Year: 2026
Type: conference-paper
Abstract: VLA models have emerged as a powerful paradigm for transferring semantic knowledge from web-scale data to physical robotic control.However, current single-frame architectures suffer from intrinsic limitations: temporal myopia that discards historical dynamics, reasoning gaps between high-level instructions and low-level motor commands, and inference inefficiency due to autoregressive scalar decoding.In this work, we propose MIRTH, a unified framework designed to address these challenges.MIRTH augments a pretrained VLA backbone with three key innovations: (1) dualscale temporal memory hubs that compress long-term scene evolution and short-term motion trends into compact embeddings; (2) latent reasoning tokens optimized via a mutualinformation objective carving out a semantic plan space to align multimodal context with action trajectories; and (3) a parallel action decoding scheme that replaces autoregressive generation with vector-wise prediction to maximize control throughput.Extensive evaluations on the LIBERO simulation benchmark and a real-world LeRobot platform demonstrate that MIRTH achieves state-of-the-art performance and exhibiting emergent error recovery capabilities.The codes and collected datasets are released at http://github.com/kiva12138/mirth.
Authors: Hao Sun, Yu Song, Teng Shiyu, Ziwei Niu, Yen-wei Chen
Institutions: Ritsumeikan University, Zhejiang University
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166870768