# Robust Brachiation on a Life-Sized Dual-Arm Robot Using Waypoint-Guided Reinforcement Learning

arXiv: https://arxiv.org/abs/2608.17320 · HTML: https://mlsanity.com/paper/2608.17320/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods.
- Builds on 10 of its 17 references that are in the map.
- First posted on arXiv 18 Aug 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, 10 of 10)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Sim-to-Real Learning of All Common Bipedal Gaits via Periodic Reward Composition](https://mlsanity.com/paper/2011.01387/) (2020)
- [Robust Ladder Climbing with a Quadrupedal Robot](https://mlsanity.com/paper/2409.17731/) (2024)
- [Learning to Brachiate via Simplified Model Imitation](https://mlsanity.com/paper/2205.03943/) (2022)
- [Waypoint-Based Reinforcement Learning for Robot Manipulation Tasks](https://mlsanity.com/paper/2403.13281/) (2024)
- [AcroMonk: A Minimalist Underactuated Brachiating Robot](https://mlsanity.com/paper/2305.08373/) (2023)
- [Design and Implementation of a Three-Link Brachiation Robot with Optimal Control Based Trajectory Tracking Controller](https://mlsanity.com/paper/1911.05168/) (2019)
- [RicMonk: A Three-Link Brachiation Robot with Passive Grippers for Energy-Efficient Brachiation](https://mlsanity.com/paper/2403.15762/) (2024)
- [MEVIUS: A Quadruped Robot Easily Constructed through E-Commerce with Sheet Metal Welding and Machining](https://mlsanity.com/paper/2409.14721/) (2024)
- [MEVITA: Open-Source Bipedal Robot Assembled from E-Commerce Components via Sheet Metal Welding](https://mlsanity.com/paper/2508.17684/) (2025)

## Abstract (arXiv)

Brachiation is a form of locomotion in which primates move primarily using their arms, enabling traversal in environments without footholds. However, this motion requires highly coordinated whole-body movement and precise timing control for bar grasping and release. As a result, achieving robust behavior on life-sized robotic platforms remains challenging. In this study, we present a reinforcement learning-based method to realize brachiation on a life-sized dual-arm robot. The core of the proposed approach is Waypoint-Guided Reinforcement Learning (WGRL), a learning framework for inducing non-linear and complex motions. For high-difficulty tasks where imitation learning data are unavailable, WGRL guides behavior acquisition by sparsely specifying waypoints for the end-effector trajectory, while whole-body motion is generated through reinforcement learning. In addition, by integrating the waypoint-following guidance with rewards based on task success and mechanical energy, and training in an environment designed for Sim-to-Real transfer, the proposed method achieves both forward progression and motion stability. The acquired behavior is evaluated through Sim-to-Sim experiments under monkey-bar environments with geometric variations and hardware experiments, confirming robust brachiation including failure recovery behavior. This study provides effective learning design guidelines for realizing arm-based locomotion on life-sized robotic hardware and expanding the traversable workspace of robots.