# EFGCL: Learning Dynamic Motion through Spotting-Inspired External Force Guided Curriculum Learning

arXiv: https://arxiv.org/abs/2605.10063 · HTML: https://mlsanity.com/paper/2605.10063/

## 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 15 of its 20 references that are in the map.
- First posted on arXiv 11 May 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, 15 of 15)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Learning agile and dynamic motor skills for legged robots](https://mlsanity.com/paper/1901.08652/) (2019)
- [Learning robust perceptive locomotion for quadrupedal robots in the wild](https://mlsanity.com/paper/2201.08117/) (2022)
- [Learning Agile Robotic Locomotion Skills by Imitating Animals](https://mlsanity.com/paper/2004.00784/) (2020)
- [Robot Parkour Learning](https://mlsanity.com/paper/2309.05665/) (2023)
- [GPU-Accelerated Robotic Simulation for Distributed Reinforcement Learning](https://mlsanity.com/paper/1810.05762/) (2018)
- [Learning Agile Skills via Adversarial Imitation of Rough Partial Demonstrations](https://mlsanity.com/paper/2206.11693/) (2022)
- [OPT-Mimic: Imitation of Optimized Trajectories for Dynamic Quadruped Behaviors](https://mlsanity.com/paper/2210.01247/) (2022)
- [Robust Quadruped Jumping via Deep Reinforcement Learning](https://mlsanity.com/paper/2011.07089/) (2020)
- [High-speed control and navigation for quadrupedal robots on complex and discrete terrain](https://mlsanity.com/paper/2506.02835/) (2025)
- [Curriculum-Based Reinforcement Learning for Quadrupedal Jumping: A Reference-free Design](https://mlsanity.com/paper/2401.16337/) (2024)
- [Revisiting Sparse Rewards for Goal-Reaching Reinforcement Learning](https://mlsanity.com/paper/2407.00324/) (2024)
- [Robust Ladder Climbing with a Quadrupedal Robot](https://mlsanity.com/paper/2409.17731/) (2024)
- [KLEIYN : A Quadruped Robot with an Active Waist for Both Locomotion and Wall Climbing](https://mlsanity.com/paper/2507.06562/) (2025)

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

- [WARL: Wrench-Augmented Reinforcement Learning for Task-Agnostic Learning in Legged Robots](https://mlsanity.com/paper/2607.24036/) (2026)

## Abstract (arXiv)

Learning dynamic whole-body motions for legged robots through reinforcement learning (RL) remains challenging due to the high risk of failure, which makes efficient exploration difficult and often leads to unstable learning. In this paper, we propose External Force Guided Curriculum Learning (EFGCL), a guided RL approach based on the principle of physical guidance, in which external assistive forces are introduced during training. Inspired by spotting in artistic gymnastics, EFGCL enables agents to physically experience successful motion executions without relying on task-specific reward shaping or reference trajectories. Experiments on a quadrupedal robot performing Jump, Backflip, and Lateral-Flip tasks demonstrate that EFGCL accelerates learning of the Jump task by approximately a factor of two and enables the acquisition of complex whole body motions that conventional RL methods fail to learn. We further show that the learned policies can be deployed on real robot, reproducing motions consistent with those observed in simulation. These results indicate that physically guided exploration, which allows agents to experience success early in training, is an effective and general strategy for improving learning efficiency in dynamic whole-body motion tasks.