Jakob Wolfram, Pascal Roth, Fan Yang, Cesar Cadena, Marco Hutter
Navigating crowded urban environments requires robots to avoid pedestrians while adhering to social norms such as remaining on sidewalks. Existing approaches typically address either crowd avoidance or semantic path planning in isolation. We propose a reinforcement learning (RL) navigation policy that jointly reasons about semantic terrain and dynamic obstacles from a single semantically annotated bird's-eye-view (BEV) map, without relying on explicit human-state estimation. Our policy outperforms established crowd-navigation and semantic-aware baselines and transfers zero-shot to a legged robot, demonstrating socially compliant navigation in highly dynamic real-world scenarios.
Yifei Yuan, Jakob Wolf, Ghaith Androwis, Xianlian Zhou
Objective: This study aims to determine whether a hip-exoskeleton policy trained in simulation, by co-adapting with a musculoskeletal human model under a metabolic-energy reward, reduces the metabolic cost of walking in new users without per-user tuning, and how mechanical-power shaping affects the learned assistance. Methods: We developed MARL-Exo, a multi-agent reinforcement learning framework in which a full-body human model (304 muscle actuators; randomized anatomy, gait, and speed) and a bilateral hip-exoskeleton policy are trained concurrently in two stages. The exoskeleton observes only delayed hip kinematics and its past commands; its reward includes muscle metabolic power from a physiologically based energetics model, alone (Metabolic) or with mechanical-power shaping (Metabolic + Power). The Metabolic policy was deployed and tested without retraining in eight healthy adults walking at 1.25 m/s, using indirect calorimetry. Results: In simulation, the Metabolic policy reduced modeled muscle metabolic power by 13.8% relative to no-device walking, versus 8.8% for Metabolic + Power. On hardware, MARL-Exo reduced net metabolic rate by 18.3 $\pm$ 6.6% relative to Passive in all eight participants. Relative to No-Exo, the mean reduction was 7.4 $\pm$ 10.0%; six participants more than offset the 13.1% device-wearing penalty, walking up to 24.2% below their No-Exo cost. Conclusion: A metabolically informed policy co-adapted in simulation transferred to users without per-user tuning and consistently reduced the metabolic cost of walking with the device, while explicit power shaping reduced the simulated benefit. Significance: Unlike per-user human-in-the-loop optimization, this approach yields a single population-level controller and allows assistance objectives to be screened in simulation before human testing.
Yifei Yuan, Jakob Wolf, Ghaith Androwis, Xianlian Zhou
Learning-based controllers can enable exoskeleton assistance to be trained entirely in physics-based simulation, but achieving effective assistance on real users remains challenging because the human and device continuously adapt to one another. We investigate a Staged Multi-Agent Training (SMAT) approach that jointly trains a musculoskeletal human actor and a bilateral hip-exoskeleton actor (control policy) to learn coordinated assistance strategies while accounting for human-device co-adaptation. The resulting policy was transferred to a physical hip exoskeleton and evaluated in eight healthy adults across users and locomotion conditions without subject-specific retraining. Indirect calorimetry was used to compare no-exoskeleton, passive, and active conditions. Active assistance reduced net metabolic rate by 19.7% relative to the passive device (p < 0.001). The policy produced predominantly positive hip mechanical power across subjects, with a positive-power ratio of 0.984, and maintained effective assistance across walking speeds and terrains beyond the training conditions. These results demonstrate that simulation-based multi-agent co-adaptation can produce a single transferable exoskeleton policy that elicits meaningful physiological benefits in real users without subject-specific retraining, providing a framework for reducing the experimental burden of human-in-the-loop controller development.