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 learning directly on hardware. This motivates using data-efficient learning techniques like Bayesian Optimization (BO) to minimize collecting expensive data samples. BO is a black-box data-efficient optimization scheme, though its performance typically degrades in higher dimensions. We aim to overcome this problem by incorporating domain knowledge, with a focus on bipedal locomotion. In our previous work, we proposed a feature transformation that projected a 16-dimensional locomotion controller to a 1-dimensional space using knowledge of human walking. When optimizing a human-inspired neuromuscular controller in simulation, this feature transformation enhanced sample efficiency of BO over traditional BO with a Squared Exponential kernel. In this paper, we present a generalized feature transform applicable to non-humanoid robot morphologies and evaluate it on the ATRIAS bipedal robot, in both simulation and hardware. We present three different walking controllers and two are evaluated on the real robot. Our results show that this feature transform captures important aspects of walking and accelerates learning on hardware and simulation, as compared to traditional BO.

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 running machines, it has so far not been demonstrated on a physical robot. Here we address this implementation and verification step for a human-sized bipedal robot platform confined to a boom. We detail challenges and solutions for the implementation of the control approach and show that it leads to very robust running (ground changes ±20% of leg length) over unobserved ground in a high fidelity simulation of the robot platform. We also present initial tests on the actual robot hardware, which indicate the feasibility of the approach. If it can be generalized to 3D running, it could trigger running machines with largely improved robustness.