We introduce a method to optimize on a patient-specific basis the kinematic design of the Continuum Reconfigurable Incisionless Surgical Parallel (CRISP) robot, a needle-diameter medical robot based on a parallel structure that is capable of performing minimally invasive procedures. Our objective is to maximize the ability of the robot's tip camera to view tissue surfaces in constrained spaces. The kinematic design of the CRISP robot, which greatly influences its ability to perform a task, includes parameters that are fixed before the procedure begins, such as entry points into the body and parallel structure connection points. We combine a global stochastic optimization algorithm, Adaptive Simulated Annealing (ASA), with a motion planner designed specifically for the CRISP robot. ASA facilitates exploration of the robot's design space while the motion planner enables evaluation of candidate designs based on their ability to successfully view target regions on a tissue surface. By leveraging motion planning, we ensure that the evaluation of a design only considers motions which do not collide with the patient's anatomy. We analytically show that the method asymptotically converges to a globally optimal solution and demonstrate our algorithm's ability to optimize kinematic designs of the CRISP robot on a patient-specific basis.

We present a robot motion planning approach for performing a learned task while reacting to the movement of obstacles and task-relevant objects. We employ a closed-loop, sampling-based motion planner operating multiple times a second that senses obstacles and task-relevant objects and generates collision-free motion plans based on a learned-task model. The task model is learned from expert demonstrations prior to task execution and is represented as a hidden Markov model. During task execution, our motion planner quickly searches in the Cartesian product of the task model and a probabilistic roadmap for a collision-free plan with features most similar to the demonstrations given the current locations of the task-relevant objects. We accelerate replanning using a fast bidirectional search and by biasing the sampling distribution using information from the learned-task model. We show the efficacy of our approach with the Baxter robot performing two tasks.

In unstructured environments in people's homes and workspaces, robots executing a task may need to avoid obstacles while satisfying task motion constraints, e.g., keeping a plate of food level to avoid spills or properly orienting a finger to push a button. We introduce a sampling-based method for computing motion plans that are collision-free and minimize a cost metric that encodes task motion constraints. Our time-dependent cost metric, learned from a set of demonstrations, encodes features of a task's motion that are consistent across the demonstrations and, hence, are likely required to successfully execute the task. Our sampling-based motion planner uses the learned cost metric to compute plans that simultaneously avoid obstacles and satisfy task constraints. The motion planner is asymptotically optimal and minimizes the Mahalanobis distance between the planned trajectory and the distribution of demonstrations in a feature space parameterized by the locations of task-relevant objects. The motion planner also leverages the distribution of the demonstrations to significantly reduce plan computation time. We demonstrate the method's effectiveness and speed using a small humanoid robot performing tasks requiring both obstacle avoidance and satisfaction of learned task constraints. NOTE TO PRACTITIONERS: Motivated by the desire to enable robots to autonomously operate in cluttered home and workplace environments, this paper presents an approach for intuitively training a robot in a manner that enables it to repeat the task in novel scenarios and in the presence of unforeseen obstacles in the environment. Based on user-provided demonstrations of the task, our method learns features of the task that are consistent across the demonstrations and that we expect should be repeated by the robot when performing the task. We next present an efficient algorithm for planning robot motions to perform the task based on the learned features while avoiding obstacles. We demonstrate the effectiveness of our motion planner for scenarios requiring transferring a powder and pushing a button in environments with obstacles, and we plan to extend our results to more complex tasks in the future.

We present a motion planning approach for performing a learned task while avoiding obstacles and reacting to the movement of task-relevant objects. We employ a closed-loop sampling-based motion planner that acquires new sensor information, generates new collision-free plans that are based on a learned task model, and replans at an average rate of more than 10 times per second for a 7-DOF manipulator. The task model is learned from expert demonstrations prior to task execution and is represented as a hidden Markov model. During task execution, our motion planner quickly searches in the Cartesian product of the task model and a probabilistic roadmap for a plan with features most similar to the demonstrations given the locations of the task-relevant objects. We improve the replan rate by using a fast bidirectional search and by biasing the sampling distribution using information from the learned task model to construct high-quality roadmaps. We illustrate the efficacy of our approach by performing a simulated navigation task with a 2D point robot and a physical powder transfer task with the Baxter robot.