A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Ahmed Tlili, Boulus Shehata, Michael Agyemang Adarkwah, et al.
A qualitative case study examining early adopters' perspectives on ChatGPT in education, revealing both enthusiasm and concerns about cheating, honesty, privacy, and manipulation.
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A survey of world models for robotic manipulation, tracing their evolution from task-specific predictors toward more general and capable systems.
Wei Li, Rui Shao, Jie He, et al.
UniMPA unifies memory, prediction, and action via an action-grounded transition interface to resolve transition realizability gaps in robotic manipulation.
Xiaowei Cai, Yunuo Cai, Bingao Chen, et al.
τ_0-VLA is a hierarchical robot foundation model that uses world-model-guided test-time computation to allocate additional inference compute for high-level subtask generation, improving long-horizon manipulation.
Bingxin Xu, Yuzhang Shang, Emilio Ferrara
BATON improves long-horizon robot manipulation by making subtasks the unit of exploration and adding transition-aware memory, boosting task success by 11.6% over SoTA.
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This paper presents a comprehensive recipe for applying sim-to-real reinforcement learning to vision-based dexterous manipulation on humanoid robots, synthesizing practical techniques and insights.
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A survey summarizing the technical evolution of robot learning and identifying future areas, focusing on manipulation and other mainstream domains in the era of foundation models.
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This paper introduces Action Chain-of-Thought (ACoT) for Vision-Language-Action models, decomposing action generation into a step-by-step reasoning process to improve manipulation performance and interpretability.
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Villa-x enhances latent action modeling in Vision-Language-Action models to improve robot manipulation policy learning and generalization.
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This paper presents a language-guided long-horizon manipulation framework for multistep cloth folding, combining LLM-based planning with visual perception.
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A framework for long-horizon planning of rigid object manipulation directly from point-cloud observations without prior models.
Yunxue Shao, Tingting Wang, Lingfeng Wang
Proposes a dual-branch network with irrelevant visual information suppression and manipulated edge information enhancement for image manipulation detection, outperforming state-of-the-art methods on multiple datasets.