{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-stack-of-tasks--pinocchio","slug":"stack-of-tasks--pinocchio","name":"pinocchio","type":"repo","url":"http://stack-of-tasks.github.io/pinocchio/","page_url":"https://unfragile.ai/stack-of-tasks--pinocchio","categories":["frameworks-sdks"],"tags":["analytical-derivatives","automatic-differentiation","c-plus-plus","casadi","code-generation","conda","cppad","dynamics","fcl","kinematics","motion-planning","pinocchio","python","rigid-body-dynamics","robotics","ros","urdf"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-stack-of-tasks--pinocchio__cap_0","uri":"capability://data.processing.analysis.spatial.algebra.based.rigid.body.kinematics.computation","name":"spatial-algebra-based rigid body kinematics computation","description":"Computes forward kinematics and Jacobians for articulated systems using Roy Featherstone's spatial algebra framework, which represents rigid body transformations as 6D spatial vectors (3D linear + 3D angular). The library uses template-based C++ to instantiate algorithms for different scalar types (double, float, CppAD, CasADi), enabling both numerical and symbolic computation paths. Spatial algebra operations are optimized through specialized matrix representations (6x6 spatial inertia matrices, 6D twists/wrenches) that reduce computational overhead compared to naive 4x4 homogeneous transformation approaches.","intents":["Compute end-effector position and orientation for a robot given joint angles","Calculate Jacobian matrices for velocity and force mapping across multiple joints","Evaluate kinematic chains with arbitrary joint types (revolute, prismatic, spherical, etc.)","Perform kinematic computations with automatic differentiation for gradient-based optimization"],"best_for":["Roboticists implementing motion planning and control algorithms","Researchers requiring high-performance kinematics with analytical derivatives","Teams building trajectory optimization pipelines with differentiable kinematics"],"limitations":["Spatial algebra abstractions add ~5-10% computational overhead vs hand-optimized code for single-precision float","Template instantiation increases compile time; full library compilation takes 5-15 minutes depending on enabled backends","Jacobian computation requires full kinematic chain evaluation; no incremental updates for single joint changes"],"requires":["C++11 or later compiler (GCC 4.9+, Clang 3.5+, MSVC 2015+)","Eigen3 library (3.2.0+) for linear algebra","CMake 3.10+ for build configuration"],"input_types":["robot model (URDF/SDF/MJCF file or programmatic Model object)","joint configuration vector (Eigen::VectorXd of size njoints)","optional: joint velocities for Jacobian time derivatives"],"output_types":["SE(3) transformation matrices (4x4 homogeneous or Pinocchio's SE3 class)","Jacobian matrices (6 x njoints for spatial Jacobians)","frame positions, orientations, velocities in world or local coordinates"],"categories":["data-processing-analysis","robotics-kinematics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stack-of-tasks--pinocchio__cap_1","uri":"capability://data.processing.analysis.inverse.dynamics.with.analytical.derivatives","name":"inverse dynamics with analytical derivatives","description":"Computes joint torques required to achieve desired accelerations using the Recursive Newton-Euler algorithm (RNEA), then derives analytical gradients of torques with respect to configuration, velocity, and acceleration via automatic differentiation backends (CppAD, CasADi). The RNEA algorithm propagates spatial velocities and accelerations forward through the kinematic chain, then back-propagates forces and torques, achieving O(n) complexity. Derivatives are computed by either CppAD's tape-based AD or CasADi's symbolic graph construction, enabling gradient-based control optimization without finite differences.","intents":["Compute inverse dynamics (joint torques) for a given trajectory of configurations, velocities, and accelerations","Generate analytical Jacobians of inverse dynamics for model predictive control (MPC) and trajectory optimization","Perform sensitivity analysis: how do joint torques change with respect to inertial parameters or external forces","Build differentiable physics simulators for learning-based control policies"],"best_for":["Control engineers implementing trajectory tracking with analytical gradients","Researchers in differentiable simulation and physics-informed learning","Teams optimizing robot motions via gradient descent (e.g., iLQR, DDP)"],"limitations":["CppAD tape recording adds ~2-5x overhead vs numerical RNEA; CasADi symbolic graphs add ~3-8x overhead","Derivatives are computed for entire algorithm; no selective