Real-time remote manipulation requires careful operations by a user to ensure the safety of a robot, which is designed to follow user's commands, against dynamic obstacles. However, a user may give commands to a robot at the risk of collision with dynamic obstacles due to a user's unfamiliar control ability or unexpected situations. In this paper, we propose a risk-aware user command adjustment method to avoid potential collision with dynamic obstacles. Our method consists of a network that predicts the risk of dynamic obstacles and another network that synthesizes commands to avoid obstacles. Based on the predicted risk, our method decides an adjusted command between a user command and a command to avoid collisions. We evaluate our method in problems that face collisions with dynamic obstacles when following given commands and in problems with static obstacles. We show that our method improves safety against the risk of dynamic obstacles or follows user commands when there is no risk. We also demonstrate the feasibility of our method using the real fetch manipulator with seven-degrees-of-freedom.

Trajectory optimization (TO) is an efficient tool to generate a redundant manipulator's joint trajectory following a 6-dimensional Cartesian path. The optimization performance largely depends on the quality of initial trajectories. However, the selection of a high-quality initial trajectory is non-trivial and requires a considerable time budget due to the extremely large space of the solution trajectories and the lack of prior knowledge about task constraints in configuration space. To alleviate the issue, we present a learning-based initial trajectory generation method that generates high-quality initial trajectories in a short time budget by adopting example-guided reinforcement learning. In addition, we suggest a null-space projected imitation reward to consider null-space constraints by efficiently learning kinematically feasible motion captured in expert demonstrations. Our statistical evaluation in simulation shows the improved optimality, efficiency, and applicability of TO when we plug in our method's output, compared with three other baselines. We also show the performance improvement and feasibility via real-world experiments with a seven-degree-of-freedom manipulator.

A redundant manipulator has multiple inverse kinematics solutions per end-effector pose. Accordingly, there can be many trajectories for joints that follow a given end-effector path in the Cartesian space. In this paper, we present a trajectory optimization of a redundant manipulator (TORM) to synthesize a trajectory that follows a given end-effector path accurately, while achieving smoothness and collision-free manipulation. Our method holistically incorporates three desired properties into the trajectory optimization process by integrating the Jacobian-based inverse kinematics solving method and an optimization-based motion planning approach. Specifically, we optimize a trajectory using two-stage gradient descent to reduce potential competition between different properties during the update. To avoid falling into local minima, we iteratively explore different candidate trajectories with our local update. We compare our method with state-of-the-art methods in test scenes including external obstacles and two non-obstacle problems. Our method robustly minimizes the pose error in a progressive manner while satisfying various desirable properties.

Mobile manipulation planning commonly adopts a decoupled approach that performs planning separately on the base and the manipulator. While this approach is fast, it can generate sub-optimal paths. Another direction is a coupled approach jointly adjusting the base and manipulator in a high-dimensional configuration space. This coupled approach addresses sub-optimality and incompleteness of the decoupled approach, but has not been widely used due to its excessive computational overhead. Given this trade-off space, we present a simple, yet effective mobile manipulation sampling method, harmonious sampling, to perform the coupled approach mainly in difficult regions, where we need to simultaneously maneuver the base and the manipulator. Our method identifies such difficult regions through a low-dimensional base space by utilizing a reachability map given the target end-effector pose and narrow passage detected by generalized Voronoi diagram. For the rest of simple regions, we perform sampling mainly on the base configurations with a predefined joint configuration, accelerating the planning process. We compare our method with the decoupled and coupled approaches in six different problems with varying difficulty. Our method shows meaningful improvements experimentally in terms of time to find an initial solution (up to 5.6 times faster) and final solution cost (up to 17% lower) over the decoupled approach, especially in difficult scenes with narrow space. We also demonstrate these benefits with a real, mobile Hubo robot.

We present volumetric tree*, a hybridization of sampling-based and optimization-based motion planning. Volumetric tree*constructs an adaptive sparse graph with volumetric vertices, hyper-spheres encoding free configurations, using a sampling-based motion planner for a homotopy exploration. The coarse-grained paths computed on the sparse graph are refined by optimization-based planning during the execution, while exploiting the probabilistic completeness of the sampling- based planning for the initial path generation. We also suggest a dropout technique probabilistically ensuring that the sampling- based planner is capable of identifying all possible homotopies of solution paths. We compare the proposed algorithm against the state-of-the-art planners in both synthetic and practical benchmarks with varying dimensions, and experimentally show the benefit of the proposed algorithm.