Benchmarks

Benchmarks#

Throughput and accuracy scripts live in benchmarks/.

Run locally#

uv sync --extra dev
uv run python benchmarks/bench_ik.py
uv run python benchmarks/bench_parity.py
uv run python benchmarks/bench_solvers.py
uv run python benchmarks/bench_constrained.py
uv run --with osqp python benchmarks/bench_osqp.py

What they measure#

Script

Metric

bench_ik.py

Solves/sec vs batch size (Panda, G1); --solver dls/lm/lbfgs

bench_parity.py

Agreement with CPU Mink (unconstrained DLS oracle)

bench_solvers.py

DLS / LM / L-BFGS relative cost and tracking error

bench_constrained.py

Constrained solver throughput, joint-limit violation vs DLS; box vs constrained-ineq path; accuracy vs mink daqp + ConfigurationLimit

bench_osqp.py

Inner box / general ADMM vs reference OSQP on standard QP examples

Recorded numbers are in benchmarks/RESULTS.md.

Constrained solver notes#

  • Box path (default for ConfigurationLimit + VelocityLimit): exact feasibility each ADMM step; tune admm_iters for optimality, not safety.

  • General inequality path (LinearInequalityLimit, or use_inequalities=True): needs enough admm_iters for tight feasibility.

  • Parity vs Mink uses limits=None / ConfigurationLimit on Panda (hinge/slide only).