RoboDreamer: Anticipatory Humanoid Locomotion with Predictive State-Space Models

2026-09-07

AI Summary

本文提出RoboDreamer框架，通过预测状态空间模型和时间掩码技术解决类人机器人在不完美感知下的稳定行走问题。

Abstract

Humanoid locomotion requires control policies that remain stable under imperfect sensing while exploiting temporal context for consistent motion. We present RoboDreamer, a two-stage teacher--student framework that combines next-observation consistency with randomized continuous temporal masking. A teacher is first trained on clean observations, and a student is then distilled under masked recent observations, encouraging the policy to infer missing current information from history. At inference, the same masking interface is reused for implicit closed-loop action refinement and optional multi-step action chunking. Mamba is used as the temporal backbone, while matched ablations show that masking/distillation provides a substantial part of the gain and Mamba contributes additional tracking improvements with real-time latency. Experiments in IsaacLab, MuJoCo, and on a Unitree G1 demonstrate robust motion tracking under observation masking and successful real-world deployment.

Problem

• Humanoid Locomotion
• Imperfect Sensing
• Temporal Context
• Observation Masking
• Policy Stability

Innovation

• two-stage teacher-student framework
• predictive state-space models
• temporal masking
• closed-loop action refinement
• multi-step action chunking