UniLatent: Cross-Embodiment Transfer via Latent Observation Alignment
Overview
This paper introduces UniLatent, a framework for cross-embodiment robot policy transfer that aligns source and target robot observations in a shared latent space without pixel-level image translation. The method achieves up to 93% zero-shot success across three simulation tasks and two target robot arms, outperforming the strongest pixel-translation baseline by over 30 points on average. In real-world xArm-to-Franka transfer, fine-tuning with only 10 target demonstrations per task yields 27/30 successful rollouts. Code, videos, and data are available at https://selina2023.github.io/unilatent/.
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Event evidence and corrections
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Environment: simulation
“t robot observations in simulation and aligns a source visual encoder to a frozen target encoder. A policy trained on encoded source demonstrations can then run directly on target observations, with no pixel-level image translation. Across three simulation tasks and two target robot arms, UniLatent reaches up to 93% zero-shot success, exceeding the strongest pixel-translation baseline by over 30 points on average. In”
Exact source · revision 1Source owner not reported
Reported quantity · target demonstrations per task: 10 other · Basis not reported
“real-world xArm-to-Franka transfer, fine-tuning with only 10 target demonstrations per task yields 27/30 successful rollouts. Code, videos and data are available at https://selina2023.github.io/unilatent/.”
Exact source · revision 1Source owner not reported
Reported quantity · simulation tasks: 3 other · Basis not reported
“t robot observations in simulation and aligns a source visual encoder to a frozen target encoder. A policy trained on encoded source demonstrations can then run directly on target observations, with no pixel-level image translation. Across three simulation tasks and two target robot arms, UniLatent reaches up to 93% zero-shot success, exceeding the strongest pixel-translation baseline by over 30 points on average. In”
Exact source · revision 1Source owner not reported
Artifact availability · code: available
“real-world xArm-to-Franka transfer, fine-tuning with only 10 target demonstrations per task yields 27/30 successful rollouts. Code, videos and data are available at https://selina2023.github.io/unilatent/.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalUniLatent: Cross-Embodiment Transfer via Latent Observation Alignment
This paper introduces UniLatent, a framework for cross-embodiment robot policy transfer that aligns source and target robot observations in a shared latent space without pixel-level image translation. The method achieves up to 93% zero-shot success across three simulation tasks and two target robot arms, outperforming the strongest pixel-translation baseline by over 30 points on average. In real-world xArm-to-Franka transfer, fine-tuning with only 10 target demonstrations per task yields 27/30 successful rollouts. Code, videos, and data are available at https://selina2023.github.io/unilatent/.
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