Play2Perfect (CoRL 2026): Play Pretraining Yields Precise Zero-Shot Robot Assembly
leto__jean · x · 2026-09-11
Stanford's Play2Perfect, accepted to CoRL 2026, uses a 2-stage RL pipeline: free-space "play" pretraining to build manipulation priors, then sparse-reward finetuning on contact-rich precise assembly (screwing, tight insertion, real-size plug/fork), with zero-shot sim-to-sim transfer from Isaac Sim to MuJoCo. Systematic ablations show pretraining transfers best when robots manipulate objects in-hand with fingers; from-scratch training with hand-crafted dense rewards stalls near zero. The team also generated 2 new envs from scratch with Opus 5, reusing the same RL code. An interactive browser demo is live.
Related event: Stanford's Play2Perfect accepted to CoRL 2026 with zero-shot assembly(2 posts)→
More from Embodied
- NVIDIA releases MotionBricks: real-time humanoid motion generation at 15,000 FPS with 2ms latency — _akhaliq · 2026-10-26
- Claude Code finds root backdoor in Honda Civic head unit, then debloats it — IndraVahan · 2026-10-26
- Temple health wearable opens pre-orders, users ask: why wear it before benefits are proven — abhishekcode42 · 2026-10-26
- FLASH: GPU-native deformable sim learns cloth folding in minutes on one RTX 5090 — ChongZzZhang · 2026-10-26
- Luke Wroblewski spots it: iPhone Duo's corner radius mirrors the original iPhone — LukeW · 2026-10-26
- Humanoids shattered forecasts: half-marathon record in 2026, 100m faster than Bolt — ben_j_todd · 2026-10-26