RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills
RoboSynChallenge, a NeurIPS 2026 competition track, introduces a unified benchmark to advance generalizable robotic manipulation. It pairs large-scale synthetic data generation with standardized real-world evaluation, encouraging participants to train policies on synthesized state-action trials while assessing final performance on unseen real-world environments. Multiple baselines, including Transformer-, Diffusion-, Vision-Language-Action, and World-Action-Model policies, are provided for reproducibility and comparability.
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[Submitted on 12 Aug 2026]
Title:RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills
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Abstract:Achieving generalizable robotic manipulation remains a central challenge in embodied intelligence. Despite rapid advances in model architectures and learning algorithms, progress is often limited by the scarcity and narrow diversity of real-world data. The RoboSynChallenge competition introduces a unified benchmark to evaluate and advance the generalizability of manipulation policies across a spectrum of tasks, environments, and difficulty levels. To alleviate the shortage of realistic data, the challenge integrates large-scale synthetic data generation with standardized real-world robotic evaluation. Participants are encouraged to leverage synthesized state-action trials to improve general-purpose policy learning, while final assessments are conducted exclusively on unseen real-world manipulation environments. Baseline implementations, including Transformer-, Diffusion-, Vision-Language-Action, and World-Action-Model-based policies, are provided to ensure reproducibility and comparability. By coupling scalable simulation-based training with rigorous real-world validation, RoboSynChallenge aims to foster the development of broadly capable, data-efficient, and adaptable manipulation systems, thereby paving the way toward truly general robotic intelligence.
Comments: NeurIPS 2026 Competition Track
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2608.12416 [cs.RO]
(or arXiv:2608.12416v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2608.12416
arXiv-issued DOI via DataCite
Submission history
From: Guiliang Liu [view email] [v1] Wed, 12 Aug 2026 04:29:00 UTC (14,072 KB)
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