robot-learning

Introduces worldproof, an open-source tool for diagnosing world models, and finds that pixel metrics often cannot rank models on real robot video because evaluation horizons lack discriminative power; a usable window of 8-24 steps is identified.

Call for papers for the Scaling H2R workshop at CoRL 2026, focusing on scaling laws and diversity in human-to-robot learning. Submissions due Oct 7, 2026.

Light Origins announces a humanoid robot powered by a single perceptive whole-body policy that autonomously decides whether to walk, climb, or vault over unseen terrain, without skill labels, state machines, or motion generators at inference.

A survey paper organizing robot-learning techniques along an axis of frozen-weight policies (VLA models) versus agents that write their own executable skills as code, providing a taxonomy of self-improvement mechanisms and analyzing the emerging robot-skill economy.

Ego2Robot is a scalable pipeline that converts egocentric human manipulation videos into robot training data via action retargeting and visual synthesis, producing 18,561 hours of data across 15 robot morphologies. Experiments show that joint pretraining on this synthesized data improves out-of-distribution generalization for vision-language-action models, including on real-robot deployment.

Sergey Levine highlights the importance of learning from suboptimal robot data and promotes OopsieData, a community effort to collect and open-source the abundant but underused failed robot interaction clips.

HiFi-UMI introduces a portable data-production system for robot-free UMI data that achieves high trajectory accuracy using stereo-inertial SLAM and wide-angle cameras. Training manipulation policies on this data alone enables zero-shot deployment on real robots, matching or exceeding teleoperation baselines across several model families, and the authors open-source a 2,000-hour high-fidelity dataset.

This paper organizes embodied data sources into a five-level pyramid (real-robot, UMI, egocentric/exocentric, simulation, general vision-language), analyzing their trade-offs between scalability and robot alignment, and reviews recent embodied foundation models in terms of data recipes. It also discusses open challenges for building next-generation embodied systems.

The article discusses the potential benefits and challenges of using first-person video for robot learning, highlighting that while direct imitation is limited, the sequence of visual attention may transfer. It references LingBot-VLA 2.0 and calls for controlled evaluations to separate viewpoint effects from data volume.

The article describes how the authors optimized the LeRobot video reader in the Daft dataframe library by batching decodes by shard, grouping rows, sorting targets, and using one seek per cluster, resulting in up to 15x faster frame decoding.

Xiaomi introduces Xiaomi-Robotics-1, a vision-language-action foundation model trained on over 100,000 hours of real-world manipulation trajectories, demonstrating clear scaling laws and achieving high success rates on real-world tasks with minimal fine-tuning data.

LeRobot v0.6.0 introduces new world models, reward models, faster data loading, and improved benchmarks for robot learning, closing the robot learning loop.

This technical report presents iFLYTEK-Embodied-Omni, a unified multimodal foundation model that jointly models vision, language, and action for embodied agents, using a brain-cerebellum collaboration architecture and a four-stage training strategy.

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.