ViSA-Flow: Accelerating Robot Skill Learning via Large-Scale Video Semantic Action Flow (2505.01288v2)

Abstract: One of the central challenges preventing robots from acquiring complex manipulation skills is the prohibitive cost of collecting large-scale robot demonstrations. In contrast, humans are able to learn efficiently by watching others interact with their environment. To bridge this gap, we introduce semantic action flow as a core intermediate representation capturing the essential spatio-temporal manipulator-object interactions, invariant to superficial visual differences. We present ViSA-Flow, a framework that learns this representation self-supervised from unlabeled large-scale video data. First, a generative model is pre-trained on semantic action flows automatically extracted from large-scale human-object interaction video data, learning a robust prior over manipulation structure. Second, this prior is efficiently adapted to a target robot by fine-tuning on a small set of robot demonstrations processed through the same semantic abstraction pipeline. We demonstrate through extensive experiments on the CALVIN benchmark and real-world tasks that ViSA-Flow achieves state-of-the-art performance, particularly in low-data regimes, outperforming prior methods by effectively transferring knowledge from human video observation to robotic execution. Videos are available at https://visaflow-web.github.io/ViSAFLOW.

ViSA-Flow: Accelerating Robot Skill Learning via Large-Scale Video Semantic Action Flow

ViSA-Flow introduces a novel methodology to advance robot skill acquisition by capitalizing on large-scale video records of human manipulation. Recognizing the traditional challenges associated with procuring extensive robot demonstration datasets, this approach endeavors to bridge the observational capabilities seen in humans to robotic systems, utilizing semantic action flow as an intermediate representation. This representation is critical as it encapsulates the essential spatio-temporal interactions between manipulators and objects, while showcasing robustness against superficial visual disparities.

Methodology Overview

ViSA-Flow is predicated on a two-stage learning framework. Initially, a generative model is pre-trained on video data to form a foundational understanding of manipulation. This model leverages automatically extracted semantic action flows to craft a robust manipulation structure prior. Subsequently, the model undergoes fine-tuning specifically tailored to the robot domain via a modest collection of robot demonstrations processed by the same semantic abstraction methodology.

Key Contributions and Findings

The framework boasts various substantial contributions:

1. Semantic Action Flow Pre-training: Generating a pre-trained policy foundation utilizing Video Semantic Action Flow has enabled efficient translation of human video interaction knowledge into robotic manipulation policies.
2. Enhanced Policy Adaptation: The method permits robust adaptation of learned policies by integrating robot-specific semantic actions derived from expert demonstrations. This alignment empowers improved fine-tuning outcomes.
3. Performance Evaluation: Empirical studies in simulated environments through the CALVIN benchmark and real-world tasks demonstrate ViSA-Flow's superiority compared to existing methodologies, particularly under low-data conditions. ViSA-Flow achieves notably higher success rates in sequential task completion.

Implications and Future Directions

ViSA-Flow represents a significant step forward in leveraging video resources for robotic learning. The implications span various domains:

• Practical Implications: Reducing the reliance on exhaustive robot demonstration data catalyzes broader accessibility of skill learning systems across diverse robotic applications.
• Theoretical Implications: The results suggest that intermediate representations like semantic action flows can play a pivotal role in harmonizing the domain discrepancies between human demonstrations and robotic learning contexts.

Looking ahead, future research endeavors may focus on incorporating explicit modeling of 3D geometries and contact dynamics to enhance generalization capabilities, especially for tasks involving intricate physical interactions. Additionally, exploring reinforcement learning integration and scaling pre-training sessions to larger video databases could provide richer contextual insights for manipulating effectiveness.

ViSA-Flow stands as a pivotal contribution to imitation learning, showcasing a methodology that not only enhances efficiency and performance but also augments the alignment between human and robotic interactions through innovative abstraction techniques.