We introduce MusclePose as an end-to-end learnable physics-infused 3D human pose estimator that incorporates muscle-dynamics modeling to infer human dynamics from monocular video. Current physics pose estimators aim to predict physically plausible poses by enforcing the underlying dynamics equations that govern motion. Since this is an underconstrained problem without force-annotated data, methods often estimate kinetics with external physics optimizers that may not be compatible with existing learning frameworks, or are too slow for real-time inference. While more recent methods use a regression-based approach to overcome these issues, the estimated kinetics can be seen as auxiliary predictions, and may not be physically plausible. To this end, we build on existing regression-based approaches, and aim to improve the biofidelity of kinetic inference with a multihypothesis approach --- by inferring joint torques via Lagrange’s equations and via muscle dynamics modeling with muscle torque generators. Furthermore, MusclePose predicts detailed human anthropometrics based on values from biomechanics studies, in contrast to existing physics pose estimators that construct their human models with shape primitives. We show that MusclePose is competitive with existing 3D pose estimators in positional accuracy, while also able to infer plausible human kinetics and muscle signals consistent with values from biomechanics studies, without requiring an external physics engine.

Deep Reinforcement Learning (DRL) is used here for decentralized decision-making and longitudinal control for high-speed on-ramp merging. The DRL environment state includes the states of five vehicles: the merging vehicle, along with two preceding and two following vehicles when the merging vehicle is or is projected on the main road. The control action is the acceleration of the merging vehicle. Deep Deterministic Policy Gradient (DDPG) is the DRL algorithm for training to output continuous control actions. We investigated the relationship between collision avoidance for safety and jerk minimization for passenger comfort in the multi-objective reward function by obtaining the Pareto front. We found that, with a small jerk penalty in the multi-objective reward function, the vehicle jerk could be reduced by 73% compared with no jerk penalty while the collision rate was maintained at zero. Regardless of the jerk penalty, the merging vehicle exhibited decision-making strategies such as merging ahead or behind a main-road vehicle.

Stroke rehabilitation technologies have focused on reducing treatment cost while improving effectiveness. Rehabilitation robots are generally developed for home and clinical usage to: 1) deliver repetitive practice to post-stroke patients, 2) minimize therapist interventions, and 3) increase the number of patients per therapist, thereby decreasing the associated cost. The control of rehabilitation robots is often limited to black-or gray-box approaches; thus, safety issues regarding the human-robot interaction are not easily considered. To overcome this issue, controllers working with physics-based models gain more importance. In this study, we have developed an efficient two dimensional (2D) human-robot interaction model to implement a model-based controller on a planar end-effector-type rehabilitation robot. The developed model was used within a nonlinear model predictive control (NMPC) structure to control the rehabilitation robot. The GPOPS-II optimal control package was used to implement the proposed NMPC structure. The controller performance was evaluated by simulating the human-robot rehabilitation system, modeled in MapleSim®. In this system, a musculoskeletal model of the arm interacting with the robot is used to predict movement and muscle activation patterns, which are used by the controller to provide optimal assistance to the patient. In simulations, the controller achieved desired performance and predicted muscular activities of the dysfunctional subject with a good accuracy. In our future work, a structure exploiting the NMPC framework will be developed for the real-time control of the rehabilitation robot.

This paper addresses the challenging problem of finding collision-free trajectories for many robots moving toward individual goals within a common environment. Most popular algorithms for multirobot planning manage the complexity of the problem by planning trajectories for robots individually; such decoupled methods are not guaranteed to find a solution if one exists. In contrast, this paper describes a multiphase approach to the planning problem that uses a graph and spanning tree representation to create and maintain obstacle-free paths through the environment for each robot to reach its goal. The resulting algorithm guarantees a solution for a well-defined number of robots in a common environment. The computational cost is shown to be scalable with complexity linear in the number of the robots, and demonstrated by solving the planning problem for 100 robots, simulated in an underground mine environment, in less than 1.5 s with a 1.5 GHz processor. The practicality of the algorithm is demonstrated in a real-world application requiring coordinated motion planning of multiple physical robots.