Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robots

• URL: http://arxiv.org/abs/2405.01792v1
• Date: Fri, 3 May 2024 00:29:20 GMT
• Title: Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robots
• Abstract summary: Navigating urban environments poses unique challenges for robots, necessitating innovative solutions for locomotion and navigation. This work introduces a fully integrated system comprising adaptive locomotion control, mobility-aware local navigation planning, and large-scale path planning within the city. Using model-free reinforcement learning (RL) techniques and privileged learning, we develop a versatile locomotion controller. Our controllers are integrated into a large-scale urban navigation system and validated by autonomous, kilometer-scale navigation missions conducted in Zurich, Switzerland, and Seville, Spain.
• Score: 50.02055068660255
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Autonomous wheeled-legged robots have the potential to transform logistics systems, improving operational efficiency and adaptability in urban environments. Navigating urban environments, however, poses unique challenges for robots, necessitating innovative solutions for locomotion and navigation. These challenges include the need for adaptive locomotion across varied terrains and the ability to navigate efficiently around complex dynamic obstacles. This work introduces a fully integrated system comprising adaptive locomotion control, mobility-aware local navigation planning, and large-scale path planning within the city. Using model-free reinforcement learning (RL) techniques and privileged learning, we develop a versatile locomotion controller. This controller achieves efficient and robust locomotion over various rough terrains, facilitated by smooth transitions between walking and driving modes. It is tightly integrated with a learned navigation controller through a hierarchical RL framework, enabling effective navigation through challenging terrain and various obstacles at high speed. Our controllers are integrated into a large-scale urban navigation system and validated by autonomous, kilometer-scale navigation missions conducted in Zurich, Switzerland, and Seville, Spain. These missions demonstrate the system's robustness and adaptability, underscoring the importance of integrated control systems in achieving seamless navigation in complex environments. Our findings support the feasibility of wheeled-legged robots and hierarchical RL for autonomous navigation, with implications for last-mile delivery and beyond.

