gr00t-n1-5-2f8d9056·3 events·first seen Aliases: GR00T N1.5, GR00T N1.6, GR00T-N1.7
Researchers introduce πR², a method that makes large action-chunking flow policies reactive and real-time by splitting conditioning into fast proprioception and slow vision-language channels, plus a latency-adaptive denoising schedule. Applied to NVIDIA's GR00T-N1.7 on a real xArm6+XHand platform, it achieves ~25Hz closed-loop replanning (roughly 4× faster than the base policy), acting on fresh observations every 40ms. The approach improves task success rates by up to 23% in simulation and 30% in real-world manipulation over the strongest baseline, requiring only minimal architectural modification and fine-tuning from a pretrained checkpoint.
Researchers present DEED (Data-Efficient Post-Training and Experience-Driven Learning), a systems-level framework for deploying Vision-Language-Action humanoid robots in real retail environments. The approach is evaluated on a supermarket chip-restocking task using a Unitree G1-Edu humanoid and NVIDIA's GR00T N1.6 foundation model, combining data-efficient post-training, experience-driven refinement adapted from RECAP, and a latent-space analysis tool. Key finding: bridging lab-to-real performance gaps is primarily a systems integration challenge rather than an architectural one, achievable with careful data design on a single GPU.
Omega-QVLA is a post-training quantization framework that compresses both the LLM backbone and the diffusion-based action head of VLA models to uniform W4A4 precision without mixed-precision schemes or fine-tuning. It combines composite SVD-Hadamard rotation for weight energy equalization with per-step DiT activation scaling to handle dynamic-range drift across denoising steps. On the LIBERO benchmark, it achieves 98.0% and 87.8% task success on Pi 0.5 and GR00T N1.5 respectively—matching or exceeding FP16 baselines—while reducing static memory footprint by 71.3%. Real-world manipulation experiments confirm the approach generalizes beyond simulation.