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REASSEMBLE: A Multimodal Dataset for Contact-rich Robotic Assembly and Disassembly
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REASSEMBLE: A Multimodal Dataset for Contact-rich Robotic Assembly and Disassembly
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Robotic manipulation remains a core challenge in robotics, particularly for contact-rich tasks such as industrial assembly and disassembly. Existing datasets have significantly advanced learning in manipulation but are primarily focused on simpler tasks like object rearrangement, falling short of capturing the complexity and physical dynamics involved in assembly and disassembly. To bridge this gap, we present REASSEMBLE (Robotic assEmbly disASSEMBLy datasEt), a new dataset designed specifically for contact-rich manipulation tasks. Built around the NIST Assembly Task Board 1 benchmark, REASSEMBLE includes four actions (pick, insert, remove, and place) involving 17 objects. The dataset contains 4,551 demonstrations, of which 4,035 were successful, spanning a total of 781 minutes. Our dataset features multi-modal sensor data, including event cameras, force-torque sensors, microphones, and multi-view RGB cameras. This diverse dataset supports research in areas such as learning contact-rich manipulation, task condition identification, action segmentation, and task inversion learning. The REASSEMBLE will be a valuable resource for advancing robotic manipulation in complex, real-world scenarios. The dataset is publicly available on our project website: https://tuwien-asl.github.io/REASSEMBLE_page/.
Forward citations
Cited by 13 Pith papers
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FTP-1: A Generalist Foundation Tactile Policy Across Tactile Sensors for Contact-Rich Manipulation
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AssemblyBench: Physics-Aware Assembly of Complex Industrial Objects
AssemblyBench dataset and AssemblyDyno transformer model enable physics-aware prediction of assembly sequences and trajectories for complex industrial objects from multimodal instructions and 3D shapes.
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RoboProcessBench: Benchmarking Process-Aware Understanding in Vision-Language Robotic Manipulation
A 12-family, ~58k-question benchmark reveals that VLMs are weak at judging robotic manipulation progress and temporal order, and that fine-tuning on it improves local state, motion, and primitive-aware cues.
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FingerViP: Learning Real-World Dexterous Manipulation with Fingertip Visual Perception
FingerViP equips each finger with a miniature camera and trains a multi-view diffusion policy that achieves 80.8% success on real-world dexterous tasks previously limited by wrist-camera occlusion.
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AssemLM: A Spatial Reasoning Multimodal Large Language Model for Robotic Assembly
AssemLM uses a specialized point cloud encoder inside a multimodal LLM to reach state-of-the-art 6D pose prediction for assembly tasks, backed by a new 900K-sample benchmark called AssemBench.
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AssemLM: A Spatial Reasoning Multimodal Large Language Model for Robotic Assembly
AssemLM fuses SO(3)-equivariant point-cloud features into a VLM to predict discrete 6D assembly poses, reaching ~89% success on AssemBench and improved real-robot multi-step assembly.
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RoboLight: A Dataset with Linearly Composable Illumination for Robotic Manipulation
A dataset that records identical robot manipulation tasks under 14 controlled lighting conditions and uses HDR linearity to synthesize 196,000 additional lighting-varied episodes.
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DECO: Decoupled Multimodal Diffusion Transformer for Bimanual Dexterous Manipulation with a Plugin Tactile Adapter
A decoupled multimodal diffusion transformer with a LoRA tactile adapter improves real-world bimanual manipulation success by 21 percentage points over a diffusion-policy baseline; a new 50-hour tactile bimanual datas...
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World Models for Robotic Manipulation: A Survey
Survey organizing world models for robotic manipulation into representation families, a functional taxonomy, and infrastructure roles across pretraining, post-training, and inference, while reviewing 34 datasets and e...
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