Learning Multi-Agent Loco-Manipulation for Long-Horizon Quadrupedal Pushing

• URL: http://arxiv.org/abs/2411.07104v2
• Date: Thu, 14 Nov 2024 17:28:37 GMT
• Title: Learning Multi-Agent Loco-Manipulation for Long-Horizon Quadrupedal Pushing
• Authors: Yuming Feng, Chuye Hong, Yaru Niu, Shiqi Liu, Yuxiang Yang, Wenhao Yu, Tingnan Zhang, Jie Tan, Ding Zhao,
• Abstract summary: This paper tackles the task of obstacle-aware, long-horizon pushing by multiple quadrupedal robots. We propose a hierarchical multi-agent reinforcement learning framework with three levels of control.
• Score: 33.689150109924526
• License:
• Abstract: Recently, quadrupedal locomotion has achieved significant success, but their manipulation capabilities, particularly in handling large objects, remain limited, restricting their usefulness in demanding real-world applications such as search and rescue, construction, industrial automation, and room organization. This paper tackles the task of obstacle-aware, long-horizon pushing by multiple quadrupedal robots. We propose a hierarchical multi-agent reinforcement learning framework with three levels of control. The high-level controller integrates an RRT planner and a centralized adaptive policy to generate subgoals, while the mid-level controller uses a decentralized goal-conditioned policy to guide the robots toward these sub-goals. A pre-trained low-level locomotion policy executes the movement commands. We evaluate our method against several baselines in simulation, demonstrating significant improvements over baseline approaches, with 36.0% higher success rates and 24.5% reduction in completion time than the best baseline. Our framework successfully enables long-horizon, obstacle-aware manipulation tasks like Push-Cuboid and Push-T on Go1 robots in the real world.

Related papers

• FLaRe: Achieving Masterful and Adaptive Robot Policies with Large-Scale Reinforcement Learning Fine-Tuning [74.25049012472502]
  FLaRe is a large-scale Reinforcement Learning framework that integrates robust pre-trained representations, large-scale training, and gradient stabilization techniques. Our method aligns pre-trained policies towards task completion, achieving state-of-the-art (SoTA) performance on previously demonstrated and on entirely novel tasks and embodiments.
  arXiv  Detail & Related papers  (2024-09-25T03:15:17Z)
• 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)
• Learning Bipedal Walking for Humanoids with Current Feedback [5.429166905724048]
  We present an approach for overcoming the sim2real gap issue for humanoid robots arising from inaccurate torque-tracking at the actuator level. Our approach successfully trains a unified, end-to-end policy in simulation that can be deployed on a real HRP-5P humanoid robot to achieve bipedal locomotion.
  arXiv  Detail & Related papers  (2023-03-07T08:16:46Z)
• 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)
• Leveraging Sequentiality in Reinforcement Learning from a Single Demonstration [68.94506047556412]
  We propose to leverage a sequential bias to learn control policies for complex robotic tasks using a single demonstration. We show that DCIL-II can solve with unprecedented sample efficiency some challenging simulated tasks such as humanoid locomotion and stand-up.
  arXiv  Detail & Related papers  (2022-11-09T10:28:40Z)
• Advanced Skills by Learning Locomotion and Local Navigation End-to-End [10.872193480485596]
  In this work, we propose to solve the complete problem by training an end-to-end policy with deep reinforcement learning. We demonstrate the successful deployment of policies on a real quadrupedal robot.
  arXiv  Detail & Related papers  (2022-09-26T16:35:00Z)
• DC-MRTA: Decentralized Multi-Robot Task Allocation and Navigation in Complex Environments [55.204450019073036]
  We present a novel reinforcement learning based task allocation and decentralized navigation algorithm for mobile robots in warehouse environments. We consider the problem of joint decentralized task allocation and navigation and present a two level approach to solve it. We observe improvement up to 14% in terms of task completion time and up-to 40% improvement in terms of computing collision-free trajectories for the robots.
  arXiv  Detail & Related papers  (2022-09-07T00:35:27Z)
• Learning of Long-Horizon Sparse-Reward Robotic Manipulator Tasks with Base Controllers [26.807673929816026]
  We propose a method of learning long-horizon sparse-reward tasks utilizing one or more traditional base controllers. Our algorithm incorporates the existing base controllers into stages of exploration, value learning, and policy update. Our method bears the potential of leveraging existing industrial robot manipulation systems to build more flexible and intelligent controllers.
  arXiv  Detail & Related papers  (2020-11-24T14:23:57Z)
• 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)

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.