# Learning Bipedal Walking for Humanoid Robots in Challenging Environments with Obstacle Avoidance

arXiv: https://arxiv.org/abs/2410.08212 · HTML: https://mlsanity.com/paper/2410.08212/

## 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 6 of its 10 references that are in the map.
- First posted on arXiv 25 Sep 2024.

## 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, 6 of 6)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Torque-based Deep Reinforcement Learning for Task-and-Robot Agnostic Learning on Bipedal Robots Using Sim-to-Real Transfer](https://mlsanity.com/paper/2304.09434/) (2023)
- [DeepWalk: Omnidirectional Bipedal Gait by Deep Reinforcement Learning](https://mlsanity.com/paper/2106.00534/) (2021)
- [Zero-Shot Terrain Generalization for Visual Locomotion Policies](https://mlsanity.com/paper/2011.05513/) (2020)
- [Learning Bipedal Walking On Planned Footsteps For Humanoid Robots](https://mlsanity.com/paper/2207.12644/) (2022)
- [Stair Climbing Stabilization of the HRP-4 Humanoid Robot using Whole-body Admittance Control](https://mlsanity.com/paper/1809.07073/) (2018)

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

- [Hierarchical Reinforcement Learning for Collision-Free Locomotion of an Underactuated Biped](https://mlsanity.com/paper/2610.05855/) (2026)

## Abstract (arXiv)

Deep reinforcement learning has seen successful implementations on humanoid robots to achieve dynamic walking. However, these implementations have been so far successful in simple environments void of obstacles. In this paper, we aim to achieve bipedal locomotion in an environment where obstacles are present using a policy-based reinforcement learning. By adding simple distance reward terms to a state of art reward function that can achieve basic bipedal locomotion, the trained policy succeeds in navigating the robot towards the desired destination without colliding with the obstacles along the way.