differentiation of subsets (e.g., only gravity terms)","Tape/graph construction is not thread-safe; separate instances required for parallel derivative evaluation","Memory usage scales with tape length; long trajectories (>1000 steps) may exhaust GPU memory in CasADi"],"requires":["C++11 or later compiler","Eigen3 (3.2.0+)","CppAD (20190300+) or CasADi (3.5.0+) for analytical derivatives","CMake 3.10+ with AD backend enabled via -DBUILD_WITH_CPPAD_SUPPORT=ON or -DBUILD_WITH_CASADI_SUPPORT=ON"],"input_types":["robot model (URDF/SDF or Model object)","joint configuration q (Eigen::VectorXd)","joint velocity v (Eigen::VectorXd)","joint acceleration a (Eigen::VectorXd)","optional: external forces at frames (std::vector<Force>)"],"output_types":["joint torques tau (Eigen::VectorXd)","Jacobian of tau w.r.t. q, v, a (Eigen::MatrixXd or CasADi SX/MX)","partial derivatives for sensitivity analysis"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stack-of-tasks--pinocchio__cap_10","uri":"capability://image.visual.visualization.and.debugging.via.meshcat.gepetto.viewer.integration","name":"visualization and debugging via meshcat/gepetto-viewer integration","description":"Integrates with MeshCat (web-based 3D visualization) and Gepetto-Viewer (standalone viewer) to display robot configurations, trajectories, and collision geometries. The visualization system loads robot meshes from URDF, updates frame positions via forward kinematics, and renders trajectories as animated sequences. Users can interactively manipulate joint sliders to explore configurations or record videos of simulated motions. The system supports both C++ and Python interfaces, with Python providing more convenient high-level APIs.","intents":["Visualize robot configurations and trajectories for debugging and presentation","Interactively explore robot workspace by manipulating joint sliders","Record videos of simulated motions for documentation or publication","Debug collision detection by visualizing collision geometries and contact points"],"best_for":["Roboticists debugging kinematics/dynamics algorithms visually","Researchers presenting results via animated trajectories","Teams integrating visualization into Jupyter notebooks (MeshCat)"],"limitations":["Visualization is not real-time optimized; rendering >1000 frames at 60 FPS may be slow","MeshCat requires web browser and internet connection (or local server); not suitable for offline use","Gepetto-Viewer is less actively maintained than MeshCat; some features may be outdated","No built-in support for rendering contact forces, pressure distributions, or other physics quantities","Mesh loading is slow for complex geometries; may require decimation for interactive use"],"requires":["Python 3.6+ (for visualization API)","MeshCat (0.0.20+) or Gepetto-Viewer (4.0.0+) installation","Web browser (for MeshCat)","Pinocchio Python bindings"],"input_types":["robot model with visual geometries (URDF/SDF)","joint configurations (NumPy arrays or lists)","optional: trajectory data (list of configurations)"],"output_types":["interactive 3D visualization (MeshCat/Gepetto)","video files (MP4, WebM) via screen recording","static images (PNG, JPEG) via screenshot"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stack-of-tasks--pinocchio__cap_11","uri":"capability://code.generation.editing.joint.type.abstraction.with.custom.joint.support","name":"joint type abstraction with custom joint support","description":"Provides an abstract Joint interface that supports standard joint types (revolute, prismatic, spherical, planar, free-flyer) and enables users to define custom joint types via template specialization. Each joint type encapsulates its kinematics (forward kinematics, Jacobian computation) and dynamics (inertia propagation, force/torque mapping). The system uses a visitor pattern to dispatch operations to the appropriate joint type without runtime type checking. Custom joints can be added by implementing the Joint interface and registering with the Model.","intents":["Build robot models with standard joint types (revolute, prismatic, spherical, etc.)","Implement custom joint types (e.g., cable-driven, compliant, nonholonomic) without modifying core algorithms","Extend Pinocchio for specialized robotics applications (soft robotics, continuum robots)","Validate joint implementations via unit tests and benchmarks"],"best_for":["Roboticists implementing custom joint types for specialized robots","Researchers in soft robotics and continuum mechanics","Teams extending Pinocchio for non-standard kinematic chains"],"limitations":["Custom joint implementation requires C++ knowledge and understanding of Pinocchio's architecture","Joint types must satisfy certain mathematical