Effective robot autonomy requires motion generation that is safe, feasible, and reactive. Current methods are fragmented: fast planners output physically unexecutable trajectories, reactive controllers struggle with high-fidelity perception, and existing solvers fail on high-DoF systems. We present cuRoboV2, a unified framework with three key innovations: (1) B-spline trajectory optimization that enforces smoothness and torque limits; (2) a GPU-native TSDF/ESDF perception pipeline that generates dense signed distance fields covering the full workspace, unlike existing methods that only provide distances within sparsely allocated blocks, up to 10x faster and in 8x less memory than the state-of-the-art at manipulation scale, with up to 99% collision recall; and (3) scalable GPU-native whole-body computation, namely topology-aware kinematics, differentiable inverse dynamics, and map-reduce self-collision, that achieves up to 61x speedup while also extending to high-DoF humanoids (where previous GPU implementations fail). On benchmarks, cuRoboV2 achieves 99.7% success under 3kg payload (where baselines achieve only 72--77%), 99.6% collision-free IK on a 48-DoF humanoid (where prior methods fail entirely), and 89.5% retargeting constraint satisfaction (vs. 61% for PyRoki); these collision-free motions yield locomotion policies with 21% lower tracking error than PyRoki and 12x lower cross-seed variance than GMR. A ground-up codebase redesign for discoverability enabled LLM coding assistants to author up to 73% of new modules, including hand-optimized CUDA kernels, demonstrating that well-structured robotics code can unlock productive human-LLM collaboration. Together, these advances provide a unified, dynamics-aware motion generation stack that scales from single-arm manipulators to full humanoids. Code is available at https://github.com/NVlabs/curobo.

翻译：有效的机器人自主性需要生成安全、可行且具有反应性的运动。现有方法存在碎片化问题：快速规划器输出物理上不可执行的轨迹，反应式控制器难以处理高保真感知，而现有求解器在高自由度系统上失效。我们提出统一框架cuRoboV2，包含三项关键创新：（1）B样条轨迹优化，确保平滑性与力矩限制；（2）GPU原生TSDF/ESDF感知流水线，生成覆盖完整工作空间的密集符号距离场（区别于仅提供稀疏块内距离的现有方法），在操作尺度上比现有最优方案快10倍且内存占用减少8倍，碰撞召回率高达99%；（3）可扩展的GPU原生全身计算，包括拓扑感知运动学、可微逆动力学及映射归约自碰撞检测，实现最高61倍加速，同时扩展至高自由度人形机器人（先前GPU实现在此场景下失效）。在基准测试中：cuRoboV2在3kg负载下达到99.7%成功率（基线仅72-77%），48自由度人形机器人上实现99.6%无碰撞逆解（先前方法完全失败），重定向约束满足率达89.5%（PyRoki为61%）；基于这些无碰撞运动生成的步态策略相比PyRoki追踪误差降低21%，跨种子方差相比GMR降低12倍。通过针对可发现性进行的代码库重构，LLM编码助手可自主编写多达73%的新模块（包括手调CUDA内核），证明结构良好的机器人代码能激发高效的人机协作。这些进步共同构成从单臂操作器到全身人形机器人的统一动力学感知运动生成栈。代码已开源：https://github.com/NVlabs/curobo