TARSphere: A Robot-Oriented Full-Stack Framework for Long-Horizon Embodied Agents in Evolving Physical and Social Environments

Key points

• Robots must act under physical and social constraints while adapting to changes in their environment and human needs.1
• TARSphere-Infra runs robot policies in a physics simulator with embodied NPCs and constrained dialogue.1
• TARSphere-Harness, its embodied brain harness, lets a frozen multimodal large language model (MLLM) act through multi-level executable skills, using observations, hierarchical memory, and execution feedback to revise its decisions.1
• On this foundation, agent-in-the-loop curation aligns task semantics, causal dependencies, and scene geometry to construct TARSphere-Bench: 360 episodes across 30 indoor scenes.1

Sources (1)

• [1]TARSphere: A Robot-Oriented Full-Stack Framework for Long-Horizon Embodied Agents in Evolving Physical and Social Environments
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 05:02 PM

  We introduce TARSphere, a robot-oriented full-stack framework for physically and socially grounded long-horizon embodied agents.

  Robots must act under physical and social constraints while adapting to changes in their environment and human needs.

Extractive summary: sentences quoted from the sources.