Skip to content

skelarm

A lightweight, physics-based dynamics simulator for a configurable planar robot arm. skelarm focuses on kinematics and dynamics simulation without collision detection or complex shape rendering, treating the robot as a "skeleton" of links.

Features

  • Configurable Robot: Define arbitrary planar robots with custom link lengths, masses, inertias, and centers of mass, plus a configurable fixed base link. Support for TOML configuration files.
  • Kinematics:
    • Forward Kinematics (FK): recursive computation of joint, tip, and center-of-mass position, velocity, and acceleration.
    • Differential Kinematics: endpoint Jacobian and centripetal/Coriolis basis, with helpers to evaluate endpoint velocity and acceleration.
  • Dynamics (Planar, No Gravity):
    • Inverse Dynamics (ID) using Recursive Newton-Euler algorithm.
    • Forward Dynamics (FD) using mass matrix and Coriolis/centrifugal terms.
    • Fixed-step (simulate_controlled) and adaptive (scipy.integrate.solve_ivp) integration.
    • Note: Gravity is explicitly ignored as the robot operates on a horizontal plane.
  • Control: Trajectory-tracking laws (joint PD, inverse-dynamics feedforward, computed torque), human-like reaching controllers (virtual spring-damper and reference shaping), and joint-space MPC, configured from one combined TOML scenario and extensible at runtime.
  • Tasks: Reaching to a target, multiple-target reaching with live switching, periodic curve tracing (circle, ellipse, lemniscate, rose), and reference-trajectory tracking in task space or per joint — each a registered [task].type.
  • Trajectory tools: From-scratch interpolation (linear, natural cubic spline, barycentric Lagrange) and smoothing filters (first-order low-pass, Butterworth, moving average, Savitzky–Golay) for resampling recorded references.
  • Recording & Replay: Runs are recorded to a self-contained *.sklog.npz state log (geometry + channels + config) that re-runs reproducibly, replays in an analysis player, and exports headlessly to an .mp4 or animated .gif.
  • Visualization:
    • Static plotting with matplotlib.
    • Interactive PyQt6 GUIs: pose by joint sliders (FK) or click/drag the tip (IK); real-time dynamics and per-task simulators where you drag to apply disturbance forces; and a timeline player that draws the task overlay (target, curve, or reference).
  • Quality Assurance: Fully typed, tested with pytest and hypothesis, and linted with ruff.

Getting Started

Prerequisites

  • Python 3.12 or higher.
  • uv package manager (recommended) or standard pip.

Installation

  1. Clone the repository: 

    git clone https://github.com/hrshtst/skelarm.git
cd skelarm

  2. Install dependencies using uv (Recommended): 

    uv sync
    Or using make: 
    make install

    Alternatively, using pip: 

    pip install .

Usage Examples

TOML Configuration

You can define robot configurations in TOML files. See examples/simple_robot.toml, examples/four_dof_robot.toml, or examples/base_offset_robot.toml (which sets a non-zero base_length).

[skeleton]
base_length = 0.0  # Optional fixed base link offsetting the first joint along +x

[[skeleton.link]]
length = 1.0
mass = 2.0
inertia = 0.5
com = [0.5, 0.0]        # Center of mass [x, y] relative to joint
limits = [-180.0, 180.0]  # Joint limits [min, max] in degrees

[[skeleton.link]]
length = 0.8
# ...

[initial]
q = [30.0, 0.0]   # Initial joint angles (degrees), one per joint
# dq = [0.0, 0.0]  # Optional initial joint velocities (deg/s)

Skeleton keys live under the [skeleton] table so the robot can later share a single combined file with [task] / [controller] sections for reproducible runs; Skeleton.from_toml reads only the [skeleton] section and ignores the rest. (Legacy flat configs with top-level base_length / [[link]] are still accepted.)

The initial state is a run condition in its own [initial] section, so you can compare the same robot from different starting postures by swapping just that block. q (degrees) and the optional dq (degrees/second) each take one value per joint, and a length mismatch with the robot's DOF raises an error. [initial].q supersedes the older per-link q0 keys.

Joint limits are enforced: setting joint angles clamps them into each joint's [min, max] range and warns when a value is out of range. A link that omits limits defaults to [-180, 180] degrees, i.e. one full revolution — so an unspecified joint is capped at ±180°, not left unbounded.

Load it using Skeleton.from_toml:

from skelarm import Skeleton
skeleton = Skeleton.from_toml("path/to/robot.toml")

For the full per-link key reference and the [initial] schema, see the Robot Configuration guide.

