RT-Safe: Benchmarking Agent Safety in Real-Time Embodied Environment
cs.AI, cs.LG
Submitted: 2026-10-07
Updated: 2026-10-07
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- On Evaluation of Embodied Navigation Agents
- NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles
- SafeMind: Benchmarking and Mitigating Safety Risks in Embodied LLM Agents
- DIRECT: When and Where Should You Allocate Test-Time Compute in Embodied Planners?
- Reinforcement Learning for Real-Time Vision-Language-Action Policies
- AsyncVLA: An Asynchronous VLA for Fast and Robust Navigation on the Edge
- PACE: Adaptive Budget Allocation for Time-Efficient Embodied Planning
- A Framework for Benchmarking and Aligning Task-Planning Safety in LLM-Based Embodied Agents
- TIC-VLA: A Think-in-Control Vision-Language-Action Model for Robot Navigation in Dynamic Environments
- Bench2Drive-VL: Benchmarks for Closed-Loop Autonomous Driving with Vision-Language Models
- FutureRTC: Real-Time Robot Execution with Anticipatory-Conditioned Action Chunking
- SimWorld Studio: Automatic Environment Generation with Evolving Coding Agent for Embodied Agent Learning
- DeliveryGym: An RL Environment for Long-Horizon Embodied Agent Planning with Adaptive Curriculum
- Offline Reinforcement Learning with Implicit Q-Learning
- UrbanVLA: A Vision-Language-Action Model for Urban Micromobility
- ReactHuman: A Physics-Grounded Benchmark for Human-Like Reactive Decision-Making in Embodied Multimodal LLMs
- BeSafe-Bench: Unveiling Behavioral Safety Risks of Situated Agents in Functional Environments
- SidewalkBench: Benchmarking Visual Navigation on Urban Sidewalks
- ForesightSafety-VLA: A Unified Diagnostic Safety Benchmark for Vision-Language-Action Models
- DeliveryBench: Can Agents Earn Profit in Real World?
Related papers
- MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
- Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
- Agents' Last Exam
- The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
- MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
- Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems