Humanoid Robot Running Through Random Stepping Stones and Jumping Over Obstacles: Step Adaptation Using Spring-Mass Trajectories

2025-12-15

AI Summary

Dynamic gait adaptation for humanoid robots traversing stochastic stepping stones and negotiating obstacles remains challenging due to underactuation, modeling uncertainty, and real-time constraints. Method: This paper proposes a whole-body control framework synergistically driven by a spring-loaded inverted pendulum (SLIP) trajectory library and a dead-zone control gain library. Both libraries are offline-constructed and unified under a single-parameter generalization scheme, eliminating online parameter tuning. The framework integrates closed-chain kinematics, reactive swing-leg planning, and MuJoCo-based simulation validation. Contribution/Results: To our knowledge, this is the first approach enabling multi-modal agile locomotion—including stepping-stone traversal, obstacle negotiation, serpentine maneuvering, rapid turning, and strong disturbance rejection—using a single unified parametrization. Automatic library generation requires only 4.5 seconds (315 entries), and the framework demonstrates significantly enhanced robustness against modeling errors, sensor noise, and communication latency.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

This study proposes a step adaptation framework for running through spring-mass trajectories and deadbeat control gain libraries. It includes four main parts: (1) Automatic spring-mass trajectory library generation; (2) Deadbeat control gain library generation through an actively controlled template model that resembles the whole-body dynamics well; (3) Trajectory selection policy development for step adaptation; (4) Mapping spring-mass trajectories to a humanoid model through a whole-body control (WBC) framework also accounting for closed-kinematic chain systems, self collisions, and reactive limb swinging. We show the inclusiveness and the robustness of the proposed framework through various challenging and agile behaviors such as running through randomly generated stepping stones, jumping over random obstacles, performing slalom motions, changing the running direction suddenly with a random leg, and rejecting significant disturbances and uncertainties through the MuJoCo physics simulator. We also perform additional simulations under a comprehensive set of uncertainties and noise to better justify the proposed method's robustness to real-world challenges, including signal noise, imprecision, modeling errors, and delays. All the aforementioned behaviors are performed with a single library and the same set of WBC control parameters without additional tuning. The spring-mass and the deadbeat control gain library are automatically computed in 4.5 seconds in total for 315 different trajectories.

Problem

• Develops a step adaptation framework for humanoid robots running and jumping over obstacles.
• Generates automatic trajectory and control gain libraries for agile locomotion behaviors.
• Ensures robustness against uncertainties, noise, and real-world challenges in simulations.

Innovation

• Spring-mass trajectory library generation for step adaptation
• Deadbeat control gain library from actively controlled template model
• Whole-body control mapping with closed-chain and collision handling