Pixel2Catch: Multi-Agent Sim-to-Real Transfer for Agile Manipulation With a Single RGB Camera

Legged robots must achieve both robust locomotion and energy efficiency to be practical in real-world environments. Yet controllers trained in simulation often fail to transfer reliably, and most existing approaches are limited to specific robot morphologies. This paper presents a systematic framework for sim-to-real transfer across diverse legged robot platforms.

Introduces a benchmark with 1,218 embodied episodes that decouples high-level VLM action decisions from low-level motor control; no current VLM solves the tasks, with the best achieving only 16.8% success, highlighting lack of embodied self-awareness.

Real-to-Sim-to-Real: Learning Agile and Robust Recovery Skills With Terrain Imagination via Adversarial Imitation

Partially Observable Adversarial Patch Attacks on Vision-Language-Action Models in Robotics

Presents a scalable approach to robot task learning enabling acquisition of a large number of tasks within a single day. Details were discussed in a podcast episode released July 30, 2026.

Collects 1M frames (27.8 hours) of egocentric human grasping video and trains a flow-matching model to predict and retarget hand poses to robots. Achieves large zero-shot gains on diverse everyday grasping tasks without requiring robot-specific training data.

SkillPlug: Unsupervised Skill Mining for Few-Shot Adaptation in Robotic Manipulation

Token Expand-Merge: Training-Free Token Compression for Vision-Language-Action Models

Trains a single VLA policy that enables multiple heterogeneous robots to collaborate on complex manipulation tasks using only local observations and robot IDs, avoiding centralized controllers.