properties (e.g., Lie group structure); invalid implementations may produce incorrect results","No automatic validation of custom joint implementations; users must write comprehensive tests","Performance of custom joints depends on implementation quality; poorly optimized joints can bottleneck entire system","Limited documentation and examples for custom joint implementation"],"requires":["C++11 or later compiler with strong template support","Eigen3 (3.2.0+)","Understanding of Pinocchio's Joint interface and spatial algebra","CMake 3.10+ for custom joint compilation"],"input_types":["joint type specification (enum or class)","joint configuration q and velocity v (Eigen vectors)","optional: joint limits, friction parameters"],"output_types":["joint transformation (SE(3) or custom representation)","joint Jacobian (6 x ndof matrix)","joint inertia (spatial inertia matrix)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stack-of-tasks--pinocchio__cap_12","uri":"capability://data.processing.analysis.frame.and.reference.system.management","name":"frame and reference system management","description":"Manages a hierarchical frame system where each frame is associated with a joint or link in the kinematic tree. Frames represent reference points (e.g., end-effector, sensor, contact point) and are updated via forward kinematics. The system supports both fixed frames (rigidly attached to links) and moving frames (attached to joints). Users can query frame positions, orientations, Jacobians, and velocities by frame name or ID. The frame system enables intuitive robot programming without explicit kinematic chain manipulation.","intents":["Define end-effector and sensor frames for task-space control","Query frame positions and Jacobians for inverse kinematics","Manage multiple contact points or manipulation targets","Organize robot structure hierarchically for complex systems"],"best_for":["Control engineers implementing task-space control (e.g., Cartesian impedance)","Researchers in manipulation and grasping","Teams managing complex multi-limb robots (humanoids, quadrupeds)"],"limitations":["Frame queries by name are slower than numeric indices; not suitable for tight loops","No automatic frame generation from URDF; users must manually add custom frames","Frame hierarchy is fixed at model construction time; no dynamic frame addition","No built-in support for frame-relative kinematics (e.g., relative Jacobians between frames)"],"requires":["C++11 or later compiler","Eigen3 (3.2.0+)","Pinocchio library"],"input_types":["robot model with frames (URDF/SDF or programmatic Model)","joint configuration q (Eigen vector)","frame name or ID (string or integer)"],"output_types":["frame position (3D vector)","frame orientation (3x3 rotation matrix or quaternion)","frame Jacobian (6 x njoints matrix)","frame velocity (6D spatial velocity)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stack-of-tasks--pinocchio__cap_13","uri":"capability://data.processing.analysis.analytical.derivatives.of.kinematics.and.dynamics.algorithms","name":"analytical derivatives of kinematics and dynamics algorithms","description":"Computes analytical Jacobians and higher-order derivatives of kinematics and dynamics algorithms (forward kinematics, inverse dynamics, forward dynamics) via automatic differentiation or symbolic computation. Derivatives are computed by instantiating algorithms with CppAD or CasADi scalar types, which record/build expression graphs during evaluation. The system supports both first-order derivatives (Jacobians) and higher-order derivatives (Hessians, etc.) for optimization. Derivatives can be computed for entire algorithms or selected subsets via tape/graph slicing.","intents":["Compute Jacobians of kinematics for inverse kinematics and task-space control","Generate analytical Jacobians of inverse dynamics for trajectory optimization","Compute Hessians for second-order optimization methods (Newton, quasi-Newton)","Perform sensitivity analysis: how do outputs change with respect to inertial parameters"],"best_for":["Control engineers implementing gradient-based trajectory optimization","Researchers in differentiable simulation and learning-based control","Teams generating embedded C code for real-time control"],"limitations":["Derivative computation adds 2-8x overhead vs numerical evaluation depending on AD backend","Derivatives are computed for entire algorithms; selective differentiation requires manual code splitting","Tape/graph construction is not thread-safe; separate instances required for parallel evaluation","Higher-order derivatives (Hessians) are significantly slower than first-order (10-100x overhead)","No automatic sparsity exploitation; dense Jacobians are computed