physlink 0.1.2

Backend-agnostic adapter library for physical simulation ML

PhysLink

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PhysLink bridges physical simulators and deep RL adapters in one pip install.

Plug your robot trajectories into DreamerV3 (and future backends) without writing boilerplate space definitions, checkpoint logic, or compliance checks. PhysLink handles the plumbing — you keep the science.

Why PhysLink

Without PhysLink	With PhysLink
Hand-write observation/action space mappings per framework	ObservationSpace.from_proprioception(joints=7)
Debug silent OOM on Colab at step 8 000	physlink.doctor() catches it before you start
Lose 3h of T4 training on session disconnect	Auto-checkpoint every N steps, resume on reconnect
Manually verify energy conservation after adaptation	register_invariant + compliance_report()
Stare at loss curves to diagnose model drift	Triptych GIF — Imagination / Real / Difference in one call

Install

pip install physlink

Works on Google Colab out of the box. No CUDA required for diagnostics and space definitions.

Quick example

import physlink

# 1. Verify your environment (< 15 s)
physlink.doctor()

# 2. Define spaces from your robot config
obs = physlink.ObservationSpace.from_proprioception(joints=7, include_velocity=True)
act = physlink.ActionSpace.continuous(dims=7, bounds=(-1.0, 1.0))

# 3. Adapt
adapter = physlink.DreamerV3Adapter(obs, act)
adapter.fit(trajectories, steps=10_000)

# 4. Validate physics compliance
physlink.register_invariant(adapter, "energy", fn=energy_fn, tolerance=0.05)
report = adapter.compliance_report()
report.plot()

# 5. Visualise
adapter.visualize(trajectories)   # → triptych GIF
adapter.export("./run-01/")

Documentation

Full docs at Denis-hamon.github.io/physlink — includes API reference, lab adoption guide, and domain-scientist quickstart.

Status

v0.1.x — public API stable across minor versions (see CHANGELOG).
test-cpu CI passes on every PR. GPU benchmarks run on release tags.

Contributing

See CONTRIBUTING.md. Issues and PRs welcome — use the provided templates.