Cotter: Open-source framework stress-tests robot policies for safety compliance

Cotter is a new open-source command-line tool that automates safety and compliance testing for robot control policies. At its core, it loads a trained robot policy (any learned controller that maps observations to actions) and runs it through a battery of standardized tests executed within MuJoCo, a widely-used physics simulation engine. The tests themselves fall into three categories: statistical safety tests (verifying the policy operates within safe bounds), regression tests (ensuring performance remains consistent across runs), and adversarial compliance tests (checking behavior under edge cases and perturbations). After running these tests, Cotter generates structured reports with pass/fail results backed by statistical confidence, making the output suitable for regulatory audits and compliance documentation.

The timing and framing of Cotter's release reflect a clear regulatory shift. Standards such as the EU Machinery Regulation and ISO 10218 increasingly demand that manufacturers and developers provide objective evidence that learned controllers behave as intended—a requirement that manual testing and informal validation cannot reliably satisfy. Cotter fills this need by automating the evidence generation process, allowing developers to treat policy validation as a repeatable, auditable step in their development pipeline.

From a usability standpoint, Cotter is designed for accessibility: it runs entirely on CPU (requiring no specialized hardware), installs via standard Python package management (pip install cotterbot), and follows the familiar pytest CLI paradigm that most Python developers already know. The full source code is published on GitHub at github.com/yih0nk/cotter, and a project website is available at cotter-website.vercel.app for documentation and community engagement. This open-source approach removes cost and licensing barriers, making compliance-grade testing available to researchers, small teams, and enterprises alike.

Robot control policies trained via machine learning are increasingly deployed in real-world settings, but regulators and safety-conscious organizations need reproducible evidence that these learned controllers behave predictably and safely. Cotter addresses this gap by providing a lightweight, standardized testing harness that treats a trained policy as a black box—taking observations and producing actions—and validates its behavior against known safety and regression benchmarks. The use of CPU-only computation and straightforward installation (pip-based, open-source distribution) lowers the barrier to entry for roboticists and organizations developing compliant systems. By grounding compliance in pytest-style automation and statistical guarantees rather than manual documentation, Cotter aligns the developer experience with modern software testing practices while meeting the specific audit requirements of emerging robotics regulations.

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