even for sparse systems"],"requires":["C++11 or later compiler","Eigen3 (3.2.0+)","CppAD (20190300+) or CasADi (3.5.0+)","CMake 3.10+ with AD backend enabled"],"input_types":["robot model (URDF/SDF or Model object)","joint configuration, velocity, acceleration (CppAD/CasADi scalar types)"],"output_types":["kinematics/dynamics results as CppAD/CasADi expressions","Jacobian matrices (CppAD jacobian or CasADi jacobian function)","Hessian matrices (via second-order differentiation)","C code (via CasADi code generation)"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stack-of-tasks--pinocchio__cap_2","uri":"capability://data.processing.analysis.forward.dynamics.with.cholesky.based.constraint.handling","name":"forward dynamics with cholesky-based constraint handling","description":"Solves the forward dynamics problem (computing accelerations from torques) using the Articulated Body Algorithm (ABA) with optional Cholesky decomposition for efficient constraint handling. For unconstrained systems, ABA runs in O(n) time by computing spatial inertias and accelerations recursively. For constrained systems (contact, joint limits), Pinocchio uses Contact Cholesky decomposition to factorize the constraint Jacobian and solve for contact forces, then computes accelerations. The Delassus operator (contact constraint Hessian) is computed via the Cholesky factor, enabling proximal solvers for friction cones and inequality constraints.","intents":["Simulate robot dynamics forward in time given joint torques and external forces","Compute accelerations for constrained systems with contact or joint limits","Solve contact-rich dynamics problems (walking, manipulation with friction) efficiently","Generate training data for learning-based dynamics models with contact"],"best_for":["Roboticists simulating legged locomotion and contact-rich manipulation","Researchers in contact-aware trajectory optimization and MPC","Teams building differentiable simulators for sim-to-real transfer learning"],"limitations":["Cholesky decomposition assumes positive-definite constraint Jacobian; singular or rank-deficient constraints require regularization","Contact Cholesky is slower than ABA for unconstrained systems (~2-3x overhead); only beneficial with >2-3 active constraints","Proximal solvers for friction cones require tuning of regularization parameters; convergence not guaranteed for all friction coefficients","No built-in support for sliding friction models; only rigid contact and Coulomb friction cones"],"requires":["C++11 or later compiler","Eigen3 (3.2.0+)","CMake 3.10+","Optional: CppAD/CasADi for analytical derivatives of constrained dynamics"],"input_types":["robot model with contact geometry (URDF + collision meshes)","joint torques tau (Eigen::VectorXd)","joint configuration q and velocity v (Eigen::VectorXd)","external forces at frames (std::vector<Force>)","optional: contact constraints (frame pairs, friction coefficients)"],"output_types":["joint accelerations a (Eigen::VectorXd)","contact forces at constraint points (std::vector<Force>)","constraint impulses for discrete time integration"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stack-of-tasks--pinocchio__cap_3","uri":"capability://data.processing.analysis.urdf.sdf.mjcf.model.parsing.with.mimic.joint.support","name":"urdf/sdf/mjcf model parsing with mimic joint support","description":"Parses robot models from URDF, SDF, and MJCF file formats into Pinocchio's internal Model representation, handling joint hierarchies, inertial properties, collision/visual geometries, and joint constraints. The parser uses XML parsing (via tinyxml2 or urdfdom) to extract kinematic chains, then constructs a tree structure with support for mimic joints (dependent joints that follow a linear relationship to parent joints). Mimic joints are handled via an extended Model class that tracks joint dependencies, enabling efficient computation of dependent joint accelerations and forces without explicit constraint solving.","intents":["Load a robot URDF file and automatically construct a Pinocchio Model for kinematics/dynamics","Handle robots with mimic joints (e.g., parallel grippers, coupled legs) without manual constraint implementation","Parse collision geometries and associate them with robot frames for collision detection","Convert between URDF, SDF, and MJCF formats via Pinocchio's unified Model interface"],"best_for":["Roboticists working with standard robot models (UR, ABB, Boston Dynamics, etc.)","Teams integrating Pinocchio into ROS pipelines that use URDF","Researchers comparing simulators (Gazebo, MuJoCo, Drake) with unified model loading"],"limitations":["URDF parser does not support all URDF extensions (e.g., transmission elements, gazebo