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Semantic Haptic Feedback Enhances Dexterous Robotic Teleoperation
arXiv:2608.02780v1 Announce Type: new Abstract: In robot teleoperation, haptic feedback can be used to help human operators accomplish dexterous manipulation tasks. However, existing haptic feedback methods try to replicate high-fidelity sensory haptics that are felt in real world interactions, which are constrained by the sensing and feedback hardware capability and may lead to higher workload. To addresses these limitations, this work introduces semantic haptics for teleoperation, which uses abstract haptic patterns to convey critical information about robot states. We categorize robot states into "Confirmations" and "Exceptions", implement a modular haptic rendering pipeline in robot simulation, and deliver semantic haptic feedback to operators through pneumatic and vibrotactile wristbands. This simplifies hardware requirements and enables one-to-many mappings between haptic patterns and robot states. Through three evaluation studies, we identify the most effective semantic haptic d
ISEE: Interactive Semantic Enrichment for Database Fields
arXiv:2608.02604v1 Announce Type: new Abstract: LLM-based agents are increasingly being deployed for data-related tasks, including data sense-making, exploration, and retrieval. However, their performance heavily depends on the clarity and completeness of data semantics. In practice, many field descriptions remain ambiguous or incomplete, as much of the essential context (e.g., the meaning of a customized field) originates from users' domain knowledge and is rarely documented publicly. This gap restricts the agents' task performance in downstream tasks, such as entity-linking. To bridge this gap, we introduce a novel and comprehensive Interactive SEmantic Enrichment system (ISEE). Given a data field description, ISEE measures its quality through a scoring system, gathers domain knowledge, and collaboratively enriches the semantics with users. Through a user study, automated user simulation, quantitative evaluation, and case study, we demonstrate that ISEE significantly reduces cognitiv
Deep Divide-and-Reduce in Symbolic Regression
arXiv:2608.02628v1 Announce Type: new Abstract: Symbolic regression (SR) is the task of discovering underlying patterns from data and representing them using mathematical expressions. Current machine learning approaches to SR often lack a profound understanding of the intrinsic mathematical and physical principles governing these expressions. While the pioneering AI Feynman method leverages the mathematical properties underlying the data, its expression simplification mechanism suffers from a narrow scope of applicability and is prone to failure on complex equations. Furthermore, its underlying mechanisms rely heavily on brute-force searches for sub-expressions, severely limiting its practical utility. Through rigorous mathematical deduction and proofs, we propose our method, Deep Divide and Reduce in Symbolic Regression (DDRSR). DDRSR fundamentally broadens the applicability of expression decomposition and reduction, circumvents the need for brute-force sub-structure searches, and ens
Stop Explaining Tasks 47 Times: Claude Cowork Now Learns by Watching You
Anthropic introduced Record a Skill. Just do your work, talk through your reasoning aloud, and let Claude distill it into a reusable Continue reading on Medium »
Start Your AI Career with a Free Internship
Every AI engineer starts as a beginner. The difference between those who land AI jobs and those who struggle often comes down to one thing Continue reading on Medium »
# I Built an AI Resume Analyzer — Here Are the Hard Problems Nobody Talks About
Most resume checkers are either too expensive, too generic, or send your sensitive data to unknown servers. I wanted to build something Continue reading on Medium »
AI Doesn't Need More Memory. It Needs the Ability to Forget.
For decades, artificial intelligence has been designed around a simple assumption: more memory leads to better intelligence.Continue reading on Medium »
After Losses, Retail Investors Flock to 3x Leverage as 2x Product Are Restricted
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Breathe Life Into Your Content: How AI Voice Can Skyrocket Your Impact
Ever poured your heart into a presentation or online course, only to hear a polite good job! and feel like your message just didn t Continue reading on Medium »
Eight Myths on Software Engineering and GenAI
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I’m the Person No-Code Was Built For. AI Won Anyway
Airtable s $2.25 billion exit closes a chapter that people like me lived inside.Continue reading on Medium »
Lucid’s turnaround plan hinges on $1.4B in cash savings, robotaxis
Lucid's new CEO Silvio Napoli listed four must-win priorities, including the successful launch of its midsize EV, finishing a factory in Saudi Arabia, cutting expenses, and robotaxis.