| 1 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 14 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
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.
Unique: Uses Featherstone's spatial algebra framework with template-based scalar polymorphism, enabling seamless switching between numerical (double/float) and symbolic (CppAD/CasADi) computation without algorithm reimplementation. Most robotics libraries use homogeneous 4x4 matrices; Pinocchio's 6D spatial vectors reduce memory bandwidth and enable vectorized operations.
vs alternatives: Faster than ROS MoveIt for kinematics-only queries (no ROS overhead) and more flexible than RBDL for automatic differentiation (native CppAD/CasADi integration vs external wrapping)
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.
Unique: Integrates Recursive Newton-Euler algorithm with pluggable AD backends (CppAD, CasADi) via template specialization, allowing users to switch from numerical to symbolic derivatives without code changes. Most robotics libraries compute derivatives via finite differences or require manual Jacobian derivation; Pinocchio's approach is both faster and more accurate.
vs alternatives: Outperforms RBDL's finite-difference derivatives by 10-50x and provides symbolic derivatives that CasADi alone cannot generate (requires domain-specific robotics knowledge embedded in RNEA)
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.
Unique: Provides seamless integration with MeshCat and Gepetto-Viewer via high-level Python APIs that automatically handle mesh loading, frame updates, and trajectory animation. Users can visualize complex trajectories with minimal code. Most robotics libraries require manual visualization setup; Pinocchio's integration is more convenient.
vs alternatives: More convenient than RViz (no ROS overhead) and more flexible than Gazebo's visualization (supports arbitrary trajectories, not just simulation playback)
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.
Unique: Uses visitor pattern and template specialization to support arbitrary joint types without runtime polymorphism overhead. Custom joints are added via template specialization, enabling compile-time optimization. Most robotics libraries hardcode joint types; Pinocchio's design enables flexible extension.
vs alternatives: More efficient than runtime polymorphism (no virtual function overhead) and more flexible than RBDL (which supports only standard joint types)
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.
Unique: Provides hierarchical frame management with both numeric and name-based access, enabling intuitive robot programming. Frames are automatically updated via forward kinematics without explicit user management. Most robotics libraries require manual frame tracking; Pinocchio's frame system is more convenient.
vs alternatives: More flexible than ROS TF (supports arbitrary frame hierarchies, not just tree structure) and more efficient than Drake's frame system (no protobuf overhead)
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.
Unique: Computes analytical derivatives by instantiating algorithms with AD scalar types, enabling both tape-based (CppAD) and symbolic (CasADi) differentiation. Derivatives are computed for entire algorithms without manual Jacobian derivation. Most robotics libraries use finite differences or hand-derived Jacobians; Pinocchio's approach is more accurate and efficient.
vs alternatives: 10-50x faster than finite-difference Jacobians and more accurate than hand-derived Jacobians (no manual derivation errors)
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.
Unique: Implements Contact Cholesky decomposition for constraint handling, factorizing the contact constraint Jacobian to compute the Delassus operator efficiently. This enables proximal solvers for friction cones without explicit constraint matrix inversion. Most simulators use QP solvers (quadprog, OSQP) or iterative methods; Pinocchio's Cholesky approach is O(n³) in constraint count but O(n) in DOF count.
vs alternatives: Faster than Gazebo/Bullet for contact-rich dynamics (no iterative solver overhead) and more efficient than Drake for systems with few contacts due to direct Cholesky factorization
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.
Unique: Provides unified parsing for URDF, SDF, and MJCF with native support for mimic joints via an extended Model class that tracks joint dependencies. Most robotics libraries support only URDF or require separate parsers for each format; Pinocchio's unified approach enables format-agnostic model loading.
vs alternatives: More flexible than ROS urdfdom (supports SDF/MJCF and mimic joints) and faster than Drake's model loading (no protobuf overhead, direct C++ instantiation)
+6 more capabilities
Generates single-line and multi-line code suggestions in real-time as developers type, using semantic indexing of the entire codebase to retrieve relevant type definitions, function signatures, and contextual patterns. The system maintains a 1M token context window (Pro/Team tiers) that enables suggestions informed by distant code definitions and cross-file dependencies, constructed via local codebase semantic search rather than simple token-based recency. Suggestions adapt to detected coding style on Pro/Team tiers through implicit pattern learning from recent edits.
Unique: 1M token context window with codebase-wide semantic indexing enables suggestions informed by distant code definitions and cross-file patterns, versus competitors (Copilot, Tabnine) that typically use fixed context windows (4K-32K tokens) or file-local context. Claimed 250ms latency suggests optimized retrieval pipeline, though indexing mechanism and performance at scale remain undisclosed.
vs alternatives: Larger context window than GitHub Copilot (8K-32K tokens) and faster latency than unnamed competitors (250ms vs 783ms claimed), enabling suggestions on large codebases with minimal typing delay; trade-off is cloud dependency and undisclosed free tier limitations.
Provides a separate chat interface supporting multiple LLM backends (GPT-4o, Claude 3.5 Sonnet, GPT-4, others) for conversational code assistance. Users attach files, reference recent edits, and trigger compiler diagnostic uploads; the system generates diffs and applies code changes directly to the editor. Model selection is per-conversation, and $5/month in credits (included in Pro/Team) covers external model API costs; overage pricing is undisclosed. Hotkey-driven workflow enables rapid context switching between inline completion and chat.