plugins); requires manual post-processing","SDF parser is less mature than URDF; some SDF-specific features (nested models, plugins) are not fully supported","Mimic joint support is limited to linear relationships (q_mimic = multiplier * q_parent + offset); no nonlinear dependencies","Collision geometry parsing requires FCL library; visual geometry is parsed but not used for rendering (separate visualization system)","No automatic URDF validation; malformed files may produce cryptic errors or silently incorrect models"],"requires":["C++11 or later compiler","Eigen3 (3.2.0+)","urdfdom library (0.4.0+) for URDF parsing","hpp-fcl (1.0.0+) for collision geometry handling","CMake 3.10+ with -DBUILD_WITH_URDF_SUPPORT=ON"],"input_types":["URDF file path (string)","SDF file path (string)","MJCF file path (string)","optional: package path hints for resolving mesh URIs"],"output_types":["Pinocchio Model object (contains kinematic tree, inertias, joint types)","GeometryModel object (collision and visual geometries)","std::vector of mimic joint relationships (for extended models)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stack-of-tasks--pinocchio__cap_4","uri":"capability://data.processing.analysis.collision.detection.and.geometry.management.with.fcl.integration","name":"collision detection and geometry management with fcl integration","description":"Manages collision geometries (meshes, primitives) associated with robot frames and provides collision detection via integration with the hpp-fcl library. Geometries are stored in a GeometryModel that parallels the kinematic Model, with each geometry linked to a frame and updated via forward kinematics. Collision queries (distance, penetration depth, contact points) are computed by transforming geometries to world coordinates and invoking FCL's BVH-based algorithms. The system supports both collision checking (binary yes/no) and distance computation for safety margins.","intents":["Check if a robot configuration is in collision with obstacles or self-collision","Compute minimum distance between robot links for safety-aware motion planning","Generate contact points and normals for contact-aware trajectory optimization","Visualize collision geometries and debug kinematic configurations"],"best_for":["Motion planners requiring fast collision checking (RRT, PRM, trajectory optimization)","Researchers in contact-aware control and manipulation planning","Teams integrating Pinocchio with MoveIt or other ROS planning frameworks"],"limitations":["Collision detection is not real-time optimized; no spatial hashing or broad-phase culling beyond FCL's BVH","Mesh collision detection is slower than primitive-only (sphere, box, cylinder); complex meshes may require decimation","No support for deformable geometries or soft contact models; only rigid collision","Distance computation is slower than collision checking (~2-5x overhead); not suitable for high-frequency queries (>1kHz)","FCL integration is C++ only; Python bindings have reduced functionality"],"requires":["C++11 or later compiler","Eigen3 (3.2.0+)","hpp-fcl (1.0.0+) library","CMake 3.10+ with -DBUILD_WITH_COLLISION_SUPPORT=ON","Optional: mesh files (STL, OBJ) referenced in URDF"],"input_types":["GeometryModel object (from URDF parsing or manual construction)","joint configuration q (Eigen::VectorXd)","optional: collision pairs to check (std::vector<std::pair<GeometryId, GeometryId>>)"],"output_types":["boolean collision status (true/false)","minimum distance between geometries (double)","contact points and normals (std::vector<Contact>)","penetration depth for overlapping geometries"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stack-of-tasks--pinocchio__cap_5","uri":"capability://tool.use.integration.python.bindings.with.numpy.integration","name":"python bindings with numpy integration","description":"Provides Python bindings for core Pinocchio functionality via pybind11, enabling NumPy array interoperability for kinematics, dynamics, and collision queries. Bindings are generated via template specialization for common scalar types (double, float) and expose high-level APIs (RobotWrapper, Data structures) alongside low-level algorithm functions. NumPy arrays are automatically converted to/from Eigen vectors and matrices, eliminating data copy overhead for large arrays. The Python interface supports both eager evaluation (immediate computation) and lazy evaluation (symbolic graphs via CasADi).","intents":["Prototype robot control algorithms in Python without C++ compilation","Integrate Pinocchio into Jupyter notebooks for interactive robotics research","Use NumPy/SciPy optimization tools (scipy.optimize.minimize) with Pinocchio dynamics","Build Python-based motion planning