rail-berkeley/hil-serl

Health v2 · maintenance only

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Adoption not part of the score

1487 stars · 205 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

HIL-SERL is a Python library suite for training reinforcement learning policies for precise robotic manipulation using human demonstrations and human-in-the-loop corrections. It includes environment wrappers, an actor-learner training infrastructure, and examples for real robot arms such as the Franka.

Use cases

• train RL policies for robotic manipulation from demonstrations
• fine-tune robot policies with human corrections during training
• run actor-learner asynchronous RL on real robot hardware
• control a Franka arm with impedance-based gym environments
• achieve near-perfect success rates on dexterous manipulation tasks

When to choose

• you need sample-efficient RL for real-world robot manipulation
• you have a Franka arm or compatible robot and want human-in-the-loop training
• you want a research-grade JAX-based RL stack for robotics

When to avoid

• you need simulation-only RL without robot hardware
• you want a plug-and-play product rather than a research codebase
• your robot is not supported by the provided gym environments

Facets

library · maturity active

machine-learning reinforcement-learning robotics sdk robotics machine-learning reinforcement-learning python human-in-the-loop robotic-manipulation jax franka-arm rl-training linux gpu

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markdown · JSON · MCP: product_card(name="rail-berkeley/hil-serl")