dsh-robotic-harness

🤖 Robotic Harness

An embodied-intelligence research plugin suite for DeepSeek Harness

Put robot assets, simulation, capability orchestration and failure evidence into one Agent workflow — from CAD/URDF inspection to MuJoCo pick-and-place, fault injection, evidence-based diagnostics and reproducible experiment bundles. 🧪 Testers & contributors: this project is at an early Demo stage. It has been validated only in a limited local environment (Windows + Anaconda Python 3.10 + DSH 0.1.0-rc.6). ROS 2, CAD, real-robot and other OS/hardware setups have not been fully tested — please forgive rough edges and report any issues you hit. Everyone is welcome to test, modify and extend this plugin suite, and to combine your own robot-related plugins (ROS 2 / CAD / vision / control / VLA / ...) with this suite to build one bigger, complete robot plugin bundle together — every independent module can be published and contributed separately. See CONTRIBUTING.md. 📑 Table of contents ✨ Features 📸 Screenshots 🚀 Quick start (30 seconds) 🏗️ Architecture 📦 Install as a DSH plugin 🧩 Tool & skill surface 🎯 The demo (MuJoCo pick-and-place) ⚠️ Known limitations 🌊 Future vision 📂 Repository layout 📚 Documentation 🤝 Contributing 📄 License ✨ Features

A real demo run (left to right): scene render · joint tracking · trajectory with target zone · tracking error.

MuJoCo scene (offscreen render) Joint positions: target vs actual Trajectory & target zone Tracking error over time 🚀 Quick start (30 seconds) No DSH needed — pure Python. Requirements: Python ≥ 3.10 with mujoco, numpy, opencv-python, matplotlib, pytest (the Anaconda python3.10 env is recommended).

Output lands in examples/demo-output/: 🏗️ Architecture

Every tool in the bundle delegates to the Python worker over stdio (python -m robotic_harness_worker --input -). Runs, telemetry, charts and reports are written to the workspace's .rh/ directory by default. One-shot processes give crash isolation: a worker failure never takes down DSH. 📦 Install as a DSH plugin

Requirements: DSH CLI (@deepseek-ai/dsh ≥ 0.1.0-rc.6), pnpm, a Python 3.10 environment.

Alternatives: the bundle can also be installed from a tarball (dsh plugin add ./robotic-harness-dsh-bundle-0.1.0.tgz), from git (dsh plugin add github:dingkaihu63/dsh-robotic-harness), or from npm once published. Packaging/publishing steps and checks live in docs/publishing.md.

Then just ask the Agent: “Run the Robotic Harness pick-place demo: inspect the demo arm, run one clean simulation and one fault-injected simulation, diagnose the failure, export the evidence bundle and generate the report.”

The Agent will drive the rh_* tools step by step and keep every result as evidence. 🧩 Tool & skill surface