pipelines with fast kinematics/dynamics evaluation"],"best_for":["Researchers prototyping algorithms in Python before C++ optimization","Teams using Jupyter for interactive robotics education and experimentation","Data scientists building learning-based control models with PyTorch/TensorFlow"],"limitations":["Python bindings have ~10-20% overhead vs C++ due to pybind11 marshaling and GIL contention","Not all C++ features are exposed in Python (e.g., custom scalar types, some template specializations)","Symbolic computation (CasADi) in Python requires explicit graph construction; no automatic differentiation via PyTorch/JAX","Visualization requires separate MeshCat or Gepetto-Viewer installation; no built-in Jupyter rendering","Thread safety is limited; GIL prevents true parallelism in multi-threaded Python code"],"requires":["Python 3.6+","NumPy (1.14.0+)","pybind11 (2.2.0+) for binding generation","Pinocchio C++ library compiled with Python support (-DBUILD_PYTHON_INTERFACE=ON)","Optional: CasADi (3.5.0+) for symbolic computation in Python"],"input_types":["NumPy arrays (ndarray) for configurations, velocities, accelerations","URDF file paths (string)","Python lists or dicts for geometry/collision specifications"],"output_types":["NumPy arrays for kinematics/dynamics results","CasADi SX/MX objects for symbolic computation","Python objects (RobotWrapper, Data) for state management"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stack-of-tasks--pinocchio__cap_6","uri":"capability://data.processing.analysis.automatic.differentiation.via.cppad.and.casadi.backends","name":"automatic differentiation via cppad and casadi backends","description":"Enables analytical differentiation of kinematics and dynamics algorithms by instantiating Pinocchio templates with CppAD (tape-based AD) or CasADi (symbolic graph) scalar types instead of double/float. CppAD records operations into a tape during forward evaluation, then replays the tape backward to compute derivatives; CasADi builds a symbolic expression graph that can be compiled to C code or used for further symbolic manipulation. Users can switch backends by changing template parameters without modifying algorithm code. Derivatives are computed for entire algorithms (RNEA, ABA, forward kinematics) or selected subsets via tape/graph slicing.","intents":["Compute analytical Jacobians of kinematics/dynamics for gradient-based optimization","Generate C code for embedded control via CasADi code generation","Perform sensitivity analysis: how do outputs change with respect to inertial parameters","Build differentiable simulators for learning-based control (e.g., neural network training)"],"best_for":["Control engineers implementing trajectory optimization (iLQR, DDP, MPC)","Researchers in differentiable simulation and physics-informed learning","Teams generating embedded C code for real-time control"],"limitations":["CppAD tape recording adds 2-5x computational overhead vs numerical evaluation; tape memory scales with algorithm complexity","CasADi symbolic graphs add 3-8x overhead and require explicit graph construction (no automatic tracing like PyTorch)","Derivatives are computed for entire algorithms; selective differentiation (e.g., only gravity terms) requires manual code splitting","Tape/graph construction is not thread-safe; separate instances required for parallel evaluation","CasADi code generation targets C, not CUDA; no GPU acceleration for large-scale optimization"],"requires":["C++11 or later compiler","Eigen3 (3.2.0+)","CppAD (20190300+) or CasADi (3.5.0+) library","CMake 3.10+ with -DBUILD_WITH_CPPAD_SUPPORT=ON or -DBUILD_WITH_CASADI_SUPPORT=ON","Optional: IPOPT (3.12.0+) for CasADi-based optimization"],"input_types":["robot model (URDF/SDF or Model object)","joint configuration q, velocity v, acceleration a (CppAD/CasADi scalar types)","optional: external forces, constraint specifications"],"output_types":["kinematics/dynamics results as CppAD/CasADi expressions","Jacobian matrices (CppAD jacobian or CasADi jacobian function)","C code (via CasADi code generation)","symbolic expression graphs (CasADi SX/MX)"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stack-of-tasks--pinocchio__cap_7","uri":"capability://data.processing.analysis.center.of.mass.and.momentum.computation.with.derivatives","name":"center of mass and momentum computation with derivatives","description":"Computes the center of mass (CoM) position and velocity, as well as linear and angular momentum of the entire robot or selected subtrees. The computation traverses the kinematic tree, accumulating mass-weighted positions and velocities from each link. Momentum Jacobians (derivatives of momentum w.r.t. joint velocities) are computed