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SPOC: Safety-Aware Planning Under Partial Observability and Physical Constraints
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- Authors
- Hyungmin Kim, Hobeom Jeon, Dohyung Kim, Minsu Jang, Jaehong Kim
- Issue Date
- 2026-05
- Citation
- International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2026, pp.20097-20101
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- Embodied Task Planning with large language models faces safety challenges in real-world environments, where partial observability and physical constraints must be respected. Existing benchmarks often overlook these critical factors, limiting their ability to evaluate both feasibility and safety. We introduce SPOC, a benchmark for safety-aware embodied task planning, which integrates strict partial observability, physical constraints, step-by-step planning, and goal-condition-based evaluation. Covering diverse household hazards such as fire, fluid, injury, object damage, and pollution, SPOC enables rigorous assessment through both state and constraint-based online metrics. Experiments with state-of-the-art LLMs reveal that current models struggle to ensure safety-aware planning, particularly under implicit constraints. Code and dataset are available at https://github.com/khm159/SPOC
- Keyword
- Embodied Task Planning, AI Safety
- KSP Keywords
- Critical factors, Online metrics, Partial observability, Physical Constraints, Real-world, Step-by-step, Task planning, constraint-based, language models, state-of-The-Art