Aug 2026· Science Robotics· Vol 11 117, pp.
eaed3973
· 7 citations· 71 references
Medicine
TL;DR
This survey positions humanoid control at a turning point, converging toward a unified paradigm of physics-guided generative intelligence that integrates optimization, learning, and predictive reasoning.
Abstract
Humanoid robots stand at the forefront of robotics, aiming to capture the agility, robustness, and expressivity of human movement in an anthropomorphic form. The locomotion control of humanoids has evolved from classical model-based methods to reinforcement learning powered by large-scale simulation and now to generative models that produce adaptive, whole-body behaviors, propelling humanoids toward operation in real-world environments. This survey positions humanoid control at a turning point, converging toward a unified paradigm of physics-guided generative intelligence that integrates optimization, learning, and predictive reasoning. We identify three core principles linking these paradigms: physics-based modeling, constrained decision-making, and adaptation to uncertainty. Building on these connections, we provide recommendations for researchers and outline open challenges in safety, accessibility, and human-level capability. These directions represent a transformation from engineered stability to intelligent autonomy, laying the groundwork for humanoid generalists capable of operating safely, collaborating naturally, and extending human capability in the open world.
This work revisits the scaling recipe for BFMs and demonstrates that substantial performance gains can be achieved through the coordination of three core components: the learning paradigm of motion tracking that reformulates diverse humanoid control problems as the reproduction of integrated whole-body behaviors in the global frame.
Effective learning design guidelines for realizing arm-based locomotion on life-sized robotic hardware and expanding the traversable workspace of robots are provided.
Ayumu Iwata, Kento Kawaharazuka, Keita Yoneda et al.· 0 citations
Robotic mobility remains a major challenge in planetary exploration, particularly in environments characterized by uncertain terrain interaction, low gravity, and irregular contact conditions. Conventional wheeled and gait-based locomotion strategies typically rely on predefined contact patterns and motion-centric control formulations, which can become fragile in highly unstructured extraterrestrial environments such as lava tubes, crater walls, and granular slopes. This perspective proposes a support-centric interpretation of locomotion in which mobility is viewed as the continuous generation, redistribution, and adaptation of support under uncertain interaction conditions. Rather than treating contact as a secondary constraint within trajectory execution, the proposed framework interprets locomotion through the evolution of support configurations, support quality, and contact reliability. The paper synthesizes developments in terramechanics, adaptive legged locomotion, bio-inspired robotics, and learning-based control to establish conceptual links between contact interaction and support evolution. A conceptual framework for learning support dynamics is further introduced to outline possible directions for adaptive multi-contact locomotion in space robotics. The proposed perspective is intended not as a replacement for existing locomotion methods, but as a higher-level framework for guiding future research in robust terrain-adaptive robotic mobility.
E. Prisăcariu, Oana Dumitrescu· Robotics· 0 citations
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 release-swing-capture sequence, coordinate alternating contacts with whole-body momentum, and act without reliable measurements of segment-relative displacement or hook-contact state. We present SwingBot, a learning framework for continuous humanoid brachiation with passive wrist hooks. SwingBot makes the task trainable by organizing learning around the structure of brachiation: biomimetic keyframes make rare release-swing-capture transitions reachable during early exploration, and recurrent privileged-state estimation provides compact position and contact latents for deployment. Hardware experiments demonstrate continuous bar traversal and robustness to payload, external disturbances and different bar spacings, showing that this formulation offers a practical route to whole-body robotic brachiation.
This work proposes a hierarchical reinforcement learning pipeline that empowers the robots to perform aggressive locomotion through constrained obstacles--a narrow gate, extending the lifelike agility of legged robots to match that of their biological counterparts.
Zeren Luo, Jiahui Zhang, Yimin Han et al.· 1 citation
Humans readily adapt their movements as their bodies change through aging, injury, or load carrying, but learning-based robot policies often break when hardware properties shift. We introduce an online embodiment adaptation framework for quadrupedal locomotion that infers embodiment parameters from short interaction histories and conditions control on the inferred hardware state. Our method pairs a generalist policy trained under embodiment randomization with a lightweight adaptation module that identifies physical changes within half a second. We evaluate two representative forms of embodiment variation: joint-range constraints and trunk-mass changes, corresponding to joint-level kinematic degradation and body-level dynamic variation. In simulation, the module accurately estimates these changes and enables closed-loop control that substantially outperforms policies conditioned directly on interaction history. On a real Unitree Go2 robot, our system maintains stable locomotion under severe instances of the evaluated changes, including a fully locked leg and a 5 kg payload, where non-adaptive methods fail. These results demonstrate the practicality of explicit online embodiment identification for rapid adaptation to joint-limit and payload-mass changes, and provide a step toward handling broader forms of uncertain, degraded, or changing robot hardware.
Di-Chen Li, Bo Ai, Nico Bohlinger et al.· 0 citations
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