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Progress Reward Modeling for Robotic Learning: A Comprehensive Survey
arXiv:2607.21655v1 Announce Type: new Abstract: Robotic learning takes place in dynamic environments with large behavior spaces. A terminal success signal only tells the robot whether the task is completed. It does not explain whether the current behavior is making progress, remaining unchanged, or undoing earlier progress. For this reason, recent studies have increasingly explored progress rewards that provide feedback during task execution. However, the current literature lacks a shared framework. Existing methods use different observations, goal specifications, output signals, supervision sources, and evaluation protocols. This makes it difficult to compare them and understand what their results actually validate. In this survey, we provide a unified view of progress reward modeling for robotic learning. We organize the field in three connected steps. We first study the interface of a progress model. This defines the problem from the outside by asking what information the model rece
Learning Diverse Humanoid Tasks via Synthetic Video Scenarios without Real World Data
arXiv:2607.21648v1 Announce Type: new Abstract: The human-like morphology of humanoid robots grants them exceptional potential for agile and versatile motor capabilities, but it also introduces significant challenges in acquiring complex skills. Traditional Learning-from-Demonstrations methods are often constrained by the high cost of collecting real-world data, the difficulty of capturing motion-specific behaviors, and the limited diversity of demonstrations across individuals. Moreover, even for the same task, humans may execute the motion in multiple distinct ways. In this paper, we propose a new framework that leverages the power of Generative AI to convert textual prompts into realistic and diverse sequences of human body movements, enabling the robot to observe multiple variations of how a single task can be performed. These synthetic demonstrations are then used as a training resource, allowing the robot to learn a broad range of task-execution styles without requiring direct hu
How to Efficiently Prompt Claude Code
Maximize your efficiency with Claude Code The post How to Efficiently Prompt Claude Code appeared first on Towards Data Science.
How to Give an LLM Agent a Browser
Building a browser-use agent with OpenAI Agents SDK and Playwright MCP The post How to Give an LLM Agent a Browser appeared first on Towards Data Science.
Cracking the Data Science Case Study Interview
Data science case study interviews are not just about writing code. They test how you think through a problem, analyze data, make decisions, and explain your approach in a way that solves a real business challenge. In this guide, you ll learn a simple framework called SCOPE that you can use to approach almost any data [ ] The post Cracking the Data Science Case Study Interview appeared first on Analytics Vidhya.
A Complete Guide to AI Red-Teaming (With Garak Tutorial)
Earlier this year, an autonomous AI agent breached McKinsey s internal AI platform using nothing more than an old SQL injection flaw. No credentials. No human guidance. Less than two hours. It reached production systems, exposing millions of chat messages and hundreds of thousands of files. AI security has changed, and traditional assumptions no longer hold. [ ] The post A Complete Guide to AI Red-Teaming (With Garak Tutorial) appeared first on Analytics Vidhya.
How to Optimize Vector Search When RAM Gets Too Expensive: On-Disk vs. In-Memory ANN Indexes
Architecting cost-effective infrastructure by navigating the latency and storage trade-offs of HNSW, SPANN, and DiskANN The post How to Optimize Vector Search When RAM Gets Too Expensive: On-Disk vs. In-Memory ANN Indexes appeared first on Towards Data Science.
The Fluid Simulator That Doesn’t Solve the Fluid Equations
I generated a Kármán vortex street without solving a single fluid equation. Here's how the Lattice Boltzmann Method gets there instead, derived from first principles, implemented in C++, and run on a supercomputer. The post The Fluid Simulator That Doesn’t Solve the Fluid Equations appeared first on Towards Data Science.
Tabular LLMs: An Introduction to the Foundation Models That Predict Your Spreadsheet
Tabular foundation models predict the missing column of any spreadsheet zero-shot, the way an LLM completes text — and on the TabArena benchmark they now sit above fully tuned gradient-boosted trees. An introduction to how they work, an independent reproduction of the strongest open one, and a map of where XGBoost still wins. The post Tabular LLMs: An Introduction to the Foundation Models That Predict Your Spreadsheet appeared first on Towards Data Science.
Build and Run an Intelligent Document Processing (IDP) System in the Cloud
Automating the classification and extraction of PII from emails using AWS The post Build and Run an Intelligent Document Processing (IDP) System in the Cloud appeared first on Towards Data Science.
Grok Build CLI vs Claude Code: I Tested Both So You Don’t Have To
For months, Claude Code has been the go to terminal coding agent for developers. Then Grok Build arrived in beta on May 14, 2026, giving developers a second serious option and raising a new question: which one actually performs better? I tested both agents on the same real world coding tasks using identical prompts to [ ] The post Grok Build CLI vs Claude Code: I Tested Both So You Don t Have To appeared first on Analytics Vidhya.
Loop Engineering for RAG Generation: An LLM Cascade from a Cheap Local Model Up to a Hosted Flagship
Enterprise Document Intelligence [Vol.1 #8quater] - Two angles on the cascade, cost and a validation loop, backed by a real sweep of twenty local models against a hosted flagship The post Loop Engineering for RAG Generation: An LLM Cascade from a Cheap Local Model Up to a Hosted Flagship appeared first on Towards Data Science.