Resilience Matters for Embodied Agents System: New Metrics, Systematic Evaluation, and Optimization

arXiv:2608.23839 · cs.RO, cs.AI · Submitted 2026-08-24 · Read on arXiv

cs.RO, cs.AI

Submitted: 2026-08-24

Updated: 2026-08-24

Comments: 12 pages, 5 figures

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 14× in this paper · explained in 2 episodes
• Grounding — 10× in this paper · explained in 3 episodes
• Benchmarking — 6× in this paper · explained in 3 episodes
• Consistency — 6× in this paper · explained in 3 episodes
• Embodied Agents System — 5× in this paper
• Adaptability — 4× in this paper · explained in 1 episode
• Adaptation — 4× in this paper · explained in 1 episode
• Embodied AI — 4× in this paper · explained in 2 episodes
• LLM judge — 4× in this paper · explained in 2 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• AI Agents — 2× in this paper · explained in 2 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Archetype — 2× in this paper · explained in 1 episode
• Action Space — 1× in this paper · explained in 1 episode
• Affordance — 1× in this paper · explained in 1 episode
• Agentic Artificial Intelligence — 1× in this paper · explained in 1 episode
• Artificial Intelligence (AI) — 1× in this paper · explained in 2 episodes
• Catastrophic Forgetting — 1× in this paper · explained in 5 episodes
• Dynamic Adaptation — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• LLM-as-a-Judge — 1× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Multi-Agent System — 1× in this paper · explained in 6 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Task Planning — 1× in this paper · explained in 2 episodes

Sources

• Deep Learning using Rectified Linear Units (ReLU)
• Do As I Can, Not As I Say: Grounding Language in Robotic Affordances
• Agentic Artificial Intelligence (AI): Architectures, Taxonomies, and Evaluation of Large Language Model Agents
• HazardArena: Evaluating Semantic Safety in Vision-Language-Action Models
• Embodied AI Agents: Modeling the World
• Inner Monologue: Embodied Reasoning through Planning with Language Models
• Code as Policies: Language Model Programs for Embodied Control
• IS-Bench: Evaluating Interactive Safety of VLM-Driven Embodied Agents in Daily Household Tasks
• CycleVLA: Proactive Self-Correcting Vision-Language-Action Models via Subtask Backtracking and Minimum Bayes Risk Decoding
• MTTR-A: Measuring Cognitive Recovery Latency in Multi-Agent Systems
• Beyond Robustness: A Taxonomy of Approaches towards Resilient Multi-Robot Systems
• RePLan: Robotic Replanning with Perception and Language Models
• Voyager: An Open-Ended Embodied Agent with Large Language Models
• EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agents
• SafeAgentBench: A Benchmark for Safe Task Planning of Embodied LLM Agents
• AgentEvolver: Towards Efficient Self-Evolving Agent System
• Using large language models for embodied planning introduces systematic safety risks
• EARBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents

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