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A user asks how to access data in a custom Python operator in HSB SDK v3.0.0. This suggests missing or unclear documentation/API patterns for operator I/O and data handling.

Newton Actuator steps reuse internal buffers, so recording multiple steps on one wp.Tape causes later steps to overwrite data needed for earlier backward passes. This produces incorrect gradients for multi-step tapes, especially when nonlinear components are involved.

MuJoCo incorrectly resolves a <model> asset file path in an included file when the main MJCF is loaded from a subdirectory, duplicating the directory in the resolved path. This causes model loading to fail with a file-not-found error like 'case2/case2/sub/child.xml'.

MuJoCo's to_xml() drops explicit mass="0" and density="0" on geom defaults, changing the exported model's physical properties. The re-saved XML compiles with default density and yields a much heavier body than the original.

MuJoCo's Python rollout API claims to accept a Sequence[MjData] but only recognizes list, so tuples get wrapped incorrectly and then fail in compiled code. The resulting error message is unhelpful and doesn't explain the real cause.

A forum user asks how Yocto supports the overlays function, with no additional context provided. The lack of details suggests a documentation or onboarding gap around overlays in NVIDIA's Yocto-related support.

Isaac Sim 6.2.0.1 bundles Newton 1.6.1, which predates deterministic collision pipeline and solver APIs, leaving no supported way to get run-to-run determinism with contacts enabled. The issue notes that setting `wp.config.deterministic = RUN_TO_RUN` fails to compile in this environment.

collapse_fixed_joints merges masses and COM correctly but computes a wrong composite inertia about the merged COM. The child inertia is shifted using the child frame origin instead of its COM, and the parent inertia is not shifted to the merged COM.

Downstream Bazel modules fail when using mujoco_pip for Python 3.15 due to rules_python version pinning. The request is to bump rules_python to 2.5.0 to restore pip toolchain/interpreter resolution.

Jetson Thor T5000 PCIe C2 lane width is inconsistent across documents (data sheet/design guide vs Linux Developer Guide). The user is asking which PCIe width is correct (x1 vs x2).

In Isaac Sim Replicator, a single orchestrator.step() triggers four full application updates and renders per capture while only keeping one image. With path tracing where each update converges, this wastes most render time as discarded pixels.

Request for a USB PD VIF file for CYPD8225 (CCG8) firmware on the Jetson Thor reference design. Lack of this artifact may block USB-PD compliance/validation or bring-up workflows for partners.

Newton proposes completing deprecations by removing a legacy rigid-body formulation and making compliant ALM the only rigid-body mode in `SolverVBD`. It also removes a legacy rod body-origin convention (`start`) in favor of COM-centered frames.

In MuJoCo viewer, toggling 'Inertia' appears to toggle 'Convex Hull', while the 'Convex Hull' toggle appears to do nothing. The UI behavior is confusing and may indicate a viewer toggle bug or mislabeled control.

A thin convex hull resting on a closed triangle mesh loses support on coplanar seams due to downward-facing contact normals and also loses all contacts after sinking past its volume centroid. The object can fall through the mesh in scenarios where analytic planes and heightfields remain stable.

SolverImplicitMPM shows unphysical volume loss under plastic shear for a yielding material with the reported Q1/P0 configuration, and it does not improve with more iterations. The reporter notes this appears distinct from a known convergence issue referenced as #5110.

Proposal to rename isaaclab_physx to isaaclab_isaacsim and separate PhysX simulation vs RTX rendering submodules, clarifying responsibilities between isaaclab_* integration layers. Current naming implies PhysX-only but contains broader Isaac Sim integration, creating confusion for users and maintainers.

User is requesting the latest EU RF test reports for Jetson TX2. Missing or hard-to-find compliance documents slow procurement and certification workflows.

User is asking for an expected release date for Alpamayo 2 Edge (2B) weights. Lack of timing information blocks planning for evaluation and integration.

User asks how to add custom kernel modules to the Jetson Installer ISO for R39.2.1. The question suggests missing or unclear documentation/process for integrating out-of-tree modules into the installer media.

Request to add wp.tile_ldlt for symmetric indefinite matrices using Bunch-Kaufman pivoting. The proposal calls for float32/float64 support and 1x1/2x2 diagonal blocks with triangle-selection semantics aligned to tile_cholesky.

Request to add wp.tile_lu for general dense tiles inside Warp kernels, using cuSOLVERDx via libmathdx when available with a native fallback. The scope includes float32/float64 and partial pivoting with exposed pivot/permutation info.

A forum post reports an unaddressed issue in the Isaac ROS repo. The lack of detail aside, it signals support/triage friction for users relying on Isaac ROS.

Forum post requests a FUSA/SIPL signal generator/viewer example but includes no additional details. This suggests missing or hard-to-find sample content for SIPL workflows.

User is asking for Holoscan examples covering 25G networking and dual/quad network setups on Thor AGX. The post indicates a need for guidance or reference configurations.

Progress in robot learning, particularly physical multi-agent reinforcement learning (MARL), is currently challenged by a limited availability of accessible, standardized benchmarks. While simulation-based MARL has produced remarkable emergent behaviors, from coordinated team play to complex tool use, translating these advances to physical systems remains difficult. A key barrier is infrastructural: physical platforms are often prohibitively expensive, require specialized facilities, or lack open mechanisms and designs for reproducibility. We introduce KlaskTron, an open-source, low-cost (<\$2500 USD), desktop-scale robotic testbed for adversarial MARL based on the dynamic tabletop game KLASK. The platform features dual CoreXY gantries with high-torque brushless motors, enabling the high-speed, precise manipulation required for competitive play. Crucially, we provide a complete ecosystem: hardware designs (CAD, BOM), a GPU-native digital twin in NVIDIA Isaac Lab for high-fidelity modeling and simulation, and a validated sim-to-real baseline using a learned neural actuator model. We demonstrate that this baseline enables zero-shot policy transfer, with trained agents exhibiting emergent adversarial behaviors. To ensure full reproducibility of our results, all project assets are released publicly: hardware CAD/BOM and assembly documentation at [https://idscethzurich.github.io/klask_hardware], inference software stack at [https://github.com/IDSCETHZurich/klask_software] and the simulation and MARL setup at [https://github.com/IDSCETHZurich/klask_rl].

In robotics, shape control (or shape servoing) refers to the automatic manipulation of a deformable object to achieve a desired shape. It involves a feedback loop that exploits the relation between robot motion and changes in the measured shape. This relation, known as the interaction Jacobian or, in this context, the deformation Jacobian, depends on the object's mechanical properties, geometry, and boundary conditions (e.g., where the object is grasped). Because these factors are often difficult to model accurately, the deformation Jacobian can be estimated locally from observed shape changes produced by commanded robot motions, without requiring a fine-tuned mechanical model. This article is the first of a two-part tutorial on data-driven Jacobian-based shape control of deformable objects. It introduces the central elements of the approach: the formulation of the shape-control problem, shape representation, estimation and online update of the deformation Jacobian, and a standard control law from the literature. It then assembles these elements into a complete shape-servoing loop, derives a local stability condition for this loop, and illustrates why some target shapes cannot be reached. Part II covers more advanced topics. Throughout both parts, a simulated planar example in which two robot arms manipulate an elastic rod observed by a camera illustrates the concepts.