Speech-to-Trajectory: Learning Human-Like Verbal Guidance for Robot Motion

Full integration of robots into real-life applications necessitates their ability to interpret and execute natural language directives from untrained users. Given the inherent variability in human language, equivalent directives may be phrased differently, yet require consistent robot behavior. While Large Language Models (LLMs) have advanced language understanding, they often falter in handling user phrasing variability, rely on predefined commands, and exhibit unpredictable outputs. This letter introduces the Directive Language Model (DLM), a novel speech-to-trajectory framework that directly maps verbal commands to executable motion trajectories, bypassing predefined phrases. DLM utilizes Behavior Cloning (BC) on simulated demonstrations of human-guided robot motion. To enhance generalization, GPT-based semantic augmentation generates diverse paraphrases of training commands, labeled with the same motion trajectory. DLM further incorporates a diffusion policy-based trajectory generation for adaptive motion refinement and stochastic sampling. In contrast to LLM-based methods, DLM ensures consistent, predictable motion without extensive prompt engineering, facilitating real-time robotic guidance. As DLM learns from trajectory data, it is embodiment-agnostic, enabling deployment across diverse robotic platforms. Experimental results demonstrate DLM's improved command generalization, reduced dependence on structured phrasing, and achievement of human-like motion.

Human Arm Pose Estimation with a Shoulder-worn Force-Myography Device for Human-Robot Interaction

Accurate human pose estimation is essential for effective Human-Robot Interaction (HRI). By observing a user's arm movements, robots can respond appropriately, whether it's providing assistance or avoiding collisions. While visual perception offers potential for human pose estimation, it can be hindered by factors like poor lighting or occlusions. Additionally, wearable inertial sensors, though useful, require frequent calibration as they do not provide absolute position information. Force-myography (FMG) is an alternative approach where muscle perturbations are externally measured. It has been used to observe finger movements, but its application to full arm state estimation is unexplored. In this letter, we investigate the use of a wearable FMG device that can observe the state of the human arm for real-time applications of HRI. We propose a Transformer-based model to map FMG measurements from the shoulder of the user to the physical pose of the arm. The model is also shown to be transferable to other users with limited decline in accuracy. Through real-world experiments with a robotic arm, we demonstrate collision avoidance without relying on visual perception.