Related papers

• MulDP: Multimodal Diffusion Policy for Autonomous Quadruped Parkour Navigation across Complex Terrains [25.563239253434663]
  Quadruped robots have demonstrated impressive agility in parkour locomotion across complex terrains.<n>Key challenges include fine-grained velocity regulation, long-horizon anticipatory behaviors, and tight coupling between perception and embodied execution.<n>We propose a Multimodal Diffusion Policy (MulDP) that integrates visual perception with robot proprioception and goal information to generate temporally coherent and anticipatory navigation velocity commands.
  arXiv  Detail & Related papers  (2026-09-03T15:19:17Z)
• Agile perceptive multi-skill locomotion for quadrupedal robots in the wild [4.14240816913171]
  APT-RL (Action Pretrained Transformer-based Reinforcement Learning) is a unified framework that enables multi-skill locomotion.<n>Our approach generates large-scale, feature-rich 2D motion datasets through trajectory optimization with simplified dynamics.<n>The resulting high-quality skills serve as strong priors for efficient learning of complex downstream tasks and extend naturally to 3D environments.
  arXiv  Detail & Related papers  (2026-07-15T08:22:52Z)
• MUJICA: Multi-skill Unified Joint Integration of Control Architecture for Wheeled-Legged Robots [32.237659116389075]
  We propose a unified, fully proprioceptive control framework for wheeled-legged robots.<n>MUJICA integrates diverse low-level skills including omnidirectional moving, high platform climbing, and fall recovery-within a single policy.<n>We validate our framework in both simulation and real-world experiments on the Unitree Go2-W robot.
  arXiv  Detail & Related papers  (2026-05-13T06:31:18Z)
• AURA: Multimodal Shared Autonomy for Real-World Urban Navigation [49.419374037785815]
  Long-horizon navigation in complex urban environments relies heavily on continuous human operation.<n>We present Assistive Urban Robot Autonomy (AURA), a new multi-modal framework that decomposes urban navigation into high-level human instruction and low-level AI control.<n>AURA effectively follows human instructions, reduces manual operation effort, and improves navigation stability, while enabling online adaptation.
  arXiv  Detail & Related papers  (2026-04-02T05:59:31Z)
• An LLM-Powered Cooperative Framework for Large-Scale Multi-Vehicle Navigation [10.549493962440804]
  Multi-vehicle dynamic navigation requires simultaneously routing large fleets under evolving traffic conditions.<n>Existing path search algorithms and reinforcement learning methods struggle to scale to city-wide networks.<n>We propose CityNav, a hierarchical, LLM-powered framework for large-scale multi-vehicle navigation.
  arXiv  Detail & Related papers  (2025-10-09T06:14:29Z)
• A Practical-Driven Framework for Transitioning Drive-by-Wire to Autonomous Driving Systems: A Case Study with a Chrysler Pacifica Hybrid Vehicle [0.6649753747542207]
  Transitioning from a Drive-by-Wire (DBW) system to a fully autonomous driving system (ADS) requires robust positioning and sensing capabilities.<n>This paper presents a practice-driven framework for facilitating the transition using a 2022 Chrysler Pacifica Hybrid Minivan.
  arXiv  Detail & Related papers  (2024-10-09T02:35:50Z)
• TOP-Nav: Legged Navigation Integrating Terrain, Obstacle and Proprioception Estimation [5.484041860401147]
  TOP-Nav is a novel legged navigation framework that integrates a comprehensive path planner with Terrain awareness, Obstacle avoidance and close-loop Proprioception. We show that TOP-Nav achieves open-world navigation that the robot can handle terrains or disturbances beyond the distribution of prior knowledge.
  arXiv  Detail & Related papers  (2024-04-23T17:42:45Z)
• Dexterous Legged Locomotion in Confined 3D Spaces with Reinforcement Learning [37.95557495560936]
  We introduce a hierarchical locomotion controller that combines a classical planner tasked with planning waypoints to reach a faraway global goal location, and an RL-based policy trained to follow these waypoints by generating low-level motion commands. In simulation, our hierarchical approach succeeds at navigating through demanding confined 3D environments, outperforming both pure end-to-end learning approaches and parameterized locomotion skills.
  arXiv  Detail & Related papers  (2024-03-06T16:49:08Z)
• 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)
• Convergence of Communications, Control, and Machine Learning for Secure and Autonomous Vehicle Navigation [78.60496411542549]
  Connected and autonomous vehicles (CAVs) can reduce human errors in traffic accidents, increase road efficiency, and execute various tasks. Reaping these benefits requires CAVs to autonomously navigate to target destinations. This article proposes solutions using the convergence of communication theory, control theory, and machine learning to enable effective and secure CAV navigation.
  arXiv  Detail & Related papers  (2023-07-05T21:38:36Z)
• Learning and Adapting Agile Locomotion Skills by Transferring Experience [71.8926510772552]
  We propose a framework for training complex robotic skills by transferring experience from existing controllers to jumpstart learning new tasks. We show that our method enables learning complex agile jumping behaviors, navigating to goal locations while walking on hind legs, and adapting to new environments.
  arXiv  Detail & Related papers  (2023-04-19T17:37:54Z)
• ETPNav: Evolving Topological Planning for Vision-Language Navigation in Continuous Environments [56.194988818341976]
  Vision-language navigation is a task that requires an agent to follow instructions to navigate in environments. We propose ETPNav, which focuses on two critical skills: 1) the capability to abstract environments and generate long-range navigation plans, and 2) the ability of obstacle-avoiding control in continuous environments. ETPNav yields more than 10% and 20% improvements over prior state-of-the-art on R2R-CE and RxR-CE datasets.
  arXiv  Detail & Related papers  (2023-04-06T13:07:17Z)
• Learning to Walk by Steering: Perceptive Quadrupedal Locomotion in Dynamic Environments [25.366480092589022]
  A quadrupedal robot must exhibit robust and agile walking behaviors in response to environmental clutter and moving obstacles. We present a hierarchical learning framework, named PRELUDE, which decomposes the problem of perceptive locomotion into high-level decision-making. We demonstrate the effectiveness of our approach in simulation and with hardware experiments.
  arXiv  Detail & Related papers  (2022-09-19T17:55:07Z)
• ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation [99.2543521972137]
  ReLMoGen is a framework that combines a learned policy to predict subgoals and a motion generator to plan and execute the motion needed to reach these subgoals. Our method is benchmarked on a diverse set of seven robotics tasks in photo-realistic simulation environments. ReLMoGen shows outstanding transferability between different motion generators at test time, indicating a great potential to transfer to real robots.
  arXiv  Detail & Related papers  (2020-08-18T08:05:15Z)
• Robot Perception enables Complex Navigation Behavior via Self-Supervised Learning [23.54696982881734]
  We propose an approach to unify successful robot perception systems for active target-driven navigation tasks via reinforcement learning (RL) Our method temporally incorporates compact motion and visual perception data, directly obtained using self-supervision from a single image sequence. We demonstrate our approach on two real-world driving dataset, KITTI and Oxford RobotCar, using the new interactive CityLearn framework.
  arXiv  Detail & Related papers  (2020-06-16T07:45:47Z)

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.