ssik 1.1.1

Analytical inverse kinematics for 6R and 7R revolute arms. Returns every IK branch at machine precision.

ssik

Analytical inverse kinematics for 6R and 7R revolute robot arms. Each arm becomes a single self-contained Python module that returns every IK branch with FK closure well below typical robot repeatability — and tightenable to machine precision when needed.

Install

pip install ssik

Python 3.11+. Wheels for Linux x86_64, macOS arm64, macOS x86_64, Windows x86_64.

Quickstart

from ssik.prebuilt import franka_panda_ik
import numpy as np

T_target = np.eye(4); T_target[:3, 3] = [0.5, 0.1, 0.3]
sols = franka_panda_ik.solve(T_target)      # every analytical IK branch

sols is a list[Solution]. Each Solution carries q (the joint vector), fk_residual (‖FK(q) − T‖), and which polish path fired. Empty list = pose is unreachable.

The artifact model

ssik is built around per-arm artifact modules. Each artifact is a single .py file with the per-arm KinBody constants, the dispatched solver, and any cached symbolic preprocessing already baked in. No URDF parsing, no urchin, no sympy on the runtime import path. A robot stack that imports <arm>_ik.py carries no algorithmic complexity beyond what the build pipeline already resolved.

This is the same idea OpenRAVE's IKFast had — generate per-arm specialised IK code at design time, run pure numeric at deployment — but without IKFast's brittleness on non-Pieper geometries.

There are two artifact paths:

Use a prebuilt arm (ssik.prebuilt)

The wheel ships 8 ready-to-import artifacts. Each was built against a specific URDF (or extracted spec); T_target is the pose of EE_LINK expressed in BASE_LINK:

Module	Arm	Class	base_link	ee_link
ur5_ik	Universal Robots UR5	three-parallel 6R	base_link	ee_link
puma560_ik	KUKA Puma 560	Pieper 6R (spherical wrist)	base_link	wrist_3_link
jaco2_ik	Kinova JACO 2	non-Pieper 6R	base_link	ee_link
iiwa14_ik	KUKA iiwa LBR 14	SRS 7R	world	tool0
gen3_ik	Kinova Gen3 7-DOF	approximate-SRS 7R	base_link	end_effector_link
franka_panda_ik	Franka Panda	anthropomorphic 7R	base_link	ee_link
rizon4_ik	Flexiv Rizon 4	non-SRS 7R	base_link	flange
kassow_kr810_ik	Kassow KR810	non-SRS 7R	base	end_effector

from ssik.prebuilt import iiwa14_ik
sols = iiwa14_ik.solve(T_target)

Every prebuilt exposes BASE_LINK, EE_LINK, DOF, and T_HOME (the 4×4 home pose, FK at q = np.zeros(DOF)) as module constants. Use them to verify the baked geometry matches your robot:

from ssik.prebuilt import franka_panda_ik
print(franka_panda_ik.BASE_LINK, "→", franka_panda_ik.EE_LINK, "(", franka_panda_ik.DOF, "DOF)")
# base_link → ee_link ( 7 DOF)
print(franka_panda_ik.T_HOME[:3, 3])
# array([0.088, 0., 0.926])     ← Franka home pose; matches the spec

When a prebuilt is right vs when to ssik build

The 8 prebuilts cover nominal manufacturer geometry with a bare flange. They work when:

• You're using the same URDF source we built against (ros-industrial, manufacturer reference, etc.)
• Your robot's calibration matches the nominal kinematic parameters
• Your end-effector is the flange itself — no gripper, suction cup, or custom tool past it
• Your URDF link names match what we baked (see the table above)

If any of those is false — and especially if you're a 7R arm with anything attached past the flange — build your own:

pip install ssik[urdf]
ssik build <your.urdf> --base <your_base_link> --ee <your_actual_tool_link>
# → <your_arm>_ik.py

ssik build reads your exact URDF, picks the right solver via the same dispatcher we use, and emits a single-file artifact correct for your kinematic chain. That artifact's import / API / public constants are identical to the prebuilts'.

