# CHOREO: Every Humanoid Skill as a Trajectory

arXiv: https://arxiv.org/abs/2609.22274 · HTML: https://mlsanity.com/paper/2609.22274/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods.
- Builds on 18 of its 24 references that are in the map.
- First posted on arXiv 11 Sep 2026.

## Placed in

- Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/)

## Builds on (most cited first, 18 of 18)

- [Perpetual Humanoid Control for Real-time Simulated Avatars](https://mlsanity.com/paper/2305.06456/) (2023)
- [OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body Teleoperation and Learning](https://mlsanity.com/paper/2406.08858/) (2024)
- [Expressive Whole-Body Control for Humanoid Robots](https://mlsanity.com/paper/2402.16796/) (2024)
- [ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skills](https://mlsanity.com/paper/2502.01143/) (2025)
- [MaskedMimic: Unified Physics-Based Character Control Through Masked Motion Inpainting](https://mlsanity.com/paper/2409.14393/) (2024)
- [CLoSD: Closing the Loop between Simulation and Diffusion for multi-task character control](https://mlsanity.com/paper/2410.03441/) (2024)
- [SONIC: Supersizing Motion Tracking for Natural Humanoid Whole-Body Control](https://mlsanity.com/paper/2511.07820/) (2025)
- [Harness VLA: Steering Frozen VLAs into Reliable Manipulation Primitives via Memory-Guided Agents](https://mlsanity.com/paper/2607.08448/) (2026)
- [Perceptive Humanoid Parkour: Chaining Dynamic Human Skills via Motion Matching](https://mlsanity.com/paper/2602.15827/) (2026)
- [RoboHarness: Memory-Driven Orchestration of Heterogeneous Robot Policies for Long-Horizon Planning](https://mlsanity.com/paper/2607.18060/) (2026)
- [Guava: Distilling Frontier VLMs into a Compact Agent through a Robotic Manipulation Harness](https://mlsanity.com/paper/2606.18363/) (2026)
- [Towards the Harness of Embodied Agents](https://mlsanity.com/paper/2608.11246/) (2026)
- [RPG: Robust Policy Gating for Smooth Multi-Skill Transitions in Humanoid Fighting](https://mlsanity.com/paper/2604.21355/) (2026)
- [CEER: Compliant End-Effector and Root Control as a Unified Interface for Hierarchical Humanoid Loco-Manipulation](https://mlsanity.com/paper/2605.19981/) (2026)
- [Humanoid Hanoi: Investigating Shared Whole-Body Control for Skill-Based Box Rearrangement](https://mlsanity.com/paper/2602.13850/) (2026)
- [HANDOFF: Humanoid Agentic Task-Space Whole-Body Control via Distilled Complementary Teachers](https://mlsanity.com/paper/2606.06493/) (2026)
- [OmniContact: Chaining Meta-Skills via Contact Flow for Generalizable Humanoid Loco-Manipulation](https://mlsanity.com/paper/2606.26201/) (2026)
- [Switch: Learning Agile Skills Switching for Humanoid Robots](https://mlsanity.com/paper/2604.14834/) (2026)

## Built on by (most cited first, 1 of 1)

- [InterEvolve: Test-Time Evolution of Reward Programs for Humanoid Loco-Manipulation](https://mlsanity.com/paper/2610.02196/) (2026)

## Abstract (arXiv)

Recent advances in humanoid robotics have produced diverse skills through reinforcement learning, motion imitation, and generative modeling. Yet these capabilities remain siloed because they are built around incompatible representations, interfaces, and controllers. We present CHOREO, a framework for training-free composition of heterogeneous humanoid skills. Our key observation is that, regardless of how a skill is learned, it can ultimately be expressed as an executable motion trajectory. Based on this observation, CHOREO converts each capability into SkillMotion, a unified representation that combines motion states, contacts, semantics, and boundary conditions. Skills are composed through direct continuation, cubic Hermite blending, or validated bridge motions, without retraining source models or updating models at test time. On Unitree G1 in MuJoCo, CHOREO organizes 2,950 admitted SkillMotion assets derived from heterogeneous sources and achieves 95.4% sequence success across 130 multi-action tasks, including 93.8% success on eight-action sequences. These results demonstrate that executable trajectories provide a scalable interface for accumulating and composing pretrained humanoid capabilities.