Realizing Torque Controllers for Underactuated Bipedal Walking Using the Ideal Model Resolved Motion Method

This thesis presents an application of hybrid zero dynamics to realize underactuated bipedal walking on DURUS, a testbed designed and built by SRI International. The main contribution of this work is the ideal model resolved motion method (IMRMM), which is a simple method to convert ideal torque controllers to PD controllers to implement on hardware. Walking was first achieved using the proven method of the hybrid zero dynamics (HZD) reconstruction, followed by the Input-Output Feedback Linearization (IO) and Rapidly Exponentially Stabilizing Control Lyapunov Function Quadratic Programs (CLF-QPs) torque controllers implemented via IMRMM. The simulation and experimental results are presented and compared, and the best resulting specific cost of electrical transport on hardware was computed as 0.63 for the CLF-QP IM-RMM controller, and the record for walking was achieved on a separate occasion with the same CLF-QP IM-RMM controller, which yielded walking for 2 hours and 53 minutes, covering 7 km

Diffusion Policy: Visuomotor Policy Learning via Action Diffusion

This paper introduces Diffusion Policy, a new way of generating robot behavior by representing a robot's visuomotor policy as a conditional denoising diffusion process. We benchmark Diffusion Policy across 11 different tasks from 4 different robot manipulation benchmarks and find that it consistently outperforms existing state-of-the-art robot learning methods with an average improvement of 46.9%. Diffusion Policy learns the gradient of the action-distribution score function and iteratively optimizes with respect to this gradient field during inference via a series of stochastic Langevin dynamics steps. We find that the diffusion formulation yields powerful advantages when used for robot policies, including gracefully handling multimodal action distributions, being suitable for high-dimensional action spaces, and exhibiting impressive training stability. To fully unlock the potential of diffusion models for visuomotor policy learning on physical robots, this paper presents a set of key technical contributions including the incorporation of receding horizon control, visual conditioning, and the time-series diffusion transformer. We hope this work will help motivate a new generation of policy learning techniques that are able to leverage the powerful generative modeling capabilities of diffusion models. Code, data, and training details will be publicly available

Realizing Dynamic and Efficient Bipedal Locomotion on the Humanoid Robot DURUS

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.DOI: 10.1109/ICRA.2016.7487325This paper presents the methodology used to achieve efficient and dynamic walking behaviors on the prototype humanoid robotics platform, DURUS. As a means of providing a hardware platform capable of these behaviors, the design of DURUS combines highly efficient electromechanical components with “control in the loop” design of the leg morphology. Utilizing the final design of DURUS, a formal framework for the generation of dynamic walking gaits which maximizes efficiency by exploiting the full body dynamics of the robot, including the interplay between the passive and active elements, is developed. The gaits generated through this methodology form the basis of the control implementation experimentally realized on DURUS; in particular, the trajectories generated through the formal framework yield a feedforward control input which is modulated by feedback in the form of regulators that compensate for discrepancies between the model and physical system. The end result of the unified approach to control-informed mechanical design, formal gait design and regulator-based feedback control implementation is efficient and dynamic locomotion on the humanoid robot DURUS. In particular, DURUS was able to demonstrate dynamic locomotion at the DRC Finals Endurance Test, walking for just under five hours in a single day, traveling 3.9 km with a mean cost of transport of 1.61-the lowest reported cost of transport achieved on a bipedal humanoid robot

Manipulating the alpha level cannot cure significance testing

We argue that making accept/reject decisions on scientific hypotheses, including a recent call for changing the canonical alpha level from p = 0.05 to p = 0.005, is deleterious for the finding of new discoveries and the progress of science. Given that blanket and variable alpha levels both are problematic, it is sensible to dispense with significance testing altogether. There are alternatives that address study design and sample size much more directly than significance testing does; but none of the statistical tools should be taken as the new magic method giving clear-cut mechanical answers. Inference should not be based on single studies at all, but on cumulative evidence from multiple independent studies. When evaluating the strength of the evidence, we should consider, for example, auxiliary assumptions, the strength of the experimental design, and implications for applications. To boil all this down to a binary decision based on a p-value threshold of 0.05, 0.01, 0.005, or anything else, is not acceptable

Systematic assessment of long-read RNA-seq methods for transcript identification and quantification

The Long-read RNA-Seq Genome Annotation Assessment Project Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. Using different protocols and sequencing platforms, the consortium generated over 427 million long-read sequences from complementary DNA and direct RNA datasets, encompassing human, mouse and manatee species. Developers utilized these data to address challenges in transcript isoform detection, quantification and de novo transcript detection. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. Incorporating additional orthogonal data and replicate samples is advised when aiming to detect rare and novel transcripts or using reference-free approaches. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis

Realizing underactuated bipedal walking with torque controllers via the ideal model resolved motion method

This paper presents experimentally realized bipedal robotic walking using ideal torque controllers via a novel approach termed the ideal model resolved motion method (IM-RMM), where a system's ideal closed-loop dynamics are integrated forward from the actual state of the hardware to provide desired positions and velocity commands to a PD controller. By combining this method with gaits generated using the Human-Inspired Control framework, walking was realized experimentally on the DURUS platform, designed and built by SRI, and achieved with minimal system identification. For comparison, two controllers, one using feedback linearization and one using Control Lyapunov Function based Quadratic Programs (CLF-QP), both realized through IM-RMM, are compared with a benchmark procedure, the Hybrid Zero Dynamics reconstruction, that is shown to provide reliable walking in literature. The results of both simulations and experiments are presented, with the CLF-QP implemented via IM-RMM resulting in the lowest experimental specific energetic cost of transport of c_(et) = 0.63 achieved during sustained walking on the 31.5 kg bipedal robot

Realizing underactuated bipedal walking with torque controllers via the ideal model resolved motion method

This paper presents experimentally realized bipedal robotic walking using ideal torque controllers via a novel approach termed the ideal model resolved motion method (IM-RMM), where a system's ideal closed-loop dynamics are integrated forward from the actual state of the hardware to provide desired positions and velocity commands to a PD controller. By combining this method with gaits generated using the Human-Inspired Control framework, walking was realized experimentally on the DURUS platform, designed and built by SRI, and achieved with minimal system identification. For comparison, two controllers, one using feedback linearization and one using Control Lyapunov Function based Quadratic Programs (CLF-QP), both realized through IM-RMM, are compared with a benchmark procedure, the Hybrid Zero Dynamics reconstruction, that is shown to provide reliable walking in literature. The results of both simulations and experiments are presented, with the CLF-QP implemented via IM-RMM resulting in the lowest experimental specific energetic cost of transport of c_(et) = 0.63 achieved during sustained walking on the 31.5 kg bipedal robot

Analytical approach to study Electromagnetic emission EME contributors on DC/DC applications

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