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# CARE

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arXiv Sep 28, 2026
CARE turns robot execution failures into training data, lifting average task success by 14.5 points in simulation and 15.9 points in the real world
The work proposes CARE, which collects failed rollouts, models stage-conditioned post-failure geometric deviation distributions, synthesizes representative failure states and corrective demonstrations from them, and at inference combines stage-wise planning with physically grounded 3D point-cloud monitoring to trigger atomic adjustments or re-operations; across RoboTwin 2.0, RoboFactory, the newly introduced FSR-Bench, and real-world dual-arm tasks it reports average task-success gains of 14.5 points in simulation, 15.9 points in the real world, and a 7.5-point gain in recovery success on FSR-Bench.
The work proposes CARE, which collects failed rollouts, models stage-conditioned post-failure geometric deviation distributions, synthesizes representative failure states and corrective demonstrations from them, and at inference combines stage-wise planning with physically grounded 3D point-cloud monitoring to trigger atomic adjustments or re-operations; across RoboTwin 2.0, RoboFactory, the newly introduced FSR-Bench, and real-world dual-arm tasks it reports average task-success gains of 14.5 points in simulation, 15.9 points in the real world, and a 7.5-point gain in recovery success on FSR-Bench.
Junlan Xiao（First author）
CARE Atomic Corrective Execution FSR-Bench Vision-Language-Action Models
arXiv Sep 27, 2026
Qwen-Planner-Agent achieves the best overall performance among all evaluated models and systems on MobilePA-Bench, improving over its base model in tool use, memory, skills, and sub-agent coordination
The work builds Qwen-Planner-Agent within a closed-loop AI-for-AI framework that links data production, model training, and deployment through a shared action-feedback-verification contract: AI for Data builds a human-gated agentic data flywheel, AI for Training combines a supervised planning cold start with hybrid-environment online agentic reinforcement learning and introduces CARE to reduce reasoning and tool-use costs, and AI drives model-harness co-evolution; the agent achieves the best overall performance among all evaluated models and systems on MobilePA-Bench, improving over its base model across tool use, memory, skills, and sub-agent coordination, with further gains on non-mobile agentic benchmarks while largely preserving general capabilities.
The work builds Qwen-Planner-Agent within a closed-loop AI-for-AI framework that links data production, model training, and deployment through a shared action-feedback-verification contract: AI for Data builds a human-gated agentic data flywheel, AI for Training combines a supervised planning cold start with hybrid-environment online agentic reinforcement learning and introduces CARE to reduce reasoning and tool-use costs, and AI drives model-harness co-evolution; the agent achieves the best overall performance among all evaluated models and systems on MobilePA-Bench, improving over its base model across tool use, memory, skills, and sub-agent coordination, with further gains on non-mobile agentic benchmarks while largely preserving general capabilities.
Qwen
Tingyu Qu（First author）
Qwen-Planner-Agent Closed-Loop AI-for-AI Framework Mobile Planning CARE Agentic Reinforcement Learning

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