DexDribbler: Learning Dexterous Soccer Manipulation via Dynamic Supervision

• URL: http://arxiv.org/abs/2403.14300v1
• Date: Thu, 21 Mar 2024 11:16:28 GMT
• Title: DexDribbler: Learning Dexterous Soccer Manipulation via Dynamic Supervision
• Authors: Yutong Hu, Kehan Wen, Fisher Yu,
• Abstract summary: Joint manipulation of moving objects and locomotion with legs, such as playing soccer, receive scant attention in the learning community. We propose a feedback control block to compute the necessary body-level movement accurately and using the outputs as dynamic joint-level locomotion supervision. We observe that our learning scheme can not only make the policy network converge faster but also enable soccer robots to perform sophisticated maneuvers.
• Score: 26.9579556496875
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning dexterous locomotion policy for legged robots is becoming increasingly popular due to its ability to handle diverse terrains and resemble intelligent behaviors. However, joint manipulation of moving objects and locomotion with legs, such as playing soccer, receive scant attention in the learning community, although it is natural for humans and smart animals. A key challenge to solve this multitask problem is to infer the objectives of locomotion from the states and targets of the manipulated objects. The implicit relation between the object states and robot locomotion can be hard to capture directly from the training experience. We propose adding a feedback control block to compute the necessary body-level movement accurately and using the outputs as dynamic joint-level locomotion supervision explicitly. We further utilize an improved ball dynamic model, an extended context-aided estimator, and a comprehensive ball observer to facilitate transferring policy learned in simulation to the real world. We observe that our learning scheme can not only make the policy network converge faster but also enable soccer robots to perform sophisticated maneuvers like sharp cuts and turns on flat surfaces, a capability that was lacking in previous methods. Video and code are available at https://github.com/SysCV/soccer-player

Related papers

• 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)
• Barkour: Benchmarking Animal-level Agility with Quadruped Robots [70.97471756305463]
  We introduce the Barkour benchmark, an obstacle course to quantify agility for legged robots. Inspired by dog agility competitions, it consists of diverse obstacles and a time based scoring mechanism. We present two methods for tackling the benchmark.
  arXiv  Detail & Related papers  (2023-05-24T02:49:43Z)
• Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning [26.13655448415553]
  Deep Reinforcement Learning (Deep RL) is able to synthesize sophisticated and safe movement skills for a low-cost, miniature humanoid robot. We used Deep RL to train a humanoid robot with 20 actuated joints to play a simplified one-versus-one (1v1) soccer game. The resulting agent exhibits robust and dynamic movement skills such as rapid fall recovery, walking, turning, kicking and more.
  arXiv  Detail & Related papers  (2023-04-26T16:25:54Z)
• DribbleBot: Dynamic Legged Manipulation in the Wild [10.29780236909404]
  DribbleBot is a legged robotic system that can dribble a soccer ball under the same real-world conditions as humans (i.e., in-the-wild) We adopt the paradigm of training policies in simulation using reinforcement learning and transferring them into the real world.
  arXiv  Detail & Related papers  (2023-04-03T17:26:09Z)
• Legs as Manipulator: Pushing Quadrupedal Agility Beyond Locomotion [34.33972863987201]
  We train quadruped robots to use the front legs to climb walls, press buttons, and perform object interaction in the real world. These skills are trained in simulation using curriculum and transferred to the real world using our proposed sim2real variant. We evaluate our method in both simulation and real-world showing successful executions of both short as well as long-range tasks.
  arXiv  Detail & Related papers  (2023-03-20T17:59:58Z)
• Deep Whole-Body Control: Learning a Unified Policy for Manipulation and Locomotion [25.35885216505385]
  An attached arm can significantly increase the applicability of legged robots to mobile manipulation tasks. Standard hierarchical control pipeline for such legged manipulators is to decouple the controller into that of manipulation and locomotion. We learn a unified policy for whole-body control of a legged manipulator using reinforcement learning.
  arXiv  Detail & Related papers  (2022-10-18T17:59:30Z)
• GraspARL: Dynamic Grasping via Adversarial Reinforcement Learning [16.03016392075486]
  We introduce an adversarial reinforcement learning framework for dynamic grasping, namely GraspARL. We formulate the dynamic grasping problem as a'move-and-grasp' game, where the robot is to pick up the object on the mover and the adversarial mover is to find a path to escape it. In this way, the mover can auto-generate diverse moving trajectories while training. And the robot trained with the adversarial trajectories can generalize to various motion patterns.
  arXiv  Detail & Related papers  (2022-03-04T03:25:09Z)
• A Differentiable Recipe for Learning Visual Non-Prehensile Planar Manipulation [63.1610540170754]
  We focus on the problem of visual non-prehensile planar manipulation. We propose a novel architecture that combines video decoding neural models with priors from contact mechanics. We find that our modular and fully differentiable architecture performs better than learning-only methods on unseen objects and motions.
  arXiv  Detail & Related papers  (2021-11-09T18:39:45Z)
• Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robots [121.42930679076574]
  We present a model-free reinforcement learning framework for training robust locomotion policies in simulation. domain randomization is used to encourage the policies to learn behaviors that are robust across variations in system dynamics. We demonstrate this on versatile walking behaviors such as tracking a target walking velocity, walking height, and turning yaw.
  arXiv  Detail & Related papers  (2021-03-26T07:14:01Z)
• Learning Quadrupedal Locomotion over Challenging Terrain [68.51539602703662]
  Legged locomotion can dramatically expand the operational domains of robotics. Conventional controllers for legged locomotion are based on elaborate state machines that explicitly trigger the execution of motion primitives and reflexes. Here we present a radically robust controller for legged locomotion in challenging natural environments.
  arXiv  Detail & Related papers  (2020-10-21T19:11:20Z)
• Learning Agile Robotic Locomotion Skills by Imitating Animals [72.36395376558984]
  Reproducing the diverse and agile locomotion skills of animals has been a longstanding challenge in robotics. We present an imitation learning system that enables legged robots to learn agile locomotion skills by imitating real-world animals.
  arXiv  Detail & Related papers  (2020-04-02T02:56:16Z)

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.