CIALLO: Learning Shared Intent Representations for Cross-Embodiment Manipulation
Overview
This paper presents CIALLO, an action codec designed to align actions, visual changes, and observation-grounded language into a shared space of short-horizon behavioral intent.
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Reported quantity · demonstrations per task: 20 other · Basis not reported
“brated codec then remains frozen while a policy learns to predict intent from observations and instructions. We instantiate this policy with Qwen3.5-4B to form Qwen-CIALLO, whose backbone requires no large-scale robot-action pretraining before policy finetuning. With 20 demonstrations per task, Qwen-CIALLO achieves 76.7\% average success across real SO-101 and single-arm Piper robots, compared with 70.0\% for fully f”
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Reported quantity · recording duration: 10 other · Basis not reported
“ual changes, and observation-grounded language in a shared space of short-horizon behavioral intent. An embodiment-conditioned decoder maps this intent back to robot-specific actions, allowing a shared representation to support different control interfaces. After pretraining on eight embodiments, CIALLO adapts to a new robot from one recording of at most 10 seconds by updating only its embodiment embeddings. The cali”
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- arXiv Robotics — research abstractsSignalCIALLO: Learning Shared Intent Representations for Cross-Embodiment Manipulation
This paper presents CIALLO, an action codec designed to align actions, visual changes, and observation-grounded language into a shared space of short-horizon behavioral intent.
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