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ARXIV:2604.00557 · ROBOTICS · SUBMITTED 02 APR · 20:55 UTC · FRESHNESS STALE
ARXIV:2604.00557ROBOTICSSUBMITTED 02 APR · 20:55 UTCFRESHNESS STALEYichen Xie · Yixiao Wang · Shuqi Zhao · Cheng-En Wu · Masayoshi Tomizuka · Jianwen Xie · +1 at arXiv
A framework for data-efficient robot imitation learning by scaling camera views during demonstration collection to improve generalization.
Opportunity summary
Pain A framework for data-efficient robot imitation learning by scaling camera views during demonstration collection to improve generalization.
Evidence 39 refs | 3 sources | 33% coverage
Blocker Evidence unverified
A framework for data-efficient robot imitation learning by scaling camera views during demonstration collection to improve generalization. In this paper, we propose a practical framework that exploits inherent scene diversity without additional human effort…
The generalization ability of imitation learning policies for robotic manipulation is fundamentally constrained by the diversity of expert demonstrations, while collecting demonstrations across varied environments is costly and difficult in practice. In this paper,…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Instead of acquiring more trajectories, multiple synchronized camera perspectives are used to generate pseudo-demonstrations from each expert trajectory, which enriches the training distribution and…
Robotics moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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A framework for data-efficient robot imitation learning by scaling camera views during demonstration collection to improve generalization.
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10.48550/arXiv.2604.00557A framework for data-efficient robot imitation learning by scaling camera views during demonstration collection to improve generalization.
Abstract
The generalization ability of imitation learning policies for robotic manipulation is fundamentally constrained by the diversity of expert demonstrations, while collecting demonstrations across varied environments is costly and difficult in practice. In this paper, we propose a practical framework that exploits inherent scene diversity without additional human effort by scaling camera views during demonstration collection. Instead of acquiring more trajectories, multiple synchronized camera perspectives are used to generate pseudo-demonstrations from each expert trajectory, which enriches the training distribution and improves viewpoint invariance in visual representations. We analyze how different action spaces interact with view scaling and show that camera-space representations further enhance diversity. In addition, we introduce a multiview action aggregation method that allows single-view policies to benefit from multiple cameras during deployment. Extensive experiments in simulation and real-world manipulation tasks demonstrate significant gains in data efficiency and generalization compared to single-view baselines. Our results suggest that scaling camera views provides a practical and scalable solution for imitation learning, which requires minimal additional hardware setup and integrates seamlessly with existing imitation learning algorithms. The website of our project is https://yichen928.github.io/robot_multiview.
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Proof status
unverified39 refs; 3 sources; 33% coverage.
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PROBLEM
A framework for data-efficient robot imitation learning by scaling camera views during demonstration collection to improve generalization. In this paper, we propose a practical framework that exploits inherent scene diversity without additional human effort by scaling camera vie...
METHOD
The generalization ability of imitation learning policies for robotic manipulation is fundamentally constrained by the diversity of expert demonstrations, while collecting demonstrations across varied environments is costly and difficult in practice. In this paper, we propose a...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Instead of acquiring more trajectories, multiple synchronized camera perspectives are used to generate pseudo-demonstrations from each expert trajectory, which enriches the training distribution and impro...
WHY NOW
Robotics moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A framework for data-efficient robot imitation learning by scaling camera views during demonstration collection to improve generalization. In this paper, we propose a practical framework that exploits inherent scene diversity without additional human effort by scaling camera views during demonstration collection.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
The generalization ability of imitation learning policies for robotic manipulation is fundamentally constrained by the diversity of expert demonstrations, while collecting demonstrations across varied environments is costly and difficult in practice. In this paper, we propose a practical framework that exploits inherent scene diversity without additional human effort by scaling camera views during demonstration collection.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Instead of acquiring more trajectories, multiple synchronized camera perspectives are used to generate pseudo-demonstrations from each expert trajectory, which enriches the training distribution and improves viewpoint invariance in visual representations. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Robotics moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A framework for data-efficient robot imitation learning by scaling camera views during demonstration collection to improve generalization.
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Robotics
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Commercial read
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39 refs / 3 sources / 33% coverage
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39 references, 3 sources, 33% evidence coverage.
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