4-DOF Simulation Example

Run a dynamic simulation of a 4-DOF robot loaded from a TOML file:

uv run python examples/simulate_four_dof.py

For an interactive version, launch the real-time GUI simulator and press/drag in the canvas to apply an external force at the tip (shown as a red arrow):

uv run python examples/interactive_dynamics.py

To simulate an arbitrary robot, use the generalized tool in tools/, which adds pause/resume, single-step, reset, a live viscous-friction control (joint damping that dissipates energy), a status panel (kinetic energy and tip position/speed), and an optional tip-trajectory plot when the window closes:

uv run python tools/dynamics_simulator.py examples/four_dof_robot.toml

Like the kinematics inspector it accepts --show-com, --pose, and --initial, plus --stiffness <N/m>, --friction <N·m·s/rad>, and --no-plot. By default the joint limits act as hard stops in the dynamics; pass --no-joint-limits to disable that and let the limits apply to kinematics (IK) only.

Replaying a Recorded Run

The dynamics simulator records the run (joint angles, velocities, torque, and the external tip force) and can export it as a *.sklog.npz state log (recording is on by default; use the Export… button). Replay it later — the motion is reconstructed and animated from the log without re-running the dynamics — and plot any channel versus time for analysis:

uv run python tools/player.py run.sklog.npz

Scrub the timeline, play/pause at a chosen speed (--speed), toggle the centers of mass (--show-com), and open per-channel plots with the Plot channels… button. When the log recorded an external tip force, it is drawn as a red arrow at the tip (toggle it with Show external force). The log embeds the robot geometry, so the file replays on its own. When the log embeds a task (any scenario simulator records it), the player also draws the task context — the target (the active one emphasized for multi-target tasks), the periodic curve, or the reference trajectory (a per-joint reference is shown in task space via forward kinematics) — each toggled with Show target(s) / Show reference.

Pass --export PATH to render the replay headlessly (no GUI window) to a video or animated GIF instead of opening the player — the format is taken from the extension (.mp4 or .gif), each frame is drawn by the same canvas (task overlay, centers of mass, and force arrow included), and --fps sets the output frame rate (--speed and --show-com apply too):

uv run python tools/player.py run.sklog.npz --export run.mp4            # headless mp4
uv run python tools/player.py run.sklog.npz --export run.gif --fps 24   # headless animated gif

Teaching a Trajectory

Author a joint trajectory by demonstration: grab the robot's tip with the left mouse button and drag it to teach a motion, recorded to a *.sklog.npz log that replays with tools/player.py. Recording starts on the first grab and stops at the max duration (or the Finish button), sampling at the configured rate; a plot of the recorded motion is shown afterward. Two modes turn the task-space teaching into joint angles:

uv run python tools/trajectory_recorder.py examples/four_dof_robot.toml                 # ik mode (default)
uv run python tools/trajectory_recorder.py examples/four_dof_robot.toml --mode dynamics  # force + forward dynamics
uv run python tools/player.py teach.sklog.npz                                            # replay the recording

In ik mode the tip tracks the cursor via the IK solver (--method); in dynamics mode the drag applies a tip force integrated under forward dynamics with viscous friction (--stiffness, --friction, with --no-joint-limits to drop the dynamics hard stop). --sample-rate and --duration configure the logger; --initial/--pose set the start pose, and an optional [task] draws a target. The log records per-joint angles and the tip path.

Reaching & Trajectory Control

A combined scenario file describes the robot ([skeleton] / [initial]), the reaching goal ([task]), and the controller ([controller]) in one place. examples/reaching.py runs a scripted reach and plots it; tools/reaching_simulator.py opens an interactive GUI where the controller drives the arm to the target (a purple marker) and you press/drag the left mouse button to apply an external force at the tip. Add --save PATH to run headlessly and write a *.sklog.npz for replay (handy for batch comparison):

uv run python examples/reaching.py                            # scripted reach + plot
uv run python tools/reaching_simulator.py examples/reach.toml              # interactive GUI
uv run python tools/reaching_simulator.py examples/reach.toml --save reach.sklog.npz   # headless batch
uv run python tools/player.py reach.sklog.npz                 # replay a saved run