analytically, enabling momentum-based control and balance optimization. The system supports both numerical computation and analytical differentiation via CppAD/CasADi for gradient-based balance control.","intents":["Compute center of mass position for balance control in legged robots","Calculate momentum Jacobians for momentum-based control (e.g., whole-body control)","Perform sensitivity analysis: how does CoM change with joint configuration","Optimize robot motion to maintain angular momentum constraints (e.g., acrobatic maneuvers)"],"best_for":["Legged robotics researchers implementing balance and locomotion control","Teams developing whole-body control frameworks (e.g., TSID, Talos)","Researchers in humanoid robotics and acrobatic motion planning"],"limitations":["CoM computation assumes rigid bodies; no support for flexible links or deformable structures","Momentum Jacobians are computed for the entire robot; no efficient subtree-specific computation","Angular momentum computation assumes fixed base; floating-base systems require manual adjustment","No built-in support for contact-induced momentum changes; requires external force integration"],"requires":["C++11 or later compiler","Eigen3 (3.2.0+)","CMake 3.10+","Optional: CppAD/CasADi for analytical derivatives"],"input_types":["robot model with inertial properties (URDF/SDF or Model object)","joint configuration q and velocity v (Eigen::VectorXd or AD scalar types)"],"output_types":["center of mass position (3D vector)","center of mass velocity (3D vector)","linear momentum (3D vector)","angular momentum (3D vector)","momentum Jacobians (6 x njoints matrix)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stack-of-tasks--pinocchio__cap_8","uri":"capability://data.processing.analysis.multiprecision.and.alternative.scalar.type.support","name":"multiprecision and alternative scalar type support","description":"Enables Pinocchio algorithms to work with arbitrary scalar types (double, float, long double, CppAD, CasADi, Boost multiprecision) via C++ template specialization. The library instantiates core algorithms for common types at compile time, and users can extend with custom types by specializing template functions. This design allows the same algorithm code to run with different precision levels or symbolic types without modification. Multiprecision support is useful for high-precision trajectory optimization or sensitivity analysis where numerical errors accumulate.","intents":["Perform high-precision dynamics computation for sensitivity analysis or validation","Use single-precision (float) for embedded systems with limited memory/compute","Conduct symbolic computation for analytical derivation or code generation","Validate numerical algorithms by comparing results across precision levels"],"best_for":["Researchers validating numerical algorithms via multiprecision comparison","Embedded robotics teams optimizing for memory/compute constraints","Teams generating symbolic code for analytical verification"],"limitations":["Multiprecision types (long double, Boost multiprecision) are significantly slower than double (5-50x overhead)","Not all external libraries support arbitrary scalar types; some algorithms may fall back to double","Template instantiation for many types increases compile time and binary size","Debugging multiprecision code is difficult; error messages are verbose and hard to interpret","No automatic precision selection; users must manually choose appropriate types"],"requires":["C++11 or later compiler with strong template support","Eigen3 (3.2.0+) with multiprecision support","Optional: Boost (1.60.0+) for multiprecision types","CMake 3.10+ for template instantiation configuration"],"input_types":["robot model (URDF/SDF or Model object)","joint configuration, velocity, acceleration in chosen scalar type"],"output_types":["kinematics/dynamics results in chosen scalar type","Jacobians and derivatives in chosen scalar type"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stack-of-tasks--pinocchio__cap_9","uri":"capability://tool.use.integration.robotwrapper.high.level.api.with.convenience.utilities","name":"robotwrapper high-level api with convenience utilities","description":"Provides a high-level Python/C++ API (RobotWrapper class) that encapsulates Model, Data, and GeometryModel objects, offering convenience methods for common tasks (forward kinematics, inverse kinematics, collision checking, visualization). RobotWrapper automatically manages Data structure allocation and updates, reducing boilerplate code. It also provides utility functions for loading robots from URDF, setting up visualizers, and accessing frame/joint information by name. 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