Garth Zeglin

Control of a bow leg hopping robot

The bow leg hopper is a new design for a locomotion system with a resilient, flexible leg. It features a passive stance phase and natural pitch stability. It is controlled by actuators that configure the leg angle and stored leg energy during flight. During the stance, the actuators are mechanically decoupled from the leg and the stored energy is released. The trajectory is determined by the spring-mass physics and the state of the leg at impact. This design casts the controller as a function mapping three trajectory parameters to two control outputs once every hopping cycle. Our particular solution uses a combination of graph-search planning and feedback control. The planner searches the sequences of foot placements and computes control outputs using numerical solution of a physical model. The feedback control is computed once per bounce. Experimental data from a planar prototype are included demonstrating navigation of simple artificial terrain.

Minimax differential dynamic programming: application to a biped walking robot

We have developed a robust control policy design and method for high-dimensional state spaces by using differential dynamic programming with a minimax criterion. As an example, we applied our method to a simulated five link biped robot. The results show lower joint torques using the optimal control policy compared to torques generated by a hand-tuned PD servo controller. Results also show that the simulated biped robot can successfully walk with unknown disturbances that cause controllers generated by standard differential dynamic programming and the hand-tuned PD servo to fail. Learning to compensate for modeling error and previously unknown disturbances in conjunction with robust control design is also demonstrated. We applied the proposed method to a real biped robot to optimize swing leg trajectories.We developed a robust control policy design method in high-dimensional state space by using differential dynamic programming with a minimax criterion. As an example, we applied our method to a simulated five link biped robot. The results show lower joint torques from the optimal control policy compared to a hand-tuned PD servo controller. Results also show that the simulated biped robot can successfully walk with unknown disturbances that cause controllers generated by standard differential dynamic programming and the hand-tuned PD servo to fail. Learning to compensate for modeling error and previously unknown disturbances in conjunction with robust control design is also demonstrated. We also applied proposed method to a real biped robot for optimizing swing leg trajectories.

A simple reinforcement learning algorithm for biped walking

We propose a model-based reinforcement learning algorithm for biped walking in which the robot learns to appropriately place the swing leg. This decision is based on a learned model of the Poincare map of the periodic walking pattern. The model maps from a state at the middle of a step and foot placement to a state at next middle of a step. We also modify the desired walking cycle frequency based on online measurements. We present simulation results, and are currently implementing this approach on an actual biped robot.

Poincaré-Map-Based Reinforcement Learning For Biped Walking

We propose a model-based reinforcement learning algorithm for biped walking in which the robot learns to appropriately modulate an observed walking pattern. Via-points are detected from the observed walking trajectories using the minimum jerk criterion. The learning algorithm modulates the via-points as control actions to improve walking trajectories. This decision is based on a learned model of the Poincaré map of the periodic walking pattern. The model maps from a state in the single support phase and the control actions to a state in the next single support phase. We applied this approach to both a simulated robot model and an actual biped robot. We show that successful walking policies are acquired.

Powered bipeds based on passive dynamic principles

We describe three bipedal robots that are designed and controlled based on principles learned from the gaits of passive dynamic walking robots. This paper explains the common control structure and design procedure used to determine the mechanical and control parameters of each robot. We present this work in the context of three robots: Denise, the Delft pneumatic biped, R1, a highly backdrivable electric biped, and R2, a hydraulic biped. This work illustrates the application of passive dynamic principles to powered systems with significant control authority

Preparatory object rotation as a human-inspired grasping strategy

Humans exhibit a rich set of manipulation strategies that may be desirable to mimic in humanoid robots. This study investigates preparatory object rotation as a manipulation strategy for grasping objects from different presented orientations. First, we examine how humans use preparatory rotation as a grasping strategy for lifting heavy objects with handles. We used motion capture to record human manipulation examples of 10 participants grasping objects under different task constraints. When sliding contact of the object on the surface was permitted, participants used preparatory rotation to first adjust the object handle to a desired orientation before grasping to lift the object from the surface. Analysis of the human examples suggests that humans may use preparatory object rotation in order to reuse a particular type of grasp in a specific capture region or to decrease the joint torques required to maintain the lifting pose. Second, we designed a preparatory rotation strategy for an anthropomorphic robot manipulator as a method of extending the capture region of a specific grasp prototype. The strategy was implemented as a sequence of two open-loop actions mimicking the human motion: a preparatory rotation action followed by a grasping action. The grasping action alone can only successfully lift the object from a 45-degree region of initial orientations (4 of 24 tested conditions). Our empirical evaluation of the robot preparatory rotation shows that even using a simple open-loop rotation action enables the reuse of the grasping action for a 360-degree capture region of initial object orientations (24 of 24 tested conditions).

Dynamic Programming in Reduced Dimensional Spaces: Dynamic Planning For Robust Biped Locomotion

We explore the use of computational optimal control techniques for automated construction of policies in complex dynamic environments. Our implementation of dynamic programming is performed in a reduced dimensional subspace of a simulated four-DOF biped robot with point feet. We show that a computed solution to this problem can be generated and yield empirically stable walking that can handle various types of disturbances.

Measuring contact points from displacements with a compliant, articulated robot hand

Manipulators with compliant actuation exhibit passive joint displacements when exposed to external forces or collisions. This paper demonstrates that this displacement information is sufficient to infer a coarse estimate of the location of an incidental collision. Three techniques for contact point detection are compared: a closed-form inference model based on a serial chain with joint springs, a variation on Self Posture Changeability, and an empirical memory-based model of joint trajectories. The methods were experimentally tested using a Shadow Hand on an industrial Motoman SDA10 arm to quantify localization performance, actively discover and avoid a thin obstacle and localize and grasp a cup.

HERB's Sure Thing: A rapid drama system for rehearsing and performing live robot theater

In Spring 2014, the Personal Robotics Lab at CMU collaborated with the School of Drama to develop, produce and stage a live theatrical performance at the Purnell Center for the Arts in Pittsburgh. This paper describes some of our unique experiences collaborating with drama faculty, the director and the actor. We highlight the challenges arising from theatrical performance and specifically describe some of the technical tools we developed: a bidirectional Blender interface for robot animation, an interactive system for manipulating speech prosody, and a conductors console for online improvisation and control during rehearsal and performance. It also explores some of the remaining challenges to our goal of developing algorithms and open-source tools that can enable any roboticist in the world to create their own dramatic performance.

Dexterous telemanipulation with a multi-touch interface

Robust manipulation with a dexterous robot hand is a grand challenge of robotics. Impressive levels of dexterity can be achieved through teleoperation. However, teleoperation devices such as a glove or force reflecting master-slave system can be expensive and can tie the robot down to a restricted workspace. We observe that inexpensive and widely available multi-touch interfaces can achieve excellent performance for a large range of telemanipulation tasks, making dexterous robot telemanipulation broadly accessible. Our key insight is that dexterous grasping and manipulation interactions frequently focus on precise control of the fingertips in a plane. Following this observation, our novel multi-touch interface focuses on reliable replication of planar fingertip trajectories, making previously difficult actions such as grasping, dragging, reorienting, rolling, and smoothing as intuitive as miming the action on a multi-touch surface. We demonstrate and evaluate these and other interactions using an iPad interface to a Shadow Hand mounted on a Motoman SDA10 robot.