robot-learning

SIEVE is a structure-aware data selection method for vision-language-action imitation learning that identifies reusable visuo-motor primitives and transition interfaces, outperforming full-data training with only 50% of demonstrations and training steps.

LeRobot v0.6.0 is a major release of Hugging Face's robot learning library, adding world model policies (VLA-JEPA, FastWAM, LingBot-VA), new VLAs, reward models, six simulation benchmarks, depth sensing, VLM-powered dataset annotation, custom video encoding, cloud training, and a deployment CLI for human-in-the-loop corrections.

This paper introduces WGO-Bench, a benchmark for robot video subtask annotation with 100 episodes and 743 segments, and finds that Gemini models outperform others, achieving 0.306 F1 for segmentation and 61% accuracy for labeling, with an open-source pipeline costing $2.64 per hour of video.

This paper introduces A2World, a diffusion-based world model pretrained on large-scale robot manipulation data to learn transferable dynamics priors. The model can be adapted into a real-world simulator (A2World-sim) for policy evaluation or a video-action prediction model (A2World-policy) for action prediction, demonstrating benefits for both simulator-centric and policy-centric robot learning.

Lerobot SO-101 is a framework that transforms robot learning from messy lab magic into a clean, reproducible software pipeline, enabling structured data collection, policy training, and deployment, similar to what PyTorch did for deep learning.

This paper proposes a bridging action representation based on relative wrist translation in the head-camera frame to transfer human manipulation skills to bi-manual robots, using a vision-language-action model with interleaved action tokens and attention masking to handle embodiment differences.

SimFoundry is a modular system that automates real-to-sim scene construction from video, generating digital twins and affordance-preserving variations for zero-shot robot policy training, achieving strong transfer to real-world tasks and high simulation-to-real performance prediction.

Robotics teams are rebuilding the data stack from scratch to overcome the 'data layer tax' that slows down iteration and scaling in robot learning, as existing infrastructure doesn't handle multi-rate and multimodal data.

EgoSteer presents a full-stack system that pre-trains a vision-language-action model from egocentric human videos for steerable dexterous manipulation, enabling robust generalization across 40+ diverse tasks with 75%+ success on complex long-horizon tasks.

PoLAR introduces a geometrically structured latent action representation in hyperbolic space that separates transition extent from mode, improving robotic policy learning performance.

This paper finds that egocentric human video, when processed with a filtering and labeling pipeline, can outperform teleoperated real-robot data for pretraining embodied foundation models, achieving lower validation loss and higher success rates on real-robot tasks.

Introduces Playful Agentic Robot Learning, where embodied coding agents use self-directed play to learn reusable skills, improving downstream task performance without additional training. The proposed RATs system achieves significant gains over baselines in simulation and real-world transfer.

Geometric Action Model repurposes a geometric foundation model for robot policy learning, achieving 85.5% on LIBERO-Plus with 6.9 ms inference, 55× faster than baselines.

This paper proposes Hierarchical Advantage-Weighted Behavior Cloning (HABC) for fine-tuning Vision-Language-Action (VLA) policies using online reinforcement learning with sparse binary episode outcomes. HABC separates viability and efficiency objectives via adaptive critic heads and intervention-aware credit assignment, significantly improving success rates on contact-rich bimanual manipulation tasks.

RL4IL introduces a reinforcement learning-guided retrieval method that uses soft fusion over frozen demonstration libraries to handle missing sensor modalities in robotic imitation learning at inference time, achieving high success rates under complete camera dropout.

Lucky Robots announces Lucky Engine, the first game engine purpose-built for robotics, enabling infinite data generation for robotic AI training through realistic simulation and deployment.

HyVLA-0.5 is an end-to-end robotic learning system that integrates data collection, model design, pre-training, fine-tuning, and reinforcement learning for real-world deployment.

μ_0 is a scalable world model that predicts smooth 3D trajectories for interaction points, enabling embodiment-agnostic robot learning without action labels by using a TraceExtract system for supervision.

ETH Zurich has open-sourced their entire 2026 robot learning course, including slides, lecture recordings, coding assignments, and a GitHub repository, covering topics from imitation learning to foundation models for robotics, with guest lectures from industry leaders.

This position paper argues that advancing robot intelligence requires integrating unstructured behavioral data through specialized interfaces for labeling, embodiment mapping, world modeling, and reward inference, rather than relying solely on scaling Vision-Language-Action (VLA) models and world models.