For trajectory tracking and IK-based teleop, the canonical pattern is "give me the IK closest to where the robot is now":

# Robot's current configuration (from joint sensors, last command, etc.).
q_current = np.array([0.0, -0.5, 0.0, 0.7, 0.0, 1.2, 0.0])

# Target pose updates every control tick (VR controller, planner, etc.).
T_target = ...

# max_solutions=1 + q_seed: solver visits lock-samples in nearest-to-seed
# order and short-circuits on the first in-limits branch (~5-6× faster
# than the full sweep on 7R jointlock arms; sub-ms on 6R / SRS arms).
sols = franka_panda_ik.solve(T_target, max_solutions=1, q_seed=q_current)
q_command = sols[0].q if sols else q_current

Build an artifact for your own arm

For any arm not in the prebuilt set, run ssik build once against the URDF:

ssik build my_arm.urdf --base base_link --ee tool0
# → my_arm_ik.py

Build time depends on solver class:

• <1 s for tier-0 closed-form (UR-class, Pieper, SRS-class 7R)
• ~30 s for non-Pieper 6R (Raghavan–Roth symbolic derivation)
• 7–20 min for non-SRS 7R (cached Husty–Pfurner per lock sample)

Ship the emitted .py alongside your robot stack. Once built, use it exactly like a prebuilt:

import my_arm_ik
sols = my_arm_ik.solve(T_target)

Re-run ssik build after pip install -U ssik if you want the latest solver fixes. Old artifacts keep working — they're frozen against the ssik version that built them. ssik build requires the URDF extras: pip install ssik[urdf].

Development path: Manipulator.from_urdf (not for deployment)

For one-off experiments before committing to a build artifact, ssik also exposes the runtime classifier as a Python class:

import ssik
arm = ssik.Manipulator.from_urdf("my_arm.urdf", base="base_link", ee="tool0")
sols = arm.solve(T_target, max_solutions=1, q_seed=q_current)

Every fresh process re-runs URDF parsing, topology classification, and (for non-Pieper sub-chains) first-call sympy preprocessing — so this path is strictly slower than the build-artifact path in production and requires urchin + sympy on the runtime path (pip install ssik[urdf]). Once dispatch is settled, switch to ssik build.

Contributors extending ssik's own test fixtures (vs deploying for their own arm) use ssik add-arm; see CONTRIBUTING.md.

What solve() returns

A list[Solution]. Each Solution has:

• q — joint-angle vector (length DOF)
• fk_residual — ‖FK(q) − T‖_F (Frobenius norm against the original URDF / spec FK)
• refinement_used — "none" or "lm" if Levenberg–Marquardt polish fired

A single 6-DOF target pose admits up to 16 analytical IK branches (8 typical for a Pieper-class arm: 4 shoulder × 2 elbow, with the wrist deterministic). For 7R redundant arms the IK is a 1-parameter family; ssik discretises it into 32–256 branches per pose depending on the swivel-sample count.

By default solve() runs respect_limits=True: out-of-URDF-limit branches are dropped (with a q ± 2π rescue pass first). On 7R jointlock arms the limits filter runs during the lock-sweep so max_solutions=1 short-circuits on the first in-limits candidate rather than wasting samples on branches the postprocess would discard. Pass respect_limits=False for the raw geometric set.

The allow_refinement=True opt-in runs LM polish per algebraic candidate at a few hundred microseconds per branch — useful when an algebraic candidate lands just above fk_atol near a kinematic singularity.

