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User reports they cannot install the ROS2 Jazzy package `ros-jazzy-isaac-ros-foundationpose` in an Isaac ROS environment. Without the package, they can’t proceed with FoundationPose workflows.

Same install failure is reposted in another NVIDIA forum category: `ros-jazzy-isaac-ros-foundationpose` cannot be installed in the Isaac ROS environment. This blocks use of FoundationPose.

A user asks about Nova Orin initialization for the Nova Carter Robot. The post contains no additional details.

Warp plans work to support GPU kernel launches with hardware-coherent CPU memory, pinned CPU arrays, and peer GPU arrays when directly addressable. The issue emphasizes preserving clear diagnostics for invalid cases.

User reports that the solver documentation page does not show soft body support for VBD and asks whether VBD supports soft bodies. This suggests docs may be incorrect or incomplete.

User reports that a 'Public sources' folder is empty. This blocks access to expected source materials.

User asks why kernel and dtb partitions were removed on Thor. This indicates confusion about platform boot/partition layout changes.

In Isaac Lab v3.0.0-beta, lift_cube_sm.py ignores the --viz kit option and no Kit/Isaac Sim window opens despite the process running. A one-line change to AppLauncher initialization appears to fix it locally.

Kamino examples were added but are missing required README entries and screenshots on the release branch. This reduces discoverability and fails documented contribution requirements.

User requests a Franka tutorial for the cuMotion MoveIt plugin with Isaac Sim. This indicates a documentation gap for a common manipulator workflow.

User requests a Franka tutorial for the cuMotion MoveIt plugin with Isaac Sim. This suggests users struggle to connect the pieces without a guided example.

Warp docs expose dtype-specific HashGrid query types alongside the intended wp.HashGridQuery, which users shouldn't need to handle. The request is to consolidate documentation/stubs around the single public type while keeping internal codegen behavior.

We compare three state-of-the-art proprioceptive state estimators for quadruped robots: MUSE [1], the Invariant Extended Kalman Filter (IEKF) [2], and the Invariant Smoother (IS) [3], on the CYN-1 sequence of the GrandTour Dataset [4]. Our goal is to give practitioners clear guidance on accuracy and computation time: we report long-term accuracy (Absolute Trajectory Error, ATE), short-term accuracy (translational and rotational Relative Pose Error, RPE), and per-update computation time on a fixed hardware/software stack. On this dataset, RPEs are broadly similar across methods, while IEKF and IS achieve a lower ATE than MUSE. Runtime results highlight the accuracy-latency trade-offs across the three approaches. In the discussion, we outline the evaluation choices used to ensure a fair comparison and analyze factors that influence short-horizon metrics. Overall, this study provides a concise snapshot of accuracy and cost, helping readers choose an estimator that fits their application constraints, with all evaluation code and documentation released open-source at https://github.com/iit-DLSLab/state_estimation_benchmark for full reproducibility.