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Paper#robotics🔬 ResearchAnalyzed: Jan 3, 2026 19:22

Robot Manipulation with Foundation Models: A Survey

Published:Dec 28, 2025 16:05
1 min read
ArXiv

Analysis

This paper provides a structured overview of learning-based approaches to robot manipulation, focusing on the impact of foundation models. It's valuable for researchers and practitioners seeking to understand the current landscape and future directions in this rapidly evolving field. The paper's organization into high-level planning and low-level control provides a useful framework for understanding the different aspects of the problem.
Reference

“The paper emphasizes the role of language, code, motion, affordances, and 3D representations in structured and long-horizon decision making for high-level planning.”

PermalinkArXiv
Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 10:21

Towards a logic of affordances

Published:Dec 4, 2025 19:34
1 min read
ArXiv

Analysis

This article, sourced from ArXiv, suggests a focus on developing a logical framework for understanding affordances. The title indicates a research direction, likely exploring how AI can perceive and reason about the possibilities for action that an environment offers. The absence of further context makes a detailed critique impossible, but the title itself is promising for the field of AI and robotics.

Key Takeaways

    Reference

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    PermalinkArXiv
    Research#AI Cognitive Abilities📝 BlogAnalyzed: Jan 3, 2026 06:25

    Affordances in the brain: The human superpower AI hasn’t mastered

    Published:Jun 23, 2025 02:59
    1 min read
    ScienceDaily AI

    Analysis

    The article highlights a key difference between human and AI intelligence: the ability to understand affordances. It emphasizes the automatic and context-aware nature of human understanding, contrasting it with the limitations of current AI models like ChatGPT. The research suggests that humans possess an intuitive grasp of physical context that AI currently lacks.
    Reference

    “Scientists at the University of Amsterdam discovered that our brains automatically understand how we can move through different environments... In contrast, AI models like ChatGPT still struggle with these intuitive judgments, missing the physical context that humans naturally grasp.”

    PermalinkScienceDaily AI
    Research#Robotics📝 BlogAnalyzed: Dec 29, 2025 08:29

    Towards Abstract Robotic Understanding with Raja Chatila - TWiML Talk #118

    Published:Mar 12, 2018 20:18
    1 min read
    Practical AI

    Analysis

    This article summarizes a podcast episode featuring Raja Chatila, a prominent figure in robotics and AI ethics. The discussion centers on Chatila's research, focusing on robotic perception, learning, and discovery. Key topics include the relationship between learning and discovery in robots, the connection between perception and action, and the exploration of advanced concepts like affordances, meta-reasoning, and self-awareness. The episode also addresses the crucial ethical considerations surrounding intelligent and autonomous systems, reflecting Chatila's role in the IEEE global initiative on ethics.
    Reference

    “We discuss the relationship between learning and discovery, particularly as it applies to robots and their environments, and the connection between robotic perception and action.”

    PermalinkPractical AI