LeRobot v0.6.0 Adds World Models, Reward Models, and an Open GR00T

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LeRobot v0.6.0 Adds World Models, Reward Models, and an Open GR00T<br>Weekly Physical AI Roundup.<br>Jay Chia<br>Jul 09, 2026

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The open-robotics stack had a big week. LeRobot v0.6.0 pulled world-model policies, reward models, an eval-and-rollout loop, and a frontier open VLA into the framework most robot-learning work already uses.

LeRobot v0.6.0 closes the learning loop

LeRobot v0.6.0 is built around closing the learning loop. It adds three world-model policies that imagine the future (VLA-JEPA, LingBot-VA, FastWAM), a reward-models API (Robometer, TOPReward), a lerobot-rollout CLI with DAgger-style human-in-the-loop corrections that turn failures into training data, and six simulation benchmarks unified under lerobot-eval. The marquee model is NVIDIA’s GR00T N1.7, a 3B open reasoning VLA released in April and trained on roughly 20,000 hours of human egocentric video under what NVIDIA calls a dexterity scaling law. It’s now the default VLA in LeRobot in place of N1.5 and ships with Isaac Teleop for data collection, reporting around 88% average on LIBERO in a preliminary integration, though running it needs an NVIDIA GPU and access to its gated Cosmos-Reason2 backbone. VLA-JEPA is the one to look at, training a JEPA world model to predict future frames from the policy’s own actions and then dropping it at inference for zero added cost, with DROID-pretrained checkpoints on the Hub. Datasets also gain depth support, automatic VLM language annotation, and up to 2x faster loading.

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Research

Robbyant open-sources a 60,000-hour VLA

Ant Group’s Robbyant open-sourced LingBot-VLA 2.0, pre-trained on what the company describes as 60,000 hours of physical data, 50,000 hours of cleaned real-robot interaction plus 10,000 hours of distilled first-person human video, across 20 robot morphologies from 17 makers. On SJTU’s GM-100 dual-arm benchmark the company reports it ahead of π0.5 and GR00T N1.7, so treat that ranking as a single-benchmark vendor claim; the reusable part is the data recipe, a 5-to-1 blend of real-robot and human-video hours. A companion 1B vision model, LingBot-Vision, was opened alongside it.

Mistral’s first robotics model navigates from a single camera

Robostral Navigate is an 8B navigation model that takes an RGB image and a plain-language instruction and moves a robot to the target, predicting where to go by pointing at image coordinates rather than metric displacements, which keeps it robust to camera and scale changes. Trained entirely in simulation and running on wheeled, legged, and flying robots, Mistral reports 76.6% on R2R-CE validation-unseen from a single camera, above depth and multi-camera systems, though it’s a vendor number on one benchmark with no weights or paper released. It’s also Mistral’s first robotics model, part of a physical-AI push that followed its Emmi AI acquisition.

GigaWorld-1 asks what makes a world model a trustworthy evaluator

Evaluating robot policies means slow, costly real rollouts, which is why people want world models to stand in as evaluators. GigaWorld-1, from GigaAI and Tsinghua, builds WMBench from real teleop paired with matched policy rollouts and studies 7 video world models across 324,000+ simulated rollouts. Its main finding is that evaluator quality tracks long-horizon, action-faithful rollout consistency rather than short-term visual realism, which cuts against the instinct to chase sharper generated video.<br>Deform360 puts 2D and 3D world models head-to-head on deformables

Deform360 (Brown, Columbia, MIT) is a real-world dataset for deformable-object dynamics: 198 everyday objects, 1,980 interaction sequences, 215+ hours from 41 surround cameras and bimanual tactile grippers, with a markerless visuotactile tracking pipeline for dense geometry and motion. The authors use it to compare 2D video world models against 3D particle models on the same deformable interactions, which is a controlled read on a case both paradigms struggle with.<br>InternVLA-A1.5 gets world-model dynamics into a VLA without generating pixels

InternVLA-A1.5, from Shanghai AI Lab, keeps a VLM backbone training on VQA and subtask prediction and adds learnable foresight tokens that condense the task-relevant future into a compact latent, supervised by a frozen video generator. The video branch is dropped at inference, so the policy inherits dynamics priors while staying real-time. Pretrained on 1.2M robot episodes plus 3M multimodal samples, it reports the best overall results across six simulation benchmarks.<br>Does a VLA’s reasoning reflect what it actually does?

This paper from Stanford and NVIDIA Research separates functional reasoning, where a chain of thought improves performance, from faithful reasoning, where it reflects the policy’s real decision process, and shows the two come...