trainnr-mjlab
PulseGate's liveness check found it on 7 Oct 2026; it is registered on GitHub and PyPI and has been in the index since 7 Oct 2026. How this is checked
trainnr-mjlab is a Python package for training robotics reinforcement-learning systems with identified actuator physics represented as MuJoCo/mjlab actuators. It supports randomization from measured intervals and linting that detects silent no-op configurations for sim-to-real workflows.
Inferred · not functionally tested
Overview
6 featuresPurpose: Reducing sim-to-real gaps and silent configuration errors in robotics reinforcement-learning training.
Inferred · not functionally tested
Audience: robotics and reinforcement-learning developers
Inferred · not functionally tested
Functions: analytics, monitoring, agents
Inferred · not functionally tested
Interfaces: API: indicated (inferred, not tested) · MCP: indicated (inferred, not tested) · CLI: indicated (inferred, not tested) · Self-hosting: indicated (inferred, not tested)
Recorded constraints: pricing: unknown · license: FSL-1.1-ALv2 · platforms: CLI · deployment: cli, self_hosted, api_only
Constraint provenance is unknown; confirm requirements with the publisher.
Record sources: pypi.org · github.com. These links do not verify the individual claims.
trainnr-mjlab sits in PulseGate's Frameworks & SDKs category. Inferred · not functionally tested: It focuses on reducing sim-to-real gaps and silent configuration errors in robotics reinforcement-learning training. Inferred · not functionally tested: trainnr-mjlab is a B2B product aimed at robotics and reinforcement-learning developers. Basis unknown · not verified: It ships for the command line and API, and it can be self-hosted.
Trainnr-AI builds and maintains trainnr-mjlab, and it first shipped in 2026. The project is developed in the open on GitHub with 844 commits in the last 90 days. Inferred · not functionally tested: Among its 6 catalogued features are actuator modeling, muJoCo integration, and parameter randomization. Inferred · not functionally tested: Catalogued interfaces include an MCP server.
Summary written by a language model from the project’s public pages.
Tasks: Inferred · not functionally tested
- Actuator modeling
- MuJoCo integration
- Parameter randomization
- Measured intervals
- Configuration linting
- Sim-to-real training
Topics: Inferred · not functionally tested
Built with & integrations
- Claude Code
- commit 7e94137e088e · since Oct 2026
Trust & compliance
Indexing history
2What PulseGate has recorded for this listing
- Indexed7 Oct · 10:21 UTCtrainnr-mjlab seen via PyPI Bulk EnumeratorSource: PyPI Bulk Enumerator · Open
Frequently asked questions about trainnr-mjlab
- What does trainnr-mjlab do?
- Inferred · not functionally tested: Trainnr-mjlab focuses on reducing sim-to-real gaps and silent configuration errors in robotics reinforcement-learning training. It is catalogued under Frameworks & SDKs on PulseGate.
- Who should use trainnr-mjlab?
- Inferred · not functionally tested: trainnr-mjlab is a B2B product built for robotics and reinforcement-learning developers.
- What platforms does trainnr-mjlab run on?
- Basis unknown · not verified: trainnr-mjlab runs on the command line and API. It can also be self-hosted.
- Is trainnr-mjlab still active?
- PulseGate's liveness check found it on 7 Oct 2026. Its GitHub repository shows 844 commits in the last 90 days.
- What are alternatives to trainnr-mjlab?
- Similar projects tracked by PulseGate include tennessee-code, spotify, and react-mongol.tennessee-codespotifyreact-mongol
- Who makes trainnr-mjlab?
- trainnr-mjlab is developed by Trainnr-AI.
- How long has trainnr-mjlab been around?
- trainnr-mjlab first shipped in 2026.
- Is trainnr-mjlab open source?
- Basis unknown · not verified: trainnr-mjlab has a public GitHub repository.
Also in Frameworks & SDKs
Same category — not a similarity match