Sat. Sep 23rd, 2023
    An Innovative Approach to Teaching Robots Parkour Skills

    Robots performing complex physical tasks, such as parkour, have always been a challenge in robotics. Parkour involves navigating obstacles with speed and agility, requiring precise coordination, perception, and decision-making. A recent research paper explores an innovative approach to teach robots these agile parkour skills efficiently.

    The researchers have developed a two-stage reinforcement learning (RL) method that integrates “soft dynamics constraints” during the initial training phase. This approach involves constructing specialized skill policies using recurrent neural networks (GRU) and multilayer perceptrons (MLP) to output joint positions based on sensory inputs like depth images, proprioception, and previous actions.

    Soft dynamics constraints play a crucial role in efficient skill acquisition. By providing critical information about the environment, these constraints guide the learning process and enable robots to explore and learn parkour skills effectively.

    The researchers train the specialized skill policies in simulated environments created with IsaacGym. These environments consist of various tracks with increasing obstacle complexity. Meticulously defined reward structures incentivize desired behaviors and discourage undesired ones.

    Transferring skills learned in simulation to the real world is a challenge in robotics. The researchers use domain adaptation techniques to bridge this gap, enabling robots to apply their parkour abilities in practical settings.

    Vision is a key component in enabling robots to perform parkour with agility. Vision sensors, such as depth cameras, provide critical information about the surroundings, allowing robots to sense obstacle properties, prepare for maneuvers, and make informed decisions.

    The proposed method outperforms several baseline methods and ablations, achieving higher success rates in climbing, leaping, crawling, and tilting tasks. Recurrent neural networks prove indispensable for memory-dependent skills like climbing and jumping.

    This research marks a significant advancement in robotic locomotion, solving the challenge of teaching parkour skills and expanding robots’ capabilities in complex tasks. By leveraging vision, simulation, reward structures, and domain adaptation, robots can navigate complex environments with precision and agility.

    – Researchers on this project