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• A minimal active stereo-camera setup outperforms complex multi-sensor configurations in ACT imitation learning for humanoid manipulation tasks in data-limited regimes.result 95%
• Adding pressure sensors to an active stereo-camera setup reduces task success rate in structured manipulation due to low signal-to-noise ratio.result 90%
• In data-limited regimes (≤250 demonstrations), additional sensor modalities often degrade performance for Imitation Learning.result 85%
• Strategic sensor selection can outperform complex configurations in data-limited regimes while reducing computational overhead.method 80%
• An open-source Unified Ablation Framework that utilizes sensor masking on a master dataset was developed to evaluate sensor combinations.method 95%
• Active vision offers a superior trade-off between robustness and complexity compared to tactile modalities in data-limited regimes.technical 85%
• Tactile modalities may require larger datasets than those used in this study (≤250 demonstrations) to be effective for IL.limitation 75%
• The study benchmarks 14 distinct sensor combinations on a Unitree G1 humanoid for two manipulation tasks.method 95%

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