RL‑100 Merges Imitation and Reinforcement Learning for High‑Performance Robot Manipulation

RL‑100 Framework

RL‑100 unifies imitation learning (IL) and reinforcement learning (RL) under a truncated proximal policy optimization (PPO) objective applied to a diffusion‑based controller. The training pipeline consists of three stages:

Collect human tele‑operation data and train a stable IL policy that captures basic grasping and motion behaviors.

Apply offline RL to the same dataset to fine‑tune the policy while preserving safety via the truncated PPO surrogate.

Perform a brief online RL phase in the real environment to further adjust the controller.

To meet low‑latency deployment requirements, a lightweight consistency distillation compresses the multi‑step diffusion process into a single‑step controller, enabling high‑frequency control.

Real‑World Evaluation

The authors evaluated RL‑100 on eight diverse manipulation tasks:

Object pushing

Bowling

Liquid pouring

Towel folding

Screw tightening

Juicing

Dual‑arm box folding

Across 1,000 evaluation trials the framework achieved a 100 % success rate on every task and maintained long‑term stability. Compared with a baseline IL policy and skilled human operators, RL‑100 reduced failure behaviors, produced smoother motions, and completed tasks faster; in several tasks the completion time matched or exceeded human performance.

Efficiency Analysis

Deployment‑time measurements show that the distilled single‑step controller reduces latency relative to the original diffusion process, resulting in lower overall task completion times across the suite.

Broader Implications

The study demonstrates that starting from strong human priors and applying real‑world RL fine‑tuning yields policies that are both reliable and efficient enough for practical deployment. Ongoing work aims to extend the post‑RL fine‑tuning to large vision‑language‑action models for more complex tasks.

Paper link: https://www.science.org/doi/10.1126/scirobotics.aed6267

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来源：ScienceAI
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