2 Jul 2024 | Albert Yu, Adeline Foote, Raymond Mooney, and Roberto Martín-Martín
The paper "Natural Language Can Help Bridge the Sim2Real Gap" by Albert Yu, Adeline Foote, Raymond Mooney, and Roberto Martín-Martín from UT Austin addresses the challenge of learning image-conditioned robotic policies in the real world. The main issue is acquiring a visual representation that is conducive to low-level control, which requires a large amount of visual data. To overcome data scarcity, the Sim2Real paradigm uses simulators to collect cheap data related to the target task. However, transferring an image-conditioned policy from simulation to the real world is difficult when the domains are visually dissimilar.
The authors propose using natural language descriptions of images as a unifying signal across domains to capture task-relevant semantics. They hypothesize that if two images from different domains have similar language descriptions, the policy should predict similar action distributions for both images. This insight is used to train an image encoder to predict language descriptions or the distance between descriptions of images from simulation and the real world. This pretraining step helps learn a domain-invariant image representation, which can then be used as the backbone of an IL policy trained on a large amount of simulated data and a few real demonstrations.
The proposed method, Lang4Sim2Real, is evaluated on three task suites: stacking objects, multi-step pick-and-place, and wrapping wire. The results show that Lang4Sim2Real outperforms prior sim2real methods and strong vision-language pretraining baselines like CLIP and R3M by 25 to 40%. The method bridges a wide sim2real gap, including differences in camera point-of-view, friction coefficients, task goals, and initial positions.
The paper also discusses the limitations of the approach, such as its limited generalizability compared to pretraining methods that use large-scale datasets, and suggests future work on scaling the method to larger datasets and combining it with existing pretraining techniques.The paper "Natural Language Can Help Bridge the Sim2Real Gap" by Albert Yu, Adeline Foote, Raymond Mooney, and Roberto Martín-Martín from UT Austin addresses the challenge of learning image-conditioned robotic policies in the real world. The main issue is acquiring a visual representation that is conducive to low-level control, which requires a large amount of visual data. To overcome data scarcity, the Sim2Real paradigm uses simulators to collect cheap data related to the target task. However, transferring an image-conditioned policy from simulation to the real world is difficult when the domains are visually dissimilar.
The authors propose using natural language descriptions of images as a unifying signal across domains to capture task-relevant semantics. They hypothesize that if two images from different domains have similar language descriptions, the policy should predict similar action distributions for both images. This insight is used to train an image encoder to predict language descriptions or the distance between descriptions of images from simulation and the real world. This pretraining step helps learn a domain-invariant image representation, which can then be used as the backbone of an IL policy trained on a large amount of simulated data and a few real demonstrations.
The proposed method, Lang4Sim2Real, is evaluated on three task suites: stacking objects, multi-step pick-and-place, and wrapping wire. The results show that Lang4Sim2Real outperforms prior sim2real methods and strong vision-language pretraining baselines like CLIP and R3M by 25 to 40%. The method bridges a wide sim2real gap, including differences in camera point-of-view, friction coefficients, task goals, and initial positions.
The paper also discusses the limitations of the approach, such as its limited generalizability compared to pretraining methods that use large-scale datasets, and suggests future work on scaling the method to larger datasets and combining it with existing pretraining techniques.