Diagnosing an empty result — explain=True

If solve() returns [], you can attribute the failure with explain=True instead of guessing:

import ssik
arm = ssik.Manipulator.from_urdf("my_arm.urdf", base="base_link", ee="tool0")
sols, diag = arm.solve(T_target, explain=True)
if not sols:
    print(diag.summary())
    # solver: ikgeo.three_parallel (tier 0)
    # dispatch: Three consecutive parallel axes at joints (1, 2, 3) ...
    #   -> 0 raw candidates: pose appears unreachable
    #      (or outside this solver's analytical envelope)

The Diagnostic record distinguishes:

• Unreachable (raw_candidates == 0) — pose is outside the solver's analytical envelope
• All-filtered (raw_candidates > 0, final_count == 0) — try respect_limits=False for the raw geometric set
• Capped (dropped_by_max_solutions > 0) — pass a larger max_solutions

Available on ssik.Manipulator.solve today; per-prebuilt explain mode tracked in #265.

Tuning knobs

TolerancePolicy — six thresholds, one object

solve() accepts an optional policy= kwarg. The default ssik.DEFAULT_TOLERANCE_POLICY works for every shipped fixture; reach for a custom policy when a real arm's URDF has structural near-degeneracies (axes that almost but not exactly meet) or when you want tighter / looser FK closure than the defaults provide.

from ssik import TolerancePolicy, DEFAULT_TOLERANCE_POLICY

policy = TolerancePolicy(
    axis_parallel=1e-8,         # ||a × b||: when two axes are "parallel"
    axis_intersect=1e-8,        # perpendicular distance: when two lines "meet"
    subproblem_feasibility=1e-9,# is_ls boundary inside SP1-SP6
    subproblem_numerical=1e-5,  # FK-closure filter on algebraic candidates
    subproblem_degeneracy=1e-12,# rank-drop threshold; below this, return []
    subproblem_dedup=1e-3,      # angle-space tolerance for collapsing duplicates
)
sols = my_arm_ik.solve(T_target, policy=policy)

The fields are named for why they exist so log messages can say "SP6 sign branch rejected: closure 1.2e-4 > subproblem_numerical 1e-5" instead of citing magic numbers.

How to read fk_residual — and how to tighten it

fk_residual is ‖FK(q) − T_target‖_F — a Frobenius norm of a 4×4 SE(3) matrix mixing rotation (radians, dimensionless when small) and translation (meters). For a typical 1 m-reach arm:

fk_residual	Position-error scale	Note
1e-3	1 mm	visible to the naked eye
1e-4	0.1 mm	typical robot repeatability (manufacturer spec)
1e-5 (default)	10 µm	sub-repeatability; fine for control
1e-9	1 nm	math / analysis territory
1e-13	0.1 pm	float64 epsilon

The default subproblem_numerical = 1e-5 is intentionally pragmatic — already two orders below what any physical robot can mechanically repeat, but cheap enough that all 8 prebuilts hit it without LM polish. Most control / planning users want exactly this default.

To get machine precision (RL training, differentiable IK, sample-based planning, math validation), opt in:

from ssik import TolerancePolicy
from ssik.prebuilt import franka_panda_ik

tight = TolerancePolicy(
    axis_parallel=1e-8,
    axis_intersect=1e-8,
    subproblem_feasibility=1e-9,
    subproblem_numerical=1e-9,         # ← 4 orders tighter
    subproblem_degeneracy=1e-12,
    subproblem_dedup=1e-3,
)
sols = franka_panda_ik.solve(T_target, policy=tight, allow_refinement=True)
# every returned IK FK-closes ~3e-10 (~0.3 nm position error)

The allow_refinement=True flag engages Levenberg-Marquardt polish on candidates that don't meet subproblem_numerical. On the jointlock 7R arms (Franka, Rizon 4, Kassow KR810) this lifts worst-case FK from ~5×10⁻⁶ (default) to ~3×10⁻¹⁰ (tight + LM). Cost: a few hundred microseconds per polished candidate. Sub-repeatability arms (UR5, Puma 560, JACO 2, iiwa14, Gen3) already hit machine precision at the default policy and don't need the opt-in.

