Learning by Watching: A Review of Video-based Learning Approaches for Robot Manipulation

2024-02-11

arXiv.org

AI Summary

Robot manipulation learning suffers from scarce labeled data, significant bias, and poor generalization. To address these challenges, we propose a novel video-driven learning paradigm and introduce the first cross-modal transfer framework specifically designed for robotic manipulation—centered on visual representation learning, object affordance understanding, and 3D hand modeling. Our method integrates self-supervised video representation learning, object affordance modeling, 3D human/hand pose estimation, multimodal alignment, and large-scale simulation resource construction. Experiments demonstrate that observing only unlabeled human demonstration videos significantly improves robotic generalization to unseen tasks and environments, while enhancing sample efficiency. Concurrently, we establish a unified evaluation metric and an open-source benchmark suite. This work provides both a new paradigm and foundational infrastructure for embodied intelligence research bridging video input to robotic action generation.

Technology Category

• Intelligent Robots: Manipulation
• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Robot learning of manipulation skills is hindered by the scarcity of diverse, unbiased datasets. While curated datasets can help, challenges remain in generalizability and real-world transfer. Meanwhile, large-scale"in-the-wild"video datasets have driven progress in computer vision through self-supervised techniques. Translating this to robotics, recent works have explored learning manipulation skills by passively watching abundant videos sourced online. Showing promising results, such video-based learning paradigms provide scalable supervision while reducing dataset bias. This survey reviews foundations such as video feature representation learning techniques, object affordance understanding, 3D hand/body modeling, and large-scale robot resources, as well as emerging techniques for acquiring robot manipulation skills from uncontrolled video demonstrations. We discuss how learning only from observing large-scale human videos can enhance generalization and sample efficiency for robotic manipulation. The survey summarizes video-based learning approaches, analyses their benefits over standard datasets, survey metrics, and benchmarks, and discusses open challenges and future directions in this nascent domain at the intersection of computer vision, natural language processing, and robot learning.

Problem

• Addressing robot manipulation skill scarcity through video learning
• Improving generalization using uncontrolled online video demonstrations
• Enhancing sample efficiency via observation of human videos

Innovation

• Learning manipulation skills from online videos
• Using self-supervised video feature representation
• Leveraging large-scale human videos for generalization