The other task types have dedicated interactive simulators sharing the same controls (drag to perturb, Record / Export…, the --initial/--pose/--task/--controller overrides, and --save for a headless batch). Their runs replay in tools/player.py with the task overlay drawn:

uv run python tools/multi_target_simulator.py examples/multi_target.toml        # press 1–N to switch the active target live
uv run python tools/periodic_curve_simulator.py examples/periodic_curve.toml    # trace a closed curve (drawn behind the arm)
uv run python tools/trajectory_tracking_simulator.py track.toml                 # track a recorded tip / per-joint reference

The [controller].type selects the control law — trajectory tracking (computed_torque, inverse_dynamics_pd, joint_pd), human-like reaching (virtual_spring_damper, time_varying_stiffness, online_shaping, position_dependent_shaping, adaptive_shaping), or mpc — and the remaining keys are its gains. A [simulator].enforce_limits = false keeps the joint limits on the kinematics only, dropping the dynamics hard stop for the run (the resolved value is embedded for reproducibility; --no-joint-limits overrides it off for one run). The control library is also usable directly via skelarm.load_scenario and skelarm.run_scenario. The exported log embeds the full scenario config, so a run can be re-simulated later with skelarm.rerun_log (exactly for the deterministic controllers, within a small tolerance for MPC), or exported as an editable config (skelarm.export_scenario_toml, or tools/export_config.py) to tweak a parameter and re-run for comparison. See the Control Configuration guide for the full scenario schema and per-controller config keys, and the Trajectory Tracking and Reaching Control references for the theory.

Interactive Kinematics (FK & IK)

Launch the PyQt6 GUI to pose a robot arm with the joint sliders (forward kinematics) or by clicking/dragging in the canvas to move the tip (inverse kinematics):

uv run python examples/interactive_kinematics.py

To inspect an arbitrary robot, use the generalized tool in tools/, which takes a config path:

uv run python tools/kinematics_inspector.py examples/four_dof_robot.toml

It also accepts --method <ik-method>, --show-com, and an initial pose via --pose 20,45,60,30 (degrees) or --initial pose.toml (a TOML file with an [initial] table).

Basic Kinematics & Plotting

Run a script that defines a robot, computes its kinematics, and plots it using Matplotlib:

uv run python examples/basic_plotting.py

Dynamics Simulation

You can use the library to simulate robot motion. See src/skelarm/dynamics.py and tests/test_dynamics.py for API usage.

from skelarm import LinkProp, Skeleton, simulate_robot
import numpy as np

# Define a single link
link = LinkProp(length=1.0, m=1.0, i=0.1, rgx=0.5, rgy=0.0, qmin=-np.pi, qmax=np.pi)
skeleton = Skeleton([link])

# Initial state
skeleton.q = np.array([0.0])
skeleton.dq = np.array([0.0])

# Simulation parameters
time_span = (0.0, 1.0)
def control_torques(t, skel):
    return np.array([0.0]) # Zero torque

# Run simulation
times, q_traj, dq_traj = simulate_robot(skeleton, time_span, control_torques)

Running Tests

This project uses pytest for unit testing and hypothesis for property-based testing of physics consistency.

To run the full test suite:

make test
# OR
uv run pytest

To run tests with coverage report:

make test-cov

Development

We use ruff for linting and formatting, and basedpyright and mypy for static type checking.

  • Linting: make lint
  • Formatting: make format
  • Type Checking: make type-check
  • Run all checks: make all

Documentation

The project documentation is built using MkDocs.

  • Build Documentation: make docs-build
  • Serve Documentation Locally: make docs-serve

License

GPLv3

AI Assistance & Development Workflow

This project is developed with the assistance of AI coding agents. The AI is also used to generate commit messages and parts of the documentation, including API and theoretical reference sections.

Workflow: 1. Context & Theory (Human): The maintainer, Hiroshi Atsuta, establishes the project guidance in AGENTS.md and writes the theoretical background implemented as documentation in docs/reference/. 2. Scaffolding (AI): The AI assistant uses these documents and the constraints defined in AGENTS.md to implement code scaffolding and initial logic. 3. Review & Revision (Human): The maintainer reviews, tests, and revises the generated code to ensure quality and correctness. This cycle is repeated during the development.

Responsibility: All responsibilities for the code hosted in this repository lie with the maintainer. The AI serves strictly as an implementation assistant; final architectural decisions and code quality are human-led.

Feedback: If you identify problems, or find code that appears to be unoriginal or rights-protected, please notify the maintainer immediately by filing an issue.

Contributor Policy: External contributors are welcome to use AI tools for assistance, provided they adhere to the same standard of review and responsibility. If you use AI to generate code for a Pull Request, please disclose it in the PR description and ensure you have thoroughly reviewed and tested the code.