CIDER: Continual Interactive Distillation for Embodied Reinforcement Learning

2026-08-22

AI Summary

研究通过CIDER框架解决持续学习中技能遗忘问题，采用交互式蒸馏和梯度路由方法保持旧技能同时学习新技能。

Abstract

Human-in-the-loop real-world reinforcement learning enables rapid acquisition of effective robotic manipulation policies for individual tasks, often within tens of minutes. Yet it remains unclear how to extend this paradigm to continual learning, where a single policy must acquire new skills without losing previously learned behaviors. Existing real-world continual learning methods do not explicitly constrain prior behaviors, leading to severe catastrophic forgetting. We introduce Continual Interactive Distillation for Embodied Reinforcement Learning (CIDER), a continual reinforcement learning framework that freezes the accumulated historical policy as a teacher before learning each new task and interleaves task learning with distillation-based retention. We further introduce gradient routing to separate the gradients used for acquiring new tasks from those used for preserving prior behaviors. We evaluate our method with a single shared actor on six real-world household and industrial manipulation tasks. Interactive Distillation maintains high measured success on previously learned tasks across our six-task real-robot sequence while acquiring each new task in 10 to 20 minutes, whereas every baseline forgets at least one previous task. Additional ablations reveal the key design choices that govern the tradeoff between stability and plasticity in real-world continual reinforcement learning.

Problem

• continual learning
• catastrophic forgetting
• reinforcement learning
• interactive distillation

Innovation

• Continual Learning
• Interactive Distillation
• Gradient Routing
• Catastrophic Forgetting