Tactile Feedback Closes the Loop: Shipping Manipulation Agents That Learn from Touch

Research

Tactile Policies: End-to-End Manipulation from Touch Sensing

This paper demonstrates a system where tactile feedback directly informs policy learning for manipulation tasks. It's a step toward closing the loop between sensing and action without human-in-the-loop supervision.

Efficient Policy Learning with Limited Real-World Data

This research tackles the data bottleneck in robotics by proposing methods for learning effective policies from minimal real-world interactions. It's a practical approach for builders with constrained datasets.

Analysis

VLA Failure Modes: Why Foundation Models Still Struggle with Robotic Control

A critical analysis of Vision-Language-Action models highlights their failure modes in real-world robotic settings. It's a sobering reminder that foundation models aren't a silver bullet for embodied AI yet.

Tools

NVIDIA Isaac Sim: Updated Tools for Sim-to-Real Transfer

NVIDIA has rolled out updates to its Isaac Sim platform, focusing on improving the fidelity and speed of sim-to-real transfer. These tools are becoming increasingly critical for shipping robust agents.

Events

ROSCon 2025: Community Momentum and New Middleware Patterns

The ROSCon 2025 agenda reveals a shift toward more modular and composable middleware for robotics. This reflects the industry's need for scalable, maintainable systems as deployments grow.