TAME-Nav: Temporal Asymmetric Mixture-of-Experts Distillation for Agile and Versatile Robot Navigation

Key points

• Agile and reliable navigation is a core capability for embodied robots navigating complex spatial constraints and dynamic interactions.1
• The Teacher combines clean observations with causally maintained dynamic-obstacle tracks to construct privileged representations, while the Student learns complementary navigation features from corrupted observation histories through temporal encoding, soft expert routing, and latent distillation.1
• Procedural scenario generation and parallel task-level simulation broaden training coverage, while deployment-informed asymmetric domain randomization supports sim-to-real transfer.1
• Across in-distribution and unseen compositional scenarios, TAME-Nav achieves higher success rates, lower collision rates, and higher navigation scores than representative baselines.1

Sources (1)

• [1]TAME-Nav: Temporal Asymmetric Mixture-of-Experts Distillation for Agile and Versatile Robot Navigation
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 02:13 AM

  We present TAME-Nav, an asymmetric Teacher-Student navigation framework built around a temporal soft mixture of experts (Soft-MoE).

  Agile and reliable navigation is a core capability for embodied robots navigating complex spatial constraints and dynamic interactions.

Extractive summary: sentences quoted from the sources.