Physical AI

Open-set object detection recognize unseen objects

Open-set detection technology detects and segments unknown objects instantly using a text prompt or reference image with no task-specific pre-training

Walden Robotics raises 300M at 1.1B valuation

Walden Robotics raised 300M at a 1.1B valuation co-led by Toyota and Deviation Capital with NVIDIA participating spun out of Toyota Research Institute

Wi-Fi through-wall human sensing open-source system

Open-sourced system tracks body pose, breathing, heart rate through walls using only Wi-Fi on ESP32, with sub-200ms fall detection and no camera.

SpatialLM and Sam3D object segmentation reconstruction experiments

Messing around with object segmentation and reconstruction models like SpatialLM and Sam3D neat results not yet wired into e2e pipeline

Bringing 3DGS into NVIDIA Isaac Sim: A Photoreal Import Workflow and Four Industrial-Simulation Weaknesses

How to import 3DGS scans into Isaac Sim 6.0: the silent 2^24 point cap, USD conversion, orientation fixes, trajectory cameras — plus four industrial weaknesses and mesh-based workarounds.

Fashion-3DLR: A Controllable 3D Garment Generation Using Pairwise Fashion Elements for Intelligent Design

AI-generated content (AIGC) has made significant progress, with 2D generative models becoming ready-to-use tools for the digital fashion industry. However, 3D garment generation remains in its nascent stage, where in the realm of fashion, the semantic information of diverse design elements exhibits intricate coupling relationships in 3D representations, posing substantial challenges for generating diverse 3D garments. In this work, to handle the above problem, We introduce Fashion-3DLR, a novel 3D garment generation framework that utilizes diverse design elements to create high-quality, versatile 3D garment assets. Specifically, to bridge the semantic gaps between different fashion elements, we propose a Garment Feature Fusion Diffusion Transformer (GFF-DiT) module to integrate 2D fashion design elements, e.g., sketch and texture, into latent space. Within the latent space, we then employ a rectified flow transformer to generate geometry latents, which can be decoded into various 3D garment representations, including 3D Gaussians and meshes. Furthermore, we integrate Fashion-3DLR into downstream tasks, achieving the 3D Gaussian Splatting (3DGS)-driven cloth physical simulation and mesh-based virtual try-on. Experimental results indicate that Fashion-3DLR surpass the previous state-of-the-art methods, which verify that the proposed work can generate well-structured, non-watertight garments capable of physical simulation and virtual try-on, underscoring its potential as a versatile 3D garment design tool.

Shenghao Yang, Hongtao Zhang, Yuhan Yi

The Curse of Precision: A Data Scaling Law for High-Precision Robotic Manipulation

While scaling laws for imitation learning have primarily focused on generalization in open-world settings, the relationship between data and precision in closed-world tasks like robotic assembly remains largely unexplored. This paper systematically investigates this relationship and introduces a novel scaling law. We find that to achieve a fixed success rate, the required number of demonstrations N grows super-exponentially as the target precision P approaches a limit c. This relationship is accurately captured by the model log(N) ∝ 1/(P-c). Crucially, we reveal that the limit precision c is not a static physical constant of the task but an emergent property of the entire agent system, including its sensors and expert policy. Through experiments on canonical manipulation tasks, we validate this law and demonstrate that improving system components, such as adding a wrist camera or using a more effective expert, measurably lowers c, thus expanding the system's achievable precision. Our work provides a new theoretical framework for precision in robotics and a quantitative metric to evaluate system capabilities. Furthermore, these findings provide a practical methodology for guiding the development and debugging of high-precision manipulation systems.

Cuijie Xu, Yuanfan Xu, Min Xue

Pose-Aware Modeling to Mitigate Pose-Related Artifacts in Tactile Gloves

Tactile gloves digitize contact and force during hand-object interactions, enabling robotics applications in dexterous manipulation, teleoperation, and learning from demonstration. To preserve hand dexterity and capture the nuances of natural interactions, these gloves and the integrated tactile sensors are designed to be soft, flexible, and comfortable. However, such flexible sensors are sensitive not only to contact forces but also unavoidably to hand pose changes, resulting in pose-related artifacts (PRAs). PRAs are especially problematic in the low-force range, resulting in misdetections or late-onset detections of contact, which raises the minimum detectable force (MDF) of the glove. In this work, we characterize the PRAs in relation to pose and force. Building on these insights, we introduce a glove-agnostic algorithmic framework that leverages hand pose information, which is increasingly available, to mitigate PRAs without glove modifications. Our pose-aware force estimation model augments tactile-to-force pipelines with a residual prediction branch that explicitly accounts for pose-induced sensor deformations. We validate our approach across 3 glove designs and 15 users, reducing MDF by 10.4%, 12.2%, and 18.3%, with consistent improvements across all evaluated metrics. This method provides a practical path to improving the usability of tactile gloves in data collection and diverse robotic applications.

Tianhong Catherine Yu, Ziyi Kou, Mia Huang

NVIDIA MotionBricks: Block-Based Motion Synthesis

NVIDIA presents MotionBricks at SIGGRAPH 2026, combining motion components like blocks for new robot movements.

VLK: Vision-Language-Kinematics for Humanoid Loco-Manipulation

VLK model learns humanoid loco-manipulation from synthetic scenes, combining vision, language, and kinematics.

Software Replaces Expensive Robot Sensor Arrays

Software approaches displace expensive LiDAR and depth sensor arrays, slashing robot hardware costs.

Hyundai Partners NVIDIA on Robot Reference Platform

Hyundai Motor Group partners with NVIDIA on a Robot Reference Platform combining physical AI tools for faster robot development.