Skip to content
Agientry
← Tags

#Robotics (15)

WaterfallPagination
Lumos NexCore: End-to-End Robot Skill Platform
0:56

Lumos NexCore: End-to-End Robot Skill Platform

@LumosRobotics · 127 views · 2026-09-15
Robot SkillsRobot LearningRobot foundation models
Inverse Kinematics and Foot Locking: From Two-Bone IK to Offline Slide Removal

Inverse Kinematics and Foot Locking: From Two-Bone IK to Offline Slide Removal

A practical recipe for foot sliding: solve the leg with soft-clamped two-bone IK and toe orientation, lock the toe target during contacts with inertialization, annotate contacts automatically, then use a position-based dynamics-style offline pass to distribute corrections. The key insight is velocity preservation, not sticking to the ground, and the toe—not the heel—is what should be locked.

BLOG

Inverse KinematicsFoot LockingAnimationMotion MatchingCharacter Animation
LingBot-World 2.0 Opens Infinite Worlds
2:04

LingBot-World 2.0 Opens Infinite Worlds

@robbyant_brain · 219 views · 2026-07-08
World ModelsEmbodied AIInteractive Video
RoboSim: A Robot-Native Rust Simulation Engine

RoboSim: A Robot-Native Rust Simulation Engine

@arai01robo · 251 views · 2026-09-09
SimulationRustRobotics
AGIBOT Open-Sources Its World 2026 RL Dataset
0:28

AGIBOT Open-Sources Its World 2026 RL Dataset

@spaceandtech_ · 342 views · 2026-09-03
AgiBotEmbodied AIReinforcement Learning

GRASP: Gradient-Based Planning for World Models

GRASP proposes three innovations making long-horizon planning with world models practical: lifting states for parallel-in-time optimization, stopping brittle state gradients while keeping action gradients, and periodic sync refinement. Achieves leading success rates at long horizons on Push-T.

BLOG

World ModelsPlanningRoboticsGradient OptimizationLong-Range Planning
First glimpse of the Blind Skateboarder robot project
0:51

First glimpse of the Blind Skateboarder robot project

@r_a_ian_008 · 102 views · 2026-08-15
Skateboard RobotVision-FreeRobotics
DeWorldSG: Depth-Aware 3D Semantic Scene Graph Generation via World-Model Priors

DeWorldSG: Depth-Aware 3D Semantic Scene Graph Generation via World-Model Priors

DeWorldSG generates spatio-temporally robust 3D semantic scene graphs from RGB-D sequences. It estimates instance-level geometric 3D Gaussian distributions through depth-guided filtering and represents each object as a probabilistic 3D node. It aggregates spatiotemporal evidence across object pairs and refines relations using contextual priors from a world model (V-JEPA 2). Improves triplet recall by 77.4% and predicate recall by 23.2% over prior SoTA.

Seok-Young Kim, Abdelrahman Elskhawy, Taewook HaJul 1, 2026
Scene graphsRGB-DV-JEPA 2Jul 1, 2026
ChangingTek unveils a superhuman robot hand that switches left and right
0:57

ChangingTek unveils a superhuman robot hand that switches left and right

@Ronald_vanLoon · 71 views · 2026-08-07
Dexterous HandRoboticsChangingTek
SILICA: Repurposing Diffusion Priors for Joint Glass Segmentation and Depth Estimation

SILICA: Repurposing Diffusion Priors for Joint Glass Segmentation and Depth Estimation

Standard depth sensors systematically fail on transparent surfaces, creating corrupted 3D maps and severe navigation hazards. While specialized hardware sensors can detect glass, they lack modularity and have extensive hardware dependencies. Consequently, learning-based monocular depth estimation has emerged as a compelling alternative. However, domain-specific glass-aware monocular depth estimators struggle with unfamiliar indoor layouts; restricted by the severe scarcity of real-world glass depth annotations, they fail to generalize zero-shot to new settings. This motivates us to explore whether the extensive priors of text-to-image diffusion models can enable generalizable perception of transparent surfaces. We introduce SILICA, a unified pipeline leveraging these priors to jointly predict glass segmentation and glass-aware depth. This mutual information exchange establishes a robust spatial hierarchy, entirely eliminating the need for paired real-world glass depth annotations. Subsequently, we use the predicted segmentation mask to explicitly filter incorrect glass depth points from standard sensors, recovering accurate metric glass depth for downstream 3D mapping and autonomous collision avoidance. Supported by our novel Mirage 18k dataset, extensive experiments demonstrate that SILICA achieves remarkable zero-shot transfer across diverse, unseen environments, outperforming state-of-the-art models by almost 20% and setting a new benchmark for transparent surface perception.