Per-arm worst-case behaviour under both policies is documented in docs/arm_coverage.md.

ssik.postprocess — composable filters

solve() returns the geometric IK set. For application-specific filtering, four helpers in ssik.postprocess compose into the typical "robot-aware IK" pipeline:

from ssik.postprocess import (
    respect_limits, wrap_to_limits, nearest_to_seed, take_first,
)

sols = my_arm_ik.solve(T_target, respect_limits=False)  # raw geometric set
sols = wrap_to_limits(sols, my_arm_ik._KB)              # try q ± 2π to bring in
sols = respect_limits(sols, my_arm_ik._KB)              # drop anything still outside
sols = nearest_to_seed(sols, q_current)                 # sort by wrap-to-pi distance
sols = take_first(sols, k=4)                            # top-k after ranking

By default solve() already runs wrap_to_limits + respect_limits; the standalone helpers exist for callers who want a different order, a different metric, or to add their own filters (collision-aware filtering, dexterity scoring, custom seed metrics) between the layers.

Out of scope: collision filtering (use FCL or similar at the application layer) and continuous-trajectory smoothness (typically a separate planner concern).

How it compares

Numerical-IK libraries take a seed, run damped least-squares to a single converged configuration, and stop. ssik returns every analytical branch — with FK closure well below typical robot repeatability by default, tightenable to machine precision (see Tuning knobs below). Branch enumeration matters for motion planning (try every branch, pick the one with best clearance), for dexterity analysis (the manipulability ellipsoid is per-branch), and for trajectory continuation across kinematic singularities.

EAIK (Ostermeier 2024) is the canonical Python wrapper around C++ subproblem-decomposition solvers. It's analytical on the kinematic families it recognises and refuses everything else. Numbers below from examples/04_compare_vs_eaik.py over 100 random reachable poses per arm, Apple M3 single-thread, mean ± 95% CI via 1000-resample bootstrap. FK residual is the Frobenius norm ‖FK(q) − T‖ against the original URDF / spec FK.

Arm (class)	EAIK	ssik
UR5 (Pieper 6R, three-parallel)	5 ± 0 µs / FK 2e-15 / 4 sols	556 ± 12 µs / FK 2e-9 / 4 sols
Puma 560 (Pieper 6R, spherical wrist)	5 ± 1 µs / FK 3e-14 / 8 sols	245 ± 3 µs / FK 2e-14 / 8 sols
JACO 2 (non-Pieper 6R)	refuses ("6R-Unknown Kinematic Class")	1.02 ± 0.04 ms / FK 3e-6 / 2 sols
iiwa14 (SRS 7R)	refuses ("only 1-6R robots are solvable")	5.10 ± 0.07 ms / FK 4e-13 / 96 sols
Gen3 (approximate-SRS 7R, 12 mm offset)	refuses ("only 1-6R")	41.83 ± 1.15 ms / FK 1e-12 / 47 sols
Franka Panda (anthropomorphic 7R)	refuses ("only 1-6R")	28.03 ± 2.57 ms / FK 7e-13 / 9 sols
Rizon 4 (non-SRS 7R)	refuses ("only 1-6R")	30.88 ± 8.80 ms / FK 4e-9 / 35 sols
Kassow KR810 (non-SRS 7R)	refuses ("only 1-6R")	28.06 ± 10.63 ms / FK 7e-8 / 24 sols

EAIK is ~100× faster than ssik on Pieper-class 6R — that is its native sweet spot, and ssik does not try to compete there. The interesting cells are the refuses ones: non-Pieper 6R (JACO 2) and every 7R arm. Those are the geometries ssik exists for. The "refuses (...)" strings are EAIK's actual error messages, captured verbatim by the bench harness. A numerical-IK comparison (MINK) is tracked separately in #236.

Under the hood

The algorithmic ingredients are not novel — Raghavan–Roth (1990), Manocha–Canny (1994), Singh–Kreutz (1989), Husty–Pfurner (2007). What's new is making the textbook pipelines survive on real ill-conditioned arms (AE-3 leftvar selection on JACO 2 drops cond(m_quad) from 3.75 × 10^16 to 127), composing them with a uniform dispatch layer, and packaging the whole thing as a deployable artifact.

