Robocon2025

Robots That Know Where They Are on the Basketball Court

Kari Jaaskelainen

14 Jan 2026 — 1 min read

Teaching Basketball Robots to Know Where They Are

At Robocon 2025, split-second positioning can be the difference between a swish and a miss. Naren Medarametla and Sreejon Mondal present a real-time localization framework that helps autonomous basketball robots figure out their spot on the court—using only what they see on the floor.

Hybrid brain: Combines classical robotics techniques with learning-based methods.
Floor-only vision: No GPS or beacons—just visual cues from the court surface.
On-court impact: Sharper shooting, safer collision avoidance, and smarter navigation.

When robots know where they are, every pass, drive, and shot gets smarter.

Paper: Real-Time Localization Framework for Autonomous Basketball Robots

Authors: Naren Medarametla, Sreejon Mondal

arXiv: https://arxiv.org/abs/2601.08713v1

Paper: https://arxiv.org/abs/2601.08713v1

Register: https://www.AiFeta.com

Robocon2025 Robotics AI ComputerVision AutonomousRobots Localization SportsTech

Read more

Meet GRACE: a moral governor for safer, more transparent AI

Meet GRACE: a moral governor for safer, more transparent AI

AI agents are getting powerful—so how do we make sure they do the right thing, not just the effective thing? Meet GRACE, a reason-based moral governor that keeps AI behavior aligned with human norms by separating moral reasoning from goal-driven decision-making. * Moral Module: uses deontic logic and explicit reasons

Why some fine-tuned LLMs miss phishing—and how to fix it

Why some fine-tuned LLMs miss phishing—and how to fix it

Not all fine-tuned LLMs spot phishing equally. A new study tests Llama 3.1 8B, Gemma 2 9B, and Mistral on high-stakes phishing detection—and uses SHAP and mechanistic interpretability to reveal why models do (or don’t) generalize. * Architecture × data diversity matters: Gemma 2 9B hits state-of-the-art performance (F1

AI that explains itself—by following the science

AI that explains itself—by following the science

AI that explains itself—by following the science Black-box AI can be powerful, but it often can’t tell us why it made a decision. Concept Bottleneck Models (CBMs) try to fix that by predicting human-understandable concepts first, then the final answer. The catch: standard CBMs ignore domain-specific cause-and-effect and

Meet GenomAgent: A Team of AI Specialists for Smarter Genomics Q&A

Meet GenomAgent: A Team of AI Specialists for Smarter Genomics Q&A

TL;DR: Finding reliable answers in genomics is hard. GenomAgent turns one big AI into a coordinating team of specialists—and beats the current leader by 12% on a key benchmark. Genomic facts live across many databases. Standard chatbots struggle because they can’t flexibly query those sources. GeneGPT added