Tarun R, Anuj Verma, Laksh NanwaniJul 27, 2026
RoboticsRobotsJul 27, 2026
Model Predictive Planner for UAV Navigation in Non-Convex Air Corridors

Model Predictive Planner for UAV Navigation in Non-Convex Air Corridors

This work presents a motion planning framework for UAV navigation in non-convex urban air corridors. The planner is based on a mixed-integer tracking model predictive control formulation that enforces corridor feasibility and dynamic consistency within a single optimization problem. To guarantee convergence to the target and mitigate the occurrence of local minima induced by non-convex geometry, a shortest-path-based offset cost with feasibility constraints is embedded directly into the planning problem. Numerical simulations show that the proposed formulation generates dynamically valid trajectories that satisfy the corridor constraints and converge to the target without relying on external global planning stages.

Henrique Silva, Marcelo A. Santos, Guilherme V. RaffoJul 27, 2026
RoboticsRobotsJul 27, 2026
Accuracy potential of visual localization exploiting high-end street-level imagery

Accuracy potential of visual localization exploiting high-end street-level imagery

Accurate and reliable pose information with respect to a reference frame is increasingly demanded across applications such as autonomous navigation, surveying, robotics, and augmented and mixed reality. Visual localization can serve as a complementary positioning modality to GNSS, whose applicability and accuracy are often limited. Yet, the accuracy potential of visual localization has not been systematically investigated against survey-grade demands. This is mainly due to the lack of publicly available, large-scale outdoor datasets with ground-truth poses in the sub-centimeter range. In this work, we address both gaps. We introduce a scalable visual localization pipeline that employs precisely georeferenced, high-resolution street-level imagery directly as the scene representation. It combines prior-guided reference candidate selection with on-the-fly local Structure-from-Motion reconstruction and PnP-based pose estimation. We further present the FHNW Muttenz dataset, a real-world dataset covering a contiguous 10 km street network mapped in two mobile mapping campaigns approximately 1.5 years apart. It consists of high-resolution reference imagery and query sequences acquired by four different cameras across five representative scenes. All images are precisely co-registered, yielding 6-DoF ground-truth poses in the sub-centimeter range. Using this dataset, we evaluate the accuracy potential of visual localization. Our experiments demonstrate median pose accuracies in the range of 1-5 cm for translation and 0.05-0.1° for rotation, reaching as low as 1 cm and 0.03° under favorable conditions. These results show that visual localization can complement survey-grade GNSS positioning, paving the way for 3D geospatial data acquisition using consumer devices and fully automated georeferencing approaches. The dataset is publicly available at: https://fhnw-muttenz-vl-dataset.github.io/.

Jonas Meyer, Stephan Nebiker, Pascal TheilerJul 27, 2026
RoboticsRobotsJul 27, 2026
Meet Sally: Wall-Climbing Robot for Industrial Inspection
2:08

Meet Sally: Wall-Climbing Robot for Industrial Inspection

@Ronald_vanLoon · 93 views · 2026-07-22
SallyWall-ClimbingIndustrial Inspection
NVIDIA Cosmos 3 Edge: 4B Edge World Model for Robots
0:14

NVIDIA Cosmos 3 Edge: 4B Edge World Model for Robots

@Xudong07452910 · 169 views · 2026-07-21
NVIDIACosmos 3 EdgeWorld Models
ImprovedVBGS: Real-time Continual Variational Bayes Gaussian Splatting

ImprovedVBGS: Real-time Continual Variational Bayes Gaussian Splatting

On-the-fly reconstruction is a key requirement for many applications in robotics and autonomous navigation. Variational Bayes Gaussian Splatting (VBGS) enables continual learning without replay buffers using Coordinate Ascent Variational Inference (CAVI), but its per-frame iterations over all observed points make it too slow for real-time use with strict memory and latency requirements. We present ImprovedVBGS, an accelerated framework for on-the-fly continual reconstruction. This is achieved primarily through (i) spatially truncated variational inference, and (ii) improved reassignment that uses forwarding, truncation and eliminates wasteful dynamic recompilation. On the NeRF synthetic dataset, we reduce mean per-frame latency from ~84.0 s to ~0.050 s on an RTX 3070 Ti, a 1680x speed-up while maintaining reconstruction quality.

Damani Mguni-CokerJul 17, 2026
3D Gaussian SplattingReal-TimeRoboticsJul 17, 2026