LIBERO is a robot manipulation benchmark: 130 tasks across five suites (spatial, object, goal, scene10, scene90), each with human demos and a language goal. It is the standard testbed for language-conditioned imitation, and it normally runs on robosuite with CPU MuJoCo, one environment at a time. I ported all of it to MuJoCo Warp and ran it on an RX 9070 XT ($700, 16 GB, RDNA4). Physics does 17,137 env-steps/s at 2,048 worlds; CPU robosuite does 38. A 50-epoch BC transformer gets 42.5% on Warp vs 50% on CPU. Why the GPU matters: behavioral cloning needs rollouts. Run the policy, watch where it fails, and generate labels or demos from that. On CPU it is one env at a time, and generating demos for a single suite (10 tasks) took me 8 to 9 hours. On the GPU it is minutes, so you can iterate instead of running it once. Why Warp on AMD is the interesting part: Warp is NVIDIA's GPU sim framework, and the AMD HIP/ROCm port is recent (Tomas Thoresen, Strix Halo). Getting it working on RDNA4, with Warp compiling HIP kernels for gfx1201, JAX seeing rocm:0, and PyTorch seeing cuda, is what made this possible. The renderer was the annoying bit. A BC policy is a fixed function of the pixels, and Warp's ray tracer is not MuJoCo's CPU renderer. My first Warp eval scored 0%. Four fixes got it to 42.5%: vertical flip, shadow constant 0.3 -> 0.0, 1.15x brightness, and cube-map sampling (the table wood grain rendered flat). Brightness alone was worth 12.5 points. Everything builds from public sources on ROCm, and the example plus a 21.6 MB BC checkpoint are in the repo. What I didn't finish: the Warp path collects rollouts, training is still offline in PyTorch. I worked on the R9700 and some Instinct cards at AMD over the summer, so in-loop vision is next. Writeup: https://amohan.dev/blog/2026/libero-warp-mjx-rdna4/ Code: https://github.com/poad42/libero_mjx   submitted by   /u/one_does_not_just [link]   [comments]