly g...
- Learning to Navigate Efficiently with Only 0.58M Trainable Parameters
Recent progress in visual navigation has largely been driven by scale: end-to-end policies with hundreds of millions of parameters trained on billions of frames or large-scale simulated data. We ask how much of this scale a single task family actually requires, and what structure can substitute for ...
- SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation
Imitation learning enables robots to acquire manipulation skills from demonstrations by mapping observations to actions. Existing approaches predict either short-horizon continuous action sequences or discrete keyposes. However, continuous prediction methods suffer from compounding errors due to sho...
- Whole-Body Semantic-to-Actuation Grounding of Elephant-Inspired Soft-Trunk Motion via Lightweight Flow Matching
For close-contact human-robot interaction (HRI), trunk-like continuum manipulators provide a physical channel for diverse whole-body expression, but grounding open-vocabulary responses into such robots is difficult: end-effector motion underspecifies body shape, whereas direct whole-body commands ar...
- Wearing A Coat: Dual-Arm Robot-Assisted Dressing with Differentiable Clothing Simulation
The development of assistive robots for dressing tasks serves to augment human convenience and improve the quality of life for individuals with physical impairments. However, due to the intricate contact interactions between garments and the human limbs during dressing, most robot-assisted dressing ...
- Markov Chain Monte Carlo with Diffusion Paths
Sampling from multimodal distributions is a longstanding challenge for classical local Markov chain Monte Carlo (MCMC) methods. A popular remedy is to introduce a sequence of intermediate distributions that interpolate between the target and a simpler reference. The classical choice, tempering, rais...
- NeuroMem-FHP: A Likelihood-Free Deep Learning Framework for Parameter Estimation of Fractional Hawkes Process
In this paper, we propose deep learning based NeuroMem-FHP framework for estimating the parameters of the fractional Hawkes process (FHP), a self-exciting point process that captures long-range dependence through a fractional Mittag-Leffler excitation kernel. Two neural architectures, namely a Long ...
- Adapting Evidential Neural Networks to Test-Time Neighbor Fusion Improves Molecular Property Prediction
A trained molecular property model can be refined at test time by correcting each prediction with the measured labels of the most similar training molecules, a retraining-free procedure we call neighbor fusion; evidential neural networks make it principled by using their aleatoric and epistemic unce...
- Actor-Critic Learning for Extended Mean Field Control with Deterministic Policies
This paper develops a model-free reinforcement learning framework for continuous--time extended mean field control problems, where both the dynamics and reward may depend on the joint distribution of states and controls. We adopt deterministic feedback policies, under which the state--action distrib...
- Robust Subgroup Analysis for Heterogeneous Censored Data
Subgroup analysis is important in practice because real-world data typically come from heterogeneous populations, where meaningful patterns can differ substantially across subpopulations. Correctly identifying these subgroups can improve prediction accuracy, prevent biased or misleading conclusions,...
- Learning to control switching nonlinear systems with Koopman operator regression
In this work, we consider the identification and control of nonlinear systems with finite action spaces. The unknown dynamics are estimated from finite samples with Koopman operator regression in a reproducing kernel Hilbert space, yielding a linear switching predictive model, the switches governed ...
- Long-Memory Reservoir Computing for Data-Scarce Dengue Forecasting
Accurate dengue forecasting is crucial for public health planning, but remains challenging because incidence series are often short, noisy, non-stationary, nonlinear, and often affected by long-range temporal dependence. Fractional differencing in Autoregressive Fractionally Integrated Moving Averag...
- Trustworthy synthetic data for campaign decision support: strategy simulation fidelity and the PolicySynth framework
Decision support systems (DSS) increasingly run retention what-if analysis on synthetic customer populations, because privacy constraints preclude unrestricted use of real data. Such a system is trustworthy only if the synthetic data lead managers to the same decisions as the real data would; yet pr...
- Rank-Conditioned Sample Reuse for the Plackett--Luce Best-of-$K$ Objective
We study the coupled objective J_K^WOR = E_{S ~ PL-WOR_K}[max_{i in S} R_i]: the expected maximum reward of a size-K Plackett-Luce draw without replacement, the law of Gumbel-Top-K / Stochastic Beam Search decoding. This estimand differs from the conventional i.i.d. objective J_K^iid = E[max_{i<=K} ...
- Difference-Driven Gating: Adaptive Feature Fusion for U-Net Decoder
The U-Net style models have been widely used in many applications. A critical step in these models is to reconstruct the lower-level features using a top-down decoder. This reconstruction requires precise fusion of high-level semantics and low-level details. Existing attention-based fusion methods t...
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