TARSphere: A Robot-Oriented Full-Stack Framework for Long-Horizon Embodied Agents in Evolving Physical and Social Environments
TARSphere is presented as a robot-oriented full-stack framework for long-horizon embodied agents in physics-simulated indoor scenes with embodied NPCs and constrained dialogue. An event-driven finite-state machine updates task states, while a frozen multimodal LLM acts through executable skills with hierarchical memory and execution feedback. The authors build TARSphere-Bench with 360 episodes across 30 indoor scenes and evaluate 8 MLLMs on perception, disruption adaptation, task updates, human coordination, and intent understanding. They report that general-purpose models can outperform embodied models, and that task updates, human interaction, collisions and falls remain challenging.
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- Primary sourceHugging Face Daily Papers
- Discovery linkHugging Face
- Linked in sourceView arXiv page
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TARSphere is presented as a robot-oriented full-stack framework for long-horizon embodied agents in physics-simulated indoor scenes with embodied NPCs and constrained dialogue. An event-driven finite-state machine updates task states, while a frozen multimodal LLM acts through executable skills with hierarchical memory and execution feedback. The authors build TARSphere-Bench with 360 episodes across 30 indoor scenes and evaluate 8 MLLMs on perception, disruption adaptation, task updates, human coordination, and intent understanding. They report that general-purpose models can outperform embodied models, and that task updates, human interaction, collisions and falls remain challenging.
Key findings from the stored brief
- TARSphere-Bench contains 360 episodes across 30 indoor scenes for navigate-and-report and fetch-and-deliver tasks.
- Evaluation of 8 MLLMs found general-purpose models can outperform embodied models.
- Task updates and human interaction are reported as difficult for the evaluated models.
- Frequent collisions and falls motivate evaluating execution safety alongside task success.
Source claims not independently checked; results are reported by the authors on their own benchmark.
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Why this may matter
The framework targets embodied agents that must adapt to changing environments and human needs, rather than fixed goals. Its benchmark pairs task success with execution safety, which may help researchers compare agents on realistic, socially grounded robot tasks.
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