Smart Robotics: Minimizing Energy in Infrastructure Maintenance
![An end-to-end reinforcement learning framework achieves effective manipulation of articulated objects while actively regulating energy consumption by integrating RGB-D part segmentation, masked point-cloud sampling, and PointNet-based visual encoding with proprioceptive states, and enforcing an explicit energy constraint through a constrained SAC controller utilizing a Lagrangian mechanism [latex] \mathcal{L} [/latex].](https://arxiv.org/html/2602.12288v1/images/architecture.png)
A new approach to robotic manipulation uses artificial intelligence to efficiently operate and maintain complex components, reducing energy consumption and downtime.
Science
Beyond the Equation: Guiding Symbolic Regression with Scientific Insight
A new framework addresses the challenge of spurious correlations in equation discovery, ensuring that derived formulas align with established scientific principles.
Robots That Think It Through: A New Approach to Zero-Shot Manipulation
Researchers have developed a novel framework that empowers robots to tackle complex manipulation tasks without prior training by combining high-level reasoning with real-time feedback.
Mapping the Tissue Landscape: A New View of Cellular Organization
![CellScape constructs a cellular landscape by jointly modeling spatial proximity and gene co-expression, employing a dual-branch architecture that generates both spatial embeddings [latex]Z_{\text{spatial}}[/latex] and intrinsic gene expression embeddings [latex]Z_{\text{intrinsic}}[/latex], thereby enabling a nuanced understanding of cellular organization and facilitating diverse downstream analyses in spatial omics data.](https://arxiv.org/html/2602.12651v1/figs/fig1_v3.png)
Researchers have developed a powerful deep learning framework to decode the complex spatial arrangement of cells and their genomic interactions within tissues.
Let the AI Lead: Reimagining Human-Machine Collaboration
New research proposes a shift in how humans and AI work together, suggesting AI-driven workflows can boost efficiency and reduce mental strain.
Teaching Robots to Feel: Smarter Manipulation with Force-Aware Learning
![Across five manipulation tasks, a curriculum informed by force feedback-specifically, the CRAFT enhancement to both [latex]\pi_0[/latex]-base and RDT models-consistently elevates task success rates, with particularly pronounced improvements observed in scenarios demanding substantial physical contact, thus demonstrating the efficacy of force-aware fine-tuning.](https://arxiv.org/html/2602.12532v1/x4.png)
A new framework prioritizes tactile feedback to help robots master complex, contact-rich manipulation tasks.
Beyond Chatbots: Building Truly Skilled AI Agents
![Agent skills are structured through a progressive disclosure architecture, loading information in stages to efficiently manage context window limitations while retaining access to complex procedural knowledge, with token estimations reflecting per-skill averages as detailed in prior work [35].](https://arxiv.org/html/2602.12430v1/x1.png)
A new paradigm is emerging where large language models aren’t just responding to prompts, but actively learning and applying specific skills to complete complex tasks.
A Robot’s Sense of Touch: Seeing *and* Hearing Fabric
Researchers have enhanced robotic tactile sensing by fusing internal vision with audio input, allowing robots to identify fabrics with human-like accuracy.
Building Research Software, Alone, With AI
A new framework offers practical guidance for researchers leveraging artificial intelligence to develop software independently, prioritizing both methodological rigor and personal learning.
The Empathetic Tutor: Designing Social Robots for Higher Education
As generative social robots move into classrooms, careful consideration of their underlying knowledge is crucial for effective and responsible learning support.