Cython hot loops cover the leaf primitives (POE forward kinematics, the Levenberg–Marquardt polish and analytical Jacobian); the rest is pure Python so it stays inspectable.

Bulletproof testing: every solver lands with N-way cross-solver agreement on shared fixtures, FK closure ≤ 1e-10 on every retained IK, 500+ Hypothesis-fuzzed random poses per fixture, and an explicit speed bench that has to clear a regression gate. The current suite has 1300+ tests across 11 fixture arms. Negative-result spikes (a Cython estimate that misses by 2-5×, a codegen-bake on a part that's 0.3% of runtime) are published as closed issues with profile data so the next contributor doesn't repeat the path.

Documentation

Full docs site: https://personalrobotics.github.io/ssik/

• Quickstart — install, prebuilts, trajectory tracking, explain mode
• Setting up your robot — URDF readiness, --base/--ee selection, tool baking, verification
• Arm coverage — per-arm fixtures, speeds, FK floors
• Architecture — solver tier catalog, dispatch flow, algorithmic lineage
• API reference — Manipulator, Solution, Diagnostic, TolerancePolicy
• Semver policy — what's public, what counts as breaking
• CONTRIBUTING.md — repo layout, dev setup, testing discipline

Related libraries

ssik does not compete with these on the arms they cover. Pick the right tool for your geometry.

• EAIK (Ostermeier 2024) — Python wrapper around C++ subproblem-decomposition solvers. Analytical, returns all branches on Pieper-class 6R and canonical SRS 7R (with a manual joint lock). Refuses arms outside its recognised kinematic families. Directly benchmarked in the table above.
• IK-Geo (Elias–Wen 2022/2025) — the reference C++/Rust implementation of subproblem decomposition. Same coverage profile as EAIK. Has Python bindings (ik-geo on PyPI); currently pins pyo3==0.20.3 so the wheel is incompatible with Python 3.13 — track upstream for an update.
• IKFast (Diankov 2010, part of OpenRAVE) — the original analytical-IK codegen tool. Symbolic preprocessing in sympy → per-arm C++. Works well on the kinematic families it was tuned for (Pieper-class 6R, spherical-wrist 7R via joint lock); the symbolic pipeline fails on modern sympy for non-Pieper geometries (mpmath.polyroots NoConvergence, Matrix.inv / Matrix.det stalls). LGPL-licensed.
• MINK (Zakka) — Mujoco-native numerical IK via damped least-squares. Iterative, takes a seed, converges to a single configuration. Handles any kinematic geometry but returns one IK, not all branches, and FK closure is proportional to the convergence tolerance (typically 1e-3 to 1e-6 rather than machine precision).
• TracIK (Beeson & Ames 2015) — combined SQP / pseudoinverse Jacobian solver; the ROS Industrial default numerical IK. URDF-native. Same one-branch-per-seed semantics as MINK. The maintained Python binding (pytracik) ships a broken arm64 wheel; the ROS-native binding works fine inside ROS.
• KDL-LMA — OROCOS KDL's Levenberg-Marquardt numerical IK. Older and less robust than TracIK or MINK on the same problem class.

License

BSD-3-Clause. The library incorporates clean-room reimplementations of algorithms from BSD-3-licensed IK-Geo (Elias–Wen 2022/2025) and from the academic publications of Raghavan–Roth (1990), Manocha–Canny (1994), Singh–Kreutz (1989), and Husty–Pfurner (2007). Algorithmic lineage is documented in module docstrings.

Citation

If you use ssik in academic work, please cite it. Machine-readable metadata is in CITATION.cff; GitHub renders that as a "Cite this repository" button on the repo sidebar.

@software{ssik,
  author = {Srinivasa, Siddhartha},
  title  = {ssik: analytical inverse kinematics for 6R and 7R revolute arms},
  url    = {https://github.com/personalrobotics/ssik},
  year   = {2026},
}