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This paper introduces a simulation framework for quantitative analysis of human-robot interaction, using a full-body musculoskeletal model as a predictive human surrogate. The model is driven by a reinforcement learning controller trained to generate adaptive motor behaviors, enabling large-scale design space exploration. The framework is demonstrated by optimizing human-exoskeleton interactions, achieving improved joint alignment and reduced contact forces through co-optimization of robot structure and control policy.
Forget costly physical experiments: this framework lets you simulate embodied human-robot interaction to optimize robot designs and controls, unlocking access to internal biomechanical metrics.
Physical interactive robotics, ranging from wearable devices to collaborative humanoid robots, require close coordination between mechanical design and control. However, evaluating interactive dynamics is challenging due to complex human biomechanics and motor responses. Traditional experiments rely on indirect metrics without measuring human internal states, such as muscle forces or joint loads. To address this issue, we develop a scalable simulation-based framework for the quantitative analysis of physical human-robot interaction. At its core is a full-body musculoskeletal model serving as a predictive surrogate for the human dynamical system. Driven by a reinforcement learning controller, it generates adaptive, physiologically grounded motor behaviors. We employ a sequential training pipeline where the pre-trained human motion control policy acts as a consistent evaluator, making large-scale design space exploration computationally tractable. By simulating the coupled human-robot system, the framework provides access to internal biomechanical metrics, offering a systematic way to concurrently co-optimize a robot's structural parameters and control policy. We demonstrate its capability in optimizing human-exoskeleton interactions, showing improved joint alignment and reduced contact forces. This work establishes embodied human simulation as a scalable paradigm for interactive robotics design.
