We address the problem of adapting robot trajectories to improve safety, comfort, and efficiency in humanrobot collaborative tasks. To this end, we propose CoMOTO, a trajectory optimization framework that utilizes stochastic motion prediction to anticipate the human's motion and adapt the robot's joint trajectory accordingly. We design a multiobjective cost function that simultaneously optimizes for i) separation distance, ii) visibility of the end-effector, iii) legibility, iv) efficiency, and v) smoothness. We evaluate CoMOTO against three existing methods for robot trajectory generation when in close proximity to humans. Our experimental results indicate that our approach consistently outperforms existing methods over a combined set of safety, comfort, and efficiency metrics.

We present Surface++, which leverages our previous work SATURN, a self-powered flexible acoustic sensor, and ZEUSSS, a passive wireless sound communication technique using analog backscatter, to create a scalable and self-sustainable wireless sound sensing surface. Our new prototype allows for large area acoustic sensing using modular fabrication techniques with the promise of being fully printable. A single small Surface++ patch can be used to extend voice and gesture input for everyday surfaces, while our more sensitive Surface++ modular array allows for large-area context sensing and localization.