Imitation-learned robot policies are frequently overfit to the visual conditions present in their training demonstrations. Consequently, variations in object color or background appearance often induce substantial performance degradation. A common mitigation strategy is to acquire additional demonstrations in each novel visual context; however, this approach is resource-intensive, requiring repeated access to a robot, a controlled environment, and human operation for every appearance condition to be covered. We introduce ReShoot, a framework that synthesizes visual diversity by re-rendering previously recorded demonstrations under altered appearances, thereby shifting the burden from data collection to generation. A vision-language model captions the scene, edits a targeted attribute (e.g., background, object color, or material), and an edge-conditioned video generator re-renders both camera views to match. The instruction is updated accordingly. The action sequence and proprioceptive trajectory are copied verbatim without relabeling, so each generated episode retains the recorded action and proprioceptive labels. On LIBERO, a policy trained on an equal mixture of recorded and re-rendered demonstrations matches the performance of recorded-only training (96.5% vs. 96.9%). Moreover, the mixed training set improves robustness to scene perturbations on LIBERO-Plus (85.5% vs. 82.3%). Across two physical robotic platforms, deploying ReShoot with 43 and 100 pre-collected demonstrations increased the success rate on recolored objects from 0.0% to 42.9% and 47.5%, respectively, while maintaining performance under the original recorded appearance.
Chiyoung Kim, Min-Sung Choi, Jin-Ho Ju et al.· 0 citations
Transfer learning with ImageNet-pretrained backbones is ubiquitous in computer vision, yet the question of which intermediate layer best serves a downstream task is typically resolved by convention rather than controlled measurement. Despite the prevalence of this practice, no systematic comparison of layer-wise frozen representations has been conducted across the modern architectures that dominate current pipelines. We present the Layer Transferability Profile, a reproducible evaluation protocol and an accompanying metric, the Layer-Task Transferability Score (LTTS), that together quantify how transferable information is distributed across depth. Applying the profile to three architectures, ResNet-50, EfficientNet-B0, and ViT-B/16, on three recognition benchmarks (CIFAR-10, CIFAR-100, and Food101), we find that transfer performance improves monotonically with depth and that the final or near-final representation is consistently optimal (LTTS 0-1.2%). This pattern contrasts with recent findings in speech and language foundation models, where intermediate layers can yield substantial gains, suggesting that the concentration of transferability in late layers is characteristic of supervised visual pretraining for recognition tasks rather than a universal property of deep representations. Notably, ViT-B/16 achieves the strongest transfer accuracy (78.1% on CIFAR-100, 94.6% on CIFAR-10, and 68.6% on Food-101) while using 768-dimensional features, outperforming ResNet-50’s 2048-dimensional and EfficientNet-B0’s 1280-dimensional representations. This indicates that representational quality per dimension varies substantially across architectures and can be more important than raw feature size when selecting a backbone for transfer.
Sanghyuk Roy Choi, Chanhoe Gu, S. Kim et al.· International Conference on...· 0 citations
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