Learning to Evolve: Multi-modal Interactive Fields for Robust Humanoid Navigation in Dynamic Environments

2026-05-21 · Peifeng Jiang, Hong Liu, Jin Jin, Wenshuai Wang, Xia Li arxiv

Safe manipulation-oriented navigation for humanoid robots requires scene memory that remains reliable under locomotion-induced perceptual distortion, environmental changes, and interaction-level geometric safety constraints. Existing semantic mapping and scene-graph systems are difficult to deploy directly in this setting because they often assume stable camera trajectories, static environments, or coarse object geometry. We introduce the Multi-modal Interactive Field (MIF), a humanoid-oriented system that integrates confidence-aware semantic 3D Gaussian Splatting, discrepancy-triggered spatial memory updates, and task-driven geometric reconstruction within a closed-loop perception-adaptation pipeline. MIF couples three fields: an uncertainty-aware 3DGS Appearance Field that suppresses gait-induced blur, a Spatial Field that maintains topological memory, and a Geometry Field that supports Interaction Pose Safety (IPS) before manipulation. A discrepancy detection score is introduced to separate locomotion-induced false-positive changes from persistent changes and updates only locally inconsistent regions. On a Unitree-G1 humanoid in a real dynamic office, MIF improves relocation success in non-static environments from 12% to 94% compared with static scene-graph memory, while reducing semantic memory footprint by 91.4% through feature distillation for practical online operation. Project page and code: https://ziya-jiang.github.io/MIF-homepage/

📄 PDF Abstract BibTeX arXiv:2605.21935

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Tree Learning: A Multi-Skill Continual Learning Framework for Humanoid Robots

2026-04-14 · Yifei Yan, Linqi Ye arxiv

As reinforcement learning for humanoid robots evolves from single-task to multi-skill paradigms, efficiently expanding new skills while avoiding catastrophic forgetting has become a key challenge in embodied intelligence…

Reinforcement Learning Continual Learning

EgoPriMo: Egocentric Motion Generation for Interactive Humanoid Control

2026-06-07 · Haoyang Ge, Peng Ren, Yukun Shi, Cong Huang 외 arxiv

Humanoid robots require whole-body motions that adapt to scene context, task requirements, and user intent. Motion tracking reproduces specified trajectories, and humanoid vision-language-action systems provide semantic …

PACE: Persona Adaptation through Conversational Elicitation in Human-Robot Interaction

2026-07-17 · Peizhen Li, Longbing Cao, Megani Rajendran, Timothy Liu 외 arxiv

Equipping humanoid robots with coherent and adaptable personas is crucial for fostering natural, engaging, and trustworthy human-robot interaction (HRI). However, existing approaches often rely on static, hard-coded iden…

Training an Interactive Humanoid Robot Using Multimodal Deep Reinforcement Learning

2016-11-26 · Heriberto Cuayáhuitl, Guillaume Couly, Clément Olalainty

Training robots to perceive, act and communicate using multiple modalities still represents a challenging problem, particularly if robots are expected to learn efficiently from small sets of example interactions. We desc…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Body of Her: A Preliminary Study on End-to-End Humanoid Agent

2024-08-06 · Tenglong Ao

Interactive virtual humanoid agent is a crucial interface with the physical world. A relatively complete humanoid agent first needs to have face and body, then possess both verbal and non-verbal (such as eye contact, fac…

Language Modeling Language Modelling Large Language Model