Unique: Multi-model chat interface with per-conversation model selection and integrated diff application, combined with compiler diagnostic auto-upload. Unlike Copilot Chat (single model per tier) or standalone ChatGPT, Supermaven Chat unifies multiple LLM backends in a single hotkey-driven workflow with direct editor integration for change application.
Supermaven scores higher at 71/100 vs pinocchio at 45/100. pinocchio leads on ecosystem, while Supermaven is stronger on adoption and quality.
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vs alternatives: Supports multiple LLM backends (GPT-4o, Claude 3.5 Sonnet) in one interface with included credits, whereas GitHub Copilot Chat is single-model per tier and requires separate ChatGPT subscription for model switching; trade-off is credit limits and unknown overage pricing.
Supermaven Chat can automatically upload compiler diagnostic messages (errors, warnings) alongside code context to provide error-aware suggestions and fixes. The mechanism is described as 'automatically uploading your code together with compiler diagnostic messages,' but specific language/compiler support and the upload trigger mechanism are undisclosed. This feature is Chat-only and not available in inline completion.
Unique: Automatic compiler diagnostic upload in Chat for error-aware suggestions, versus competitors (Copilot, Tabnine) that require manual error context or have limited diagnostic integration. Supermaven's approach reduces friction but with undisclosed language/compiler support.
vs alternatives: Automatic diagnostic upload reduces manual context-gathering compared to manual copy-paste; trade-off is undisclosed language support and unclear upload trigger mechanism.
Supermaven offers a 30-day free trial of the Pro tier ($10/month), providing full access to 1M token context window, largest model, style adaptation, and $5/month chat credits. No credit card is required to start the trial (implied), and trial conversion to paid is automatic after 30 days unless cancelled. Trial terms and auto-renewal policy are not explicitly detailed.
Unique: 30-day free trial of Pro tier with full feature access (1M context, largest model, chat credits), versus competitors (Copilot 2-month free trial, Tabnine free tier only) with different trial lengths and feature access. Supermaven's approach is generous but with undisclosed auto-renewal terms.
vs alternatives: Full Pro feature access during trial compared to limited free tier; trade-off is undisclosed auto-renewal policy and potential unexpected charges if not cancelled.
Supermaven requires internet connectivity and server-side inference; no offline mode or local inference capability is mentioned or available. All code completion requests are sent to Supermaven's backend servers for processing, and responses are returned over the network. This creates a hard dependency on network connectivity and Supermaven's service availability; if the service is down or network is unavailable, code completion is not available.
Unique: Supermaven has no offline mode or local inference capability; all processing is server-side. GitHub Copilot also requires server-side inference, but Tabnine offers local inference options for some use cases. Supermaven's lack of offline capability is a significant limitation for developers with connectivity constraints.
vs alternatives: Supermaven's server-side-only approach is comparable to GitHub Copilot; Tabnine offers local inference options, making Tabnine more suitable for offline work. Supermaven's lack of offline capability is a weakness vs. Tabnine.
Analyzes recent code edits and inferred coding patterns to adapt inline suggestions to match team conventions, naming patterns, and structural preferences. The mechanism is implicit (not explicit fine-tuning) and operates only on Pro/Team tiers, suggesting pattern learning from editor activity rather than explicit configuration. Free tier uses a single base model without personalization.
Unique: Implicit style adaptation via editor activity analysis without explicit configuration, versus competitors (Copilot, Tabnine) that require manual style guides or explicit fine-tuning. Supermaven's approach is transparent to the user but also non-configurable and undisclosed in mechanism.
vs alternatives: Requires no manual style configuration compared to tools requiring explicit style guides; trade-off is lack of transparency and inability to control or export learned styles.
Delivers code suggestions to the editor inline as the developer types, with a claimed baseline latency of 250ms from keystroke to suggestion display. The system uses a cloud inference backend and local editor plugin to minimize round-trip time. Latency claim is positioned against an unnamed competitor (783ms), but methodology is undisclosed and no independent verification is provided.
Unique: Claimed 250ms latency via optimized cloud inference pipeline and editor plugin architecture, versus competitors with higher latency (783ms unnamed baseline). Actual differentiation is undisclosed; mechanism may involve request batching, model quantization, or edge caching, but specifics are not public.
vs alternatives: Faster than unnamed competitor (250ms vs 783ms claimed); trade-off is cloud dependency and unverified latency claim with no SLA or performance guarantee.
Provides native editor extensions for VS Code, JetBrains IDEs (IntelliJ IDEA, PyCharm, WebStorm, etc.), and Neovim, enabling inline suggestion rendering, hotkey-driven chat access, and compiler diagnostic integration directly within the editor. Each plugin variant is maintained separately and integrates with the editor's native autocomplete UI, keybinding system, and file context APIs.
Unique: Native plugins for three major editor ecosystems (VS Code, JetBrains, Neovim) with integrated chat and diff application, versus competitors (Copilot, Tabnine) that support broader editor ecosystems but with less deep integration in some cases. Supermaven's approach prioritizes depth over breadth.
vs alternatives: Deep integration with VS Code and JetBrains (native autocomplete UI, hotkey system) compared to web-based tools or lighter integrations; trade-off is limited editor support (no Sublime, Vim, Emacs) and undisclosed Neovim support details.
+5 more capabilities