mlsanityData to 14 Oct 2026

Development of a PPO-Reinforcement Learned Walking Tripedal Soft-Legged Robot using SOFA

arXiv 2504.09242 ↗First posted 12 Apr 2025cs.RO cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation

Abstract (arXiv)

Rigid robots were extensively researched, whereas soft robotics remains an underexplored field. Utilizing soft-legged robots in performing tasks as a replacement for human beings is an important stride to take, especially under harsh and hazardous conditions over rough terrain environments. For the demand to teach any robot how to behave in different scenarios, a real-time physical and visual simulation is essential. When it comes to soft robots specifically, a simulation framework is still an arduous problem that needs to be disclosed. Using the simulation open framework architecture (SOFA) is an advantageous step. However, neither SOFA's manual nor prior public SOFA projects show its maximum capabilities the users can reach. So, we resolved this by establishing customized settings and handling the framework components appropriately. Settling on perfect, fine-tuned SOFA parameters has stimulated our motivation towards implementing the state-of-the-art (SOTA) reinforcement learning (RL) method of proximal policy optimization (PPO). The final representation is a well-defined, ready-to-deploy walking, tripedal, soft-legged robot based on PPO-RL in a SOFA environment. Robot navigation performance is a key metric to be considered for measuring the success resolution. Although in the simulated soft robots case, an 82% success rate in reaching a single goal is a groundbreaking output, we pushed the boundaries to further steps by evaluating the progress under assigning a sequence of goals. While trailing the platform steps, outperforming discovery has been observed with an accumulative squared error deviation of 19 mm. The full code is publicly available at hrefhttps://github.com/tarekshohdy/PPOSOFASoftLeggedRobot.gitgithub.com/tarekshohdy/PPOtextunderscoreSOFAtextunderscoreSofttextunderscoreLeggedtextunderscore Robot.git

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 3 of its 19 references that are in the map. First posted on arXiv 12 Apr 2025.

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3 of 19 references.

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Same direction, newest first

  1. 2024A Learning Framework for Diverse Legged Robot Locomotion Using Barrier-Based Style Rewards24 Sep

Other references · 2

  1. 2024Body Design and Gait Generation of Chair-Type Asymmetrical Tripedal Low-rigidity Robotcited by 2
  2. 2023Direct and inverse modeling of soft robots by learning a condensed FEM modelcited by 1

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