aiqbench analysis
SynthICL demonstrates a scalable approach to in-context imitation learning by training robot policies exclusively on RGB-only synthetic data, eliminating the need for depth sensors or real-world training datasets. By integrating subgoal prediction within a flow-matching transformer framework, the method achieves a 79% success rate on unseen manipulation tasks using only a single demonstration at test time. This advancement offers a more accessible and efficient pathway for deploying generalizable robotic policies without the logistical overhead of physical data collection.
SynthICL: Scalable In-context Imitation Learning with Synthetic Data
arXiv:2606.08154v1 Announce Type: new Abstract: In-context imitation learning (ICIL) enables robots to learn new tasks from a small number of demonstrations by conditioning a pre-trained policy on task-specific examples, without retraining at test time. Despite this promise, training generalizable and scalable in-context imitation policies remains an open challenge. We present SynthICL, a scalable framework that trains ICIL policies entirely from RGB-only synthetic data. Specifically, we build a data generation pipeline to produce high-fidelity ICIL data and train a flow-matching transformer policy on the resulting dataset. SynthICL avoids the need for depth sensing, precise camera calibration, and real-world training data in prior approaches, offering a simpler and more scalable alternative. We further incorporate subgoal prediction by training the model to predict the next subgoal images, enabling more precise and visually grounded control. Evaluated on 16 unseen real-world manipulation tasks, SynthICL achieves an average success rate of 79% with only one demonstration provided at test time and outperforms prior methods. Project page: https://synth-icl.github.io
Abstract:In-context imitation learning (ICIL) enables robots to learn new tasks from a small number of demonstrations by conditioning a pre-trained policy on task-specific examples, without retraining at test time. Despite this promise, training generalizable and scalable in-context imitation policies remains an open challenge. We present SynthICL, a scalable framework that trains ICIL policies entirely from RGB-only synthetic data. Specifically, we build a data generation pipeline to produce high-fidelity ICIL data and train a flow-matching transformer policy on the resulting dataset. SynthICL avoids the need for depth sensing, precise camera calibration, and real-world training data in prior approaches, offering a simpler and more scalable alternative. We further incorporate subgoal prediction by training the model to predict the next subgoal images, enabling more precise and visually grounded control. Evaluated on 16 unseen real-world manipulation tasks, SynthICL achieves an average success rate of 79% with only one demonstration provided at test time and outperforms prior methods. Project page: this https URL
Robotics (cs.RO)
arXiv:2606.08154 [cs.RO]
(or arXiv:2606.08154v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.08154 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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