aiqbench analysis
This arXiv paper proposes conditioning a diffusion model on skill-level information, using learnable skill tokens and skill-specific trajectory priors, to generate object-centric motion flow that is then mapped to 3D robot actions. The reported gains rest on a new simulation dataset (IsaacSkill) and some real-world tests, so readers should check how well the results transfer beyond the authors' setup. The main idea to watch is grouping tasks by shared motion structure rather than modeling each task independently.
Skill-Aware Diffusion for Generalizable Robotic Manipulation
arXiv:2601.11266v2 Announce Type: replace Abstract: Robust generalization in robotic manipulation is crucial for robots to adapt flexibly to diverse environments. Existing methods usually improve generalization by scaling data and networks, but model tasks independently and overlook skill-level information. Observing that tasks within the same skill share similar motion patterns, we propose Skill-Aware Diffusion (SADiff), which explicitly incorporates skill-level information to improve generalization. SADiff learns skill-specific representations through a skill-aware encoding module with learnable skill tokens, and conditions a skill-constrained diffusion model to generate object-centric motion flow. A skill-retrieval transformation strategy further exploits skill-specific trajectory priors to refine the mapping from 2D motion flow to executable 3D actions. Furthermore, we introduce IsaacSkill, a high-fidelity dataset containing fundamental robotic skills for comprehensive evaluation and sim-to-real transfer. Experiments in simulation and real-world settings show that SADiff achieves good performance and generalization across various manipulation tasks. Dataset and videos are available at https://sites.google.com/view/sa-diff.
Abstract:Robust generalization in robotic manipulation is crucial for robots to adapt flexibly to diverse environments. Existing methods usually improve generalization by scaling data and networks, but model tasks independently and overlook skill-level information. Observing that tasks within the same skill share similar motion patterns, we propose Skill-Aware Diffusion (SADiff), which explicitly incorporates skill-level information to improve generalization. SADiff learns skill-specific representations through a skill-aware encoding module with learnable skill tokens, and conditions a skill-constrained diffusion model to generate object-centric motion flow. A skill-retrieval transformation strategy further exploits skill-specific trajectory priors to refine the mapping from 2D motion flow to executable 3D actions. Furthermore, we introduce IsaacSkill, a high-fidelity dataset containing fundamental robotic skills for comprehensive evaluation and sim-to-real transfer. Experiments in simulation and real-world settings show that SADiff achieves good performance and generalization across various manipulation tasks. Dataset and videos are available at this https URL.
Robotics (cs.RO)
arXiv:2601.11266 [cs.RO]
(or arXiv:2601.11266v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2601.11266 Focus to learn more arXiv-issued DOI via DataCite
IEEE Transactions on Automation Science and Engineering, vol. 23, pp. 17635-17650, 2026
https://doi.org/10.1109/TASE.2026.3740160 Focus to learn more DOI(s) linking to related resources
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Full text under the publisher's open licence · 259 words
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