16 accepted papers
Policy decomposition is a novel framework for approximating optimal control policies of complex dynamical systems with a hierarchy of policies derived from smaller but tractable subsystems. It stands out amongst the class of hierarchical control methods by estimating <italic xmlns:mml="http://www.w3
Data-driven methods based on neural networks are becoming more widespread for predicting human lower-limb motion. Until now, however, actual examples have focused on only a handful, steady locomotion behaviors. Here we explore if neural network predictors can simultaneously cover many more behaviors…
Avoiding obstacles poses a significant challenge for amputees using mechanically-passive transfemoral prosthetic limbs due to their lack of direct knee control. In contrast, powered prostheses can potentially improve obstacle avoidance via their ability to add energy to the system. In past work, res…
This paper presents a real-time reactive controller for a powered prosthesis that addresses the problem of trip avoidance. The control estimates the pose of the leg during swing with an extended Kalman filter, predicts future hip angles and hip heights using sparse Gaussian Processes, and reactively…
We present a control strategy for powered prostheses based on a robust estimate of the gait phase that is used to determine appropriate control actions. We use an extended Kalman filter (EKF) that fuses joint angle and velocity measurements to estimate the gait phase, which we define in this work to
Learning controllers for bipedal robots is a challenging problem, often requiring expert knowledge and extensive tuning of parameters that vary in different situations. Recently, deep reinforcement learning has shown promise at automatically learning controllers for complex systems in simulation. Th…
We propose a method for optimizing control policies for assistive lower-limb devices. The method frames parameter selection as a dueling bandits problem in which a user indicates his or her qualitative preferences between pairs of parameter sets chosen from a library. We generate the library through…
Robotics controllers often consist of expert-designed heuristics, which can be hard to tune in higher dimensions. Simulation can aid in optimizing these controllers if parameters learned in simulation transfer to hardware. Unfortunately, this is often not the case in legged locomotion, necessitating…
We present a new algorithm for optimizing control policies for human-in-the-loop systems based on qualitative preference feedback. This method is especially applicable to systems such as lower limb prostheses and exoskeletons for which it is difficult to define an objective function, hard to identif
An inspiration for developing a bipedal walking system is the ability to navigate rough terrain with discrete footholds like stepping stones. In this paper, we present a novel methodology to overcome the problem of dynamic walking over stepping stones with significant random changes to step length a…
Theoretical spring mass models with deadbeat foot placement policies reveal very robust running in the presence of large and frequent, unexpected gait disturbances. Although this performance goes beyond what has been demonstrated on running machines, a transfer of this theory has only been investiga
Series elastic actuators primarily use linear springs in their drivetrains, which introduces a design tradeoff: soft springs provide higher torque resolution at the cost of system bandwidth, whereas stiff springs provide a fast response but lower torque resolution. Nonlinear springs (NLSs) potential…
Animals and robots balance dynamically by placing their feet into proper ground targets. While foot placement controls exist for both fully robotic systems and powered prostheses, none enable the dynamism and reactiveness of able-bodied humans. A control approach was recently developed for an ideal…
The analysis of the conceptual spring mass model for running reveals swing-leg placement policies that generate very robust locomotion in unobserved terrain with large changes in ground height. However, while this theoretical result suggests a potential for large improvements on the robustness of ru…
In this paper we propose the fastest converging control policy (also known as deadbeat control) for walking with the bipedal spring-mass model, which serves as an abstraction of a robot on compliant legs. To fully leverage the passive dynamics of the system, the touchdown angle of the swing-leg is a…
Walking controllers for bipedal robots have not yet reached human levels of robustness in locomotion. Imitating the human motor control might be an alternative strategy for generating robust locomotion in robots. We seek to control bipedal robots with a specific neuromuscular human walking model pro…