# Learning Motion Skills with Adaptive Assistive Curriculum Force in Humanoid Robots

arXiv: https://arxiv.org/abs/2506.23125 · HTML: https://mlsanity.com/paper/2506.23125/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2025.

## Built on by (most cited first, 3 of 3)

- [ZEST: Zero-shot Embodied Skill Transfer for Athletic Robot Control](https://mlsanity.com/paper/2602.00401/) (2026)
- [WARL: Wrench-Augmented Reinforcement Learning for Task-Agnostic Learning in Legged Robots](https://mlsanity.com/paper/2607.24036/) (2026)
- [FootQuery: Future-Touchdown-Guided Retrieval from Depth History for Perceptive Humanoid Locomotion](https://mlsanity.com/paper/2609.21447/) (2026)

## Abstract (arXiv)

Learning policies for complex humanoid tasks remains both challenging and compelling. Inspired by how infants and athletes rely on external support--such as parental walkers or coach-applied guidance--to acquire skills like walking, dancing, and performing acrobatic flips, we propose A2CF: Adaptive Assistive Curriculum Force for humanoid motion learning. A2CF trains a dual-agent system, in which a dedicated assistive force agent applies state-dependent forces to guide the robot through difficult initial motions and gradually reduces assistance as the robot's proficiency improves. Across three benchmarks--bipedal walking, choreographed dancing, and backflip--A2CF achieves convergence 30% faster than baseline methods, lowers failure rates by over 40%, and ultimately produces robust, support-free policies. Real-world experiments further demonstrate that adaptively applied assistive forces significantly accelerate the acquisition of complex skills in high-dimensional robotic control.