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A Learning Framework for Diverse Legged Robot Locomotion Using Barrier-Based Style Rewards
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This work introduces a model-free reinforcement learning framework that enables various modes of motion (quadruped, tripod, or biped) and diverse tasks for legged robot locomotion. We employ a motion-style reward based on a relaxed logarithmic barrier function as a soft constraint, to bias the learning process toward the desired motion style, such as gait, foot clearance, joint position, or body height. The predefined gait cycle is encoded in a flexible manner, facilitating gait adjustments throughout the learning process. Extensive experiments demonstrate that KAIST HOUND, a 45 kg robotic system, can achieve biped, tripod, and quadruped locomotion using the proposed framework; quadrupedal capabilities include traversing uneven terrain, galloping at 4.67 m/s, and overcoming obstacles up to 58 cm (67 cm for HOUND2); bipedal capabilities include running at 3.6 m/s, carrying a 7.5 kg object, and ascending stairs-all performed without exteroceptive input.
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Cited by 5 Pith papers
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Agile perceptive multi-skill locomotion for quadrupedal robots in the wild
A single onboard policy trained with 2D trajectory-optimization priors, transformer latent actions, and reinforcement learning enables a quadruped to autonomously select gaits and traverse unstructured terrain at up to 6 m/s.
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Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer
A model-homotopy curriculum that gradually redistributes mass and inertia from a single-rigid-body model to full-body dynamics lets a quadruped learn flips and wall-assisted maneuvers faster and more stably than direc...
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Learning Impact-Rich Rotational Maneuvers via Centroidal Velocity Rewards and Sim-to-Real Techniques: A One-Leg Hopper Flip Case Study
A centroidal angular velocity reward, combined with actuator operating-region modeling and transmission load penalties, produces the first demonstrated full front flip on a one-leg hopper.
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GRoQ-LoCO: Generalist and Robot-agnostic Quadruped Locomotion Control using Offline Datasets
A single offline-trained attention model, using no robot-specific encoding, transfers walking and stair-climbing across multiple quadruped robots, including hardware runs.
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Design of a 3-DOF Hopping Robot with an Optimized Gearbox: An Intermediate Platform Toward Bipedal Robots
A 3-DOF flat-foot hopping robot with a custom MINLP-optimized 3K planetary gearbox performs repetitive hops, a front flip, step-up, and push recovery, serving as an intermediate validation platform for a bipedal robot.
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