Sep 2026· IEEE Robotics and Automation Letters· Vol 11, pp. 10489-10496· 0 citations· 28 references
Computer Science
Abstract
Legged spherical robots feature both walking and rolling modes and they are useful in explorations, but often end up in various body-inverted postures after rolling and accidental falls. Posture recovery in such cases where all legs are in the air and lose leg-ground contacts is an open and challenging problem. We propose a posture recovery learning method using vector reward called PRVR. A traditional scalar reward cannot evaluate each element of a vector action. Here, a vector reward method is proposed to evaluate each element of a vector action, such that the action of each joint closely conforms to the expected behavior. To reduce learning complexity, a rotation symmetry-based training and deployment method is proposed. Body-inverted postures can be divided into several symmetric classes, and then through training on one class of body-inverted postures, the robot can recover from various body-inverted postures. Simulations and experiments on a six-legged spherical robot are used to verify the effectiveness.
Effective learning design guidelines for realizing arm-based locomotion on life-sized robotic hardware and expanding the traversable workspace of robots are provided.
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Brachiation enables primates to move across overhead supports when ground paths are blocked, suggesting a complementary locomotion mode for robots operating in cluttered or hazardous environments. Bringing this capability to high-DoF humanoid robots is difficult because the controller must discover a long-horizon relea...
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Humanoid robots promise versatile mobility in cluttered, human-centric environments, but real deployment demands principled safety. Classical model-based gait generators yield interpretable motions but often lack the robustness and adaptability of modern reinforcement learning (RL) based approaches. We propose a model-...
High-dynamic quadruped jumping over steps and gap-like support interruptions remains difficult when external terrain perception is unavailable or unreliable. This paper studies a proprioception-only reinforcement-learning policy for bound jumping. The actor is initialized by a flat-ground bound strategy to obtain a sta...
Hong-Jing Huang, Cong Li, Tian-Wei Qian et al.· 2026 International Conferenc...· 0 citations
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