CarryOn: A Dual-Loop Harness for Personalized and Reliable Robot Assistance

Key points

• First, executing an instruction like "make me a sandwich" requires grounding subtask decomposition in persistent user preferences rather than relying on a single generic solution.1
• To address these gaps, we present CarryOn, a dual-loop harness designed to augment a frozen VLA policy.1
• The planning loop maintains persistent user-level and scene-level context that is continually grounded against new observations and revised through interaction, enabling personalized task selection and execution grounding across conversations and changing scenes.1
• On a real robot and in simulation, CarryOn dispatches preference-consistent subtasks across users and scenes, reduces the violation rate of an already safety-finetuned VLA from 52.6% to 6.1%, and increases recovery success from 13% to 80% when refined skills are reused.1

Sources (2)

• [1]CarryOn: A Dual-Loop Harness for Personalized and Reliable Robot Assistance
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 07:54 AM

  While generalist vision-language-action (VLA) models have enabled diverse open-world manipulation, real-world robotic assistance requires two critical capabilities they currently lack: personalization and reliable execution.

  First, executing an instruction like "make me a sandwich" requires grounding subtask decomposition in persistent user preferences rather than relying on a single generic solution.

• [2]CarryOn: A Dual-Loop Harness for Personalized and Reliable Robot Assistance
  Hugging Face Daily Papers · Oct 15, 12:00 AM · same content

Extractive summary: sentences quoted from the sources.