Manipulators can be added to legged robots, allowing them to interact with and change their environment. Legged mobile manipulation planners must consider how contact forces generated by these manipulators affect the system. Current planning strategies either treat these forces as immutable during planning or are unable to optimize over these contact forces while operating in real-time. This paper presents the Stability and Task Oriented Receding-Horizon Motion and Manipulation Autonomous Planner (STORMMAP) that is able to generate continuous plans for the robot’s motion and manipulation force trajectories that ensure dynamic feasibility and stability of the platform, and incentivizes accomplishing manipulation and motion tasks specified by a user. A variety of simulated experiments on a quadruped with a manipulator mounted to its torso demonstrate the versatility of STOR-MMAP. In contrast to existing state of the art methods, the approach described in this paper generates continuous plans in under ten milliseconds, an order of magnitude faster than previous strategies.

We report on the methodology of developing a hybrid model and utilizing it as a template to initiate running behavior in a legged robot. The hybrid model is comprised of a physics-based, rolling, spring-loaded, inverted pendulum (R-SLIP) model, and a data-driven model that compensates for unmodeled dynamics using Gaussian process (GP) regression. The hybrid R-SLIP-GP model retains the R-SLIP's intrinsic running dynamics, as well as improves the template's accuracy without intensive data training and iteration efforts. The proposed hybrid model was evaluated via simulation and in empirical robot running experiments. The results confirm that the added GP-based model greatly improved the model's accuracy, especially discrepancies resulting from the originally difficult-to-model complex leg-ground interactions. In addition, the R-SLIP-GP model was utilized as a complete motion template to design and control the robot's running motion. The experimental results demonstrate that the hybrid template can initiate more stable and power-efficient running motion in the robot than using the R-SLIP template.

This research explored the generation of period-two dynamic running motion in a robot, based on the passive dynamic period-two motion of the reduced order, rolling spring-loaded inverted pendulum (R-SLIP) model. Each cycle of period-two motion consists of two stance phases separated by two flight phases. The distribution of the period-two fixed points of the model was analyzed using a return map. Models with the same or different landing angles per motion cycle were studied, and two sets of period-two motion trajectories were implemented in a robot for experimental evaluation. Without sensory feedback or control, this evaluation relied on the open loop trajectory of the model. Based on the experiments, the robot was capable of performing dynamic period-two motion.

In this paper, we report on the development of the clock-torqued rolling spring loaded inverted pendulum (CTR-SLIP) model. The new model, which adds clock-based torque control on the leg orientation of the previously developed R-SLIP model, has two advantages: first, regulating the model to follow its passive dynamic running (i.e., at a fixed point) significantly increases the model's basin of attraction; and second, formulating the model closer to the empirical robot enables the model to serve as the transient and steady-state running template of the robot as the anchor. These features enable the model/robot to perform speed transition from one fixed point profile to another, and the experimental validation confirms that the robot can successfully transition between two running speeds bidirectionally. The achievement of variable-speed running by the proposed method has a unique merit-it is purely model-based, and there is no need for further tuning, optimization, or learning processes. Regarding the robot, the proposed strategy only requires it to have simple position control to regulate its leg orientations, and there is no need for other sensory modules to provide information for feedback.