arXiv Machine Learning
Sep 21

Benchmarking World Models for Continual Learning on Compositional Tasks

The paper introduces a compositional continual learning benchmark for world models in robot manipulation, designed to isolate knowledge reuse from learning speed and capacity. Tasks are curated to combine previously seen action and perception components, allowing analysis of how different modalities affect reuse. Experiments show that modular world models better balance reuse and forgetting than conventional methods, yet none fully solve the challenge, highlighting the need for models explicitly built to reuse knowledge without forgetting.

By Haoyu Zhou, Joe Watson, Anson Lei, Ingmar Posner
arXiv AI
Oct 5

RIFAR: Reliability and Forgetting-Aware Replay for Continual Robot Learning

RIFAR is a new continual learning method for robots that uses reliability screening and drift-aware replay to mitigate forgetting while keeping storage low. It reconstructs past trajectories from short demonstration prefixes and employs a frozen inverse-dynamics model to verify action‑visual consistency. In experiments on LIBERO suites and real‑world tasks, RIFAR outperforms previous generative replay approaches, achieving high performance with only a small fraction of stored steps.

By Zirong Song, Zheng Lu, Haoran Liao, Wanqi Zhong, Yunhe Ni, Lijie Wang, Xiuying Chen
arXiv Computer Vision
Sep 22

Think Like a World Model, Act Like a VLA: Distilling World-Model Representations into Compact Robot Policies

The paper proposes a method to distill world‑model representations into compact Vision‑Language‑Action (VLA) policies. By adding a single feature‑alignment term during VLA training, a frozen world model’s internal features are cached and the student policy learns to match them, eliminating the need for a generative future‑rolling component. The resulting lightweight policy runs in 32 ms on an RTX 5090, achieving high performance on LIBERO and RoboCasa‑GR1, and transfers effectively to real robotic hardware.

By Trung Dao, Sankalp Yamsani, Jaden Park, Joohyung Kim, Yong Jae Lee