Olympus: A Jumping Quadruped for Planetary Exploration Utilizing Reinforcement Learning for In-Flight Attitude Control

• URL: http://arxiv.org/abs/2503.03574v1
• Date: Wed, 05 Mar 2025 15:01:56 GMT
• Title: Olympus: A Jumping Quadruped for Planetary Exploration Utilizing Reinforcement Learning for In-Flight Attitude Control
• Authors: Jørgen Anker Olsen, Grzegorz Malczyk, Kostas Alexis,
• Abstract summary: This paper presents the design, simulation, and learning-based "in-flight" attitude control of Olympus, a jumping legged robot tailored to the gravity of Mars.
• Score: 9.771798181062822
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Exploring planetary bodies with lower gravity, such as the moon and Mars, allows legged robots to utilize jumping as an efficient form of locomotion thus giving them a valuable advantage over traditional rovers for exploration. Motivated by this fact, this paper presents the design, simulation, and learning-based "in-flight" attitude control of Olympus, a jumping legged robot tailored to the gravity of Mars. First, the design requirements are outlined followed by detailing how simulation enabled optimizing the robot's design - from its legs to the overall configuration - towards high vertical jumping, forward jumping distance, and in-flight attitude reorientation. Subsequently, the reinforcement learning policy used to track desired in-flight attitude maneuvers is presented. Successfully crossing the sim2real gap, extensive experimental studies of attitude reorientation tests are demonstrated.

Related papers

• PALo: Learning Posture-Aware Locomotion for Quadruped Robots [29.582249837902427]
  We propose an end-to-end deep reinforcement learning framework for posture-aware locomotion named PALo. PALo handles simultaneous linear and angular velocity tracking and real-time adjustments of body height, pitch, and roll angles. PALo achieves agile posture-aware locomotion control in simulated environments and successfully transfers to real-world settings without fine-tuning.
  arXiv  Detail & Related papers  (2025-03-06T14:13:59Z)
• Humanoid Whole-Body Locomotion on Narrow Terrain via Dynamic Balance and Reinforcement Learning [54.26816599309778]
  We propose a novel whole-body locomotion algorithm based on dynamic balance and Reinforcement Learning (RL)<n> Specifically, we introduce a dynamic balance mechanism by leveraging an extended measure of Zero-Moment Point (ZMP)-driven rewards and task-driven rewards in a whole-body actor-critic framework.<n> Experiments conducted on a full-sized Unitree H1-2 robot verify the ability of our method to maintain balance on extremely narrow terrains.
  arXiv  Detail & Related papers  (2025-02-24T14:53:45Z)
• BeamDojo: Learning Agile Humanoid Locomotion on Sparse Footholds [35.62230804783507]
  We introduce BeamDojo, a reinforcement learning framework for humanoid locomotion on sparse footholds.<n>BeamDojo balances the learning process between dense locomotion rewards and sparse foothold rewards.<n>We implement a onboard LiDAR-based elevation map to enable real-world deployment.
  arXiv  Detail & Related papers  (2025-02-14T18:42:42Z)
• Learning Humanoid Standing-up Control across Diverse Postures [27.79222176982376]
  Standing-up control is crucial for humanoid robots, with the potential for integration into current locomotion and loco-manipulation systems. We present HoST (Humanoid Standing-up Control), a reinforcement learning framework that learns standing-up control from scratch. Our experimental results demonstrate that the controllers achieve smooth, stable, and robust standing-up motions across a wide range of laboratory and outdoor environments.
  arXiv  Detail & Related papers  (2025-02-12T13:10:09Z)
• A Cross-Scene Benchmark for Open-World Drone Active Tracking [54.235808061746525]
  Drone Visual Active Tracking aims to autonomously follow a target object by controlling the motion system based on visual observations.<n>We propose a unified cross-scene cross-domain benchmark for open-world drone active tracking called DAT.<n>We also propose a reinforcement learning-based drone tracking method called R-VAT.
  arXiv  Detail & Related papers  (2024-12-01T09:37:46Z)
• Learning Humanoid Locomotion over Challenging Terrain [84.35038297708485]
  We present a learning-based approach for blind humanoid locomotion capable of traversing challenging natural and man-made terrains. Our model is first pre-trained on a dataset of flat-ground trajectories with sequence modeling, and then fine-tuned on uneven terrain using reinforcement learning. We evaluate our model on a real humanoid robot across a variety of terrains, including rough, deformable, and sloped surfaces.
  arXiv  Detail & Related papers  (2024-10-04T17:57:09Z)
• Learning to enhance multi-legged robot on rugged landscapes [7.956679144631909]
  Multi-legged robots offer a promising solution forNavigating rugged landscapes. Recent studies have shown that a linear controller can ensure reliable mobility on challenging terrains. We develop a MuJoCo-based simulator tailored to this robotic platform and use the simulation to develop a reinforcement learning-based control framework.
  arXiv  Detail & Related papers  (2024-09-14T15:53:08Z)
• Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control [106.32794844077534]
  This paper presents a study on using deep reinforcement learning to create dynamic locomotion controllers for bipedal robots. We develop a general control solution that can be used for a range of dynamic bipedal skills, from periodic walking and running to aperiodic jumping and standing. This work pushes the limits of agility for bipedal robots through extensive real-world experiments.
  arXiv  Detail & Related papers  (2024-01-30T10:48:43Z)
• Robust and Versatile Bipedal Jumping Control through Reinforcement Learning [141.56016556936865]
  This work aims to push the limits of agility for bipedal robots by enabling a torque-controlled bipedal robot to perform robust and versatile dynamic jumps in the real world. We present a reinforcement learning framework for training a robot to accomplish a large variety of jumping tasks, such as jumping to different locations and directions. We develop a new policy structure that encodes the robot's long-term input/output (I/O) history while also providing direct access to a short-term I/O history.
  arXiv  Detail & Related papers  (2023-02-19T01:06:09Z)
• Inverted Landing in a Small Aerial Robot via Deep Reinforcement Learning for Triggering and Control of Rotational Maneuvers [11.29285364660789]
  Inverted landing in a rapid and robust manner is a challenging feat for aerial robots, especially while depending entirely on onboard sensing and computation. Previous work has identified a direct causal connection between a series of onboard visual cues and kinematic actions that allow for reliable execution of this challenging aerobatic maneuver in small aerial robots. In this work, we first utilized Deep Reinforcement Learning and a physics-based simulation to obtain a general, optimal control policy for robust inverted landing.
  arXiv  Detail & Related papers  (2022-09-22T14:38:10Z)
• Learning Perceptual Locomotion on Uneven Terrains using Sparse Visual Observations [75.60524561611008]
  This work aims to exploit the use of sparse visual observations to achieve perceptual locomotion over a range of commonly seen bumps, ramps, and stairs in human-centred environments. We first formulate the selection of minimal visual input that can represent the uneven surfaces of interest, and propose a learning framework that integrates such exteroceptive and proprioceptive data. We validate the learned policy in tasks that require omnidirectional walking over flat ground and forward locomotion over terrains with obstacles, showing a high success rate.
  arXiv  Detail & Related papers  (2021-09-28T20:25:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.