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Replication of Impedance Identification Experiments on a Reinforcement-Learning-Controlled Digital Twin of Human Elbows
Feb 05, 2024
This study presents a pioneering effort to replicate human neuromechanical experiments within a virtual environment utilising a digital human model. By employing MyoSuite, a state-of-the-art human motion simulation platform enhanced by Reinforcement Learning (RL), multiple types of impedance identification experiments of human elbow were replicated on a musculoskeletal model. We compared the elbow movement controlled by an RL agent with the motion of an actual human elbow in terms of the impedance identified in torque-perturbation experiments. The findings reveal that the RL agent exhibits higher elbow impedance to stabilise the target elbow motion under perturbation than a human does, likely due to its shorter reaction time and superior sensory capabilities. This study serves as a preliminary exploration into the potential of virtual environment simulations for neuromechanical research, offering an initial yet promising alternative to conventional experimental approaches. An RL-controlled digital twin with complete musculoskeletal models of the human body is expected to be useful in designing experiments and validating rehabilitation theory before experiments on real human subjects.
Layered Rendering Diffusion Model for Zero-Shot Guided Image Synthesis
Nov 30, 2023This paper introduces innovative solutions to enhance spatial controllability in diffusion models reliant on text queries. We present two key innovations: Vision Guidance and the Layered Rendering Diffusion (LRDiff) framework. Vision Guidance, a spatial layout condition, acts as a clue in the perturbed distribution, greatly narrowing down the search space, to focus on the image sampling process adhering to the spatial layout condition. The LRDiff framework constructs an image-rendering process with multiple layers, each of which applies the vision guidance to instructively estimate the denoising direction for a single object. Such a layered rendering strategy effectively prevents issues like unintended conceptual blending or mismatches, while allowing for more coherent and contextually accurate image synthesis. The proposed method provides a more efficient and accurate means of synthesising images that align with specific spatial and contextual requirements. We demonstrate through our experiments that our method provides better results than existing techniques both quantitatively and qualitatively. We apply our method to three practical applications: bounding box-to-image, semantic mask-to-image and image editing.