Whole-body control (WBC) is a generic task-oriented control method for feedback control of loco-manipulation behaviors in humanoid robots. The combination of WBC and model-based walking controllers has been widely utilized in various humanoid robots. However, to date, the WBC method has not been employed for unsupported passive-ankle dynamic locomotion. As such, in this article, we devise a new WBC, dubbed the whole-body locomotion controller (WBLC), that can achieve experimental dynamic walking on unsupported passive-ankle biped robots. A key aspect of WBLC is the relaxation of contact constraints such that the control commands produce reduced jerk when switching foot contacts. To achieve robust dynamic locomotion, we conduct an in-depth analysis of uncertainty for our dynamic walking algorithm called the time-to-velocity-reversal (TVR) planner. The uncertainty study is fundamental as it allows us to improve the control algorithms and mechanical structure of our robot to fulfill the tolerated uncertainty. In addition, we conduct extensive experimentation for: (1) unsupported dynamic balancing (i.e., in-place stepping) with a six-degree-of-freedom biped, Mercury; (2) unsupported directional walking with Mercury; (3) walking over an irregular and slippery terrain with Mercury; and 4) in-place walking with our newly designed ten-DoF viscoelastic liquid-cooled biped, DRACO. Overall, the main contributions of this work are on: (a) achieving various modalities of unsupported dynamic locomotion of passive-ankle bipeds using a WBLC controller and a TVR planner; (b) conducting an uncertainty analysis to improve the mechanical structure and the controllers of Mercury; and (c) devising a whole-body control strategy that reduces movement jerk during walking.

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We propose a locomotion framework for bipedal robots consisting of a new motion planning method, dubbed trajectory optimization for walking robots plus (TOWR+), and a new whole-body control method, dubbed implicit hierarchical whole-body controller (IHWBC). For versatility, we consider the use of a composite rigid body (CRB) model to optimize the robot’s walking behavior. The proposed CRB model considers the floating base dynamics while accounting for the effects of the heavy distal mass of humanoids using a pre-trained centroidal inertia network. TOWR+ leverages the phase-based parameterization of its precursor, TOWR, and optimizes for base and end-effectors motions, feet contact wrenches, as well as contact timing and locations without the need to solve a complementary problem or integer program. The use of IHWBC enforces unilateral contact constraints (i.e., non-slip and non-penetration constraints) and a task hierarchy through the cost function, relaxing contact constraints and providing an implicit hierarchy between tasks. This controller provides additional flexibility and smooth task and contact transitions as applied to our 10 degree-of-freedom, line-feet biped robot DRACO. In addition, we introduce a new open-source and light-weight software architecture, dubbed planning and control (PnC), that implements and combines TOWR+ and IHWBC. PnC provides modularity, versatility, and scalability so that the provided modules can be interchanged with other motion planners and whole-body controllers and tested in an end-to-end manner. In the experimental section, we first analyze the performance of TOWR+ using various bipeds. We then demonstrate balancing behaviors on the DRACO hardware using the proposed IHWBC method. Finally, we integrate TOWR+ and IHWBC and demonstrate step-and-stop behaviors on the DRACO hardware.

The attached video describes the system developed by researchers from MIT for the Defense Advanced Research Projects Agency's (DARPA) Virtual Robotics Challenge (VRC), held in June 2013. The VRC was the first competition in the DARPA Robotics Challenge (DRC), a program that aims to "develop ground robotic capabilities to execute complex tasks in dangerous, degraded, human-engineered environments" [1]. The VRC required teams to guide a model of Boston Dynamics' humanoid robot, Atlas, through driving, walking, and manipulation tasks in simulation.Team MIT's approach centered around teleautomation, in which an operator provided high-level instructions to be carried out autonomously by the robot. The resulting system comprised a 3D user interface coupled with perception, planning, control, and networking modules. The system's perception and planning abilities were assisted by the human operator(s) as needed.Team MIT's user interface, the Viewer, provided the operator with a unified representation of all available information. A 3D rendering of the robot depicted its most recently estimated body state with respect to the surrounding environment, represented by point clouds and texture-mapped meshes as sensed by on-board LIDAR and fused over time.Inertial and kinematic measurements were combined to provide accurate estimates of the robot's body state. The pose of the robot's pelvis was estimated by freely propagating inertial acceleration and rotation rate measurements. Joint angle measurements were combined with the kinematic robot model to propagate special footstep representations in the local navigational frame. Discrepancies between inertial and kinematic estimates were used in a feedback process to correct inertial sensor and navigational state errors.To overcome the uplink restrictions of the VRC, Team MIT's networking module employed an informationtheoretic coding algorithm to compress plans prior to sending them to the robot. This encoder, based on the Goby Project [2], allowed for compression ratios of over 30:1.The planning and control infrastructure consisted of two modes: manipulation and walking. The operator initiated manipulation planning by requesting a grasp for a specified object. A grasp optimizer then suggested an appropriate grasp location for the indicated hand. The operator could then request manipulation plans to reach for, grasp, or move the object. Requests were transmitted to a whole-body planner that computed joint trajectories which could achieve the manipulation goal while satisfying stability constraints. Candidate plans were displayed in the Viewer as a sequence of ghosted renderings of the robot, through which the operator could "scrub." The operator could modify the plan before transmitting it to the robot for autonomous execution.The operator initiated walking planning by specifying a navigation goal that triggered the footstep planner to compute a set of safe footsteps given the current estimate of the terrain. These footsteps were displayed in the Viewer for adjustment as needed. ...

Previous studies of electrorheological fluids (ERFs) were motivated by brake, clutch, damping, haptic and resistive applications, but never motivated towards developing an ERF based-hydraulic rotary actuator. One design to make such an actuator is to use ERF-based valves. To fully understand the performance of such an actuator, it is imperative to study ERF valves. For this reason, this paper presents a summary of design considerations for creating ERF-based actuators, an ERF-based valve design for an ERF actuator and a new experimental test-bed to obtain viscosity and yield characteristics of the ERF at flow rates as low as 0.049 L/min, an order of magnitude lower than industrial rheometers. The new test-bed successfully measured the dynamic viscosity of the ERF to be at 0.6 Pa-s for low flow rates and 0.2 Pa-s for higher flow rates. The presented valve design can successfully resist 1 MPa of fluid pressure, which is an operation mode higher than any haptic and damping applications in the literature. The experiments also shows that higher flow rates negatively affect the ERF's yield characteristics for the first time in a situation where the ERF valve completely blocks flow. When the flow rates are increased, the response time to a fully-closed valve increases, the effective yield capability of the ERF decreases and the conductivity of the ERF increases.

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As part of a feasibility study, this paper shows the NASA Valkyrie humanoid robot performing an endto-end improvised explosive device (IED) response task. To demonstrate and evaluate robot capabilities, sub-tasks highlight different locomotion, manipulation, and perception requirements: traversing uneven terrain, passing through a narrow passageway, opening a car door, retrieving a suspected IED, and securing the IED in a total containment vessel (TCV). For each sub-task, a description of the technical approach and the hidden challenges that were overcome during development are presented. The discussion of results, which explicitly includes existing limitations, is aimed at motivating continued research and development to enable practical deployment of humanoid robots for IED response. For instance, the data shows that operator pauses contribute to 50% of the total completion time, which implies that further work is needed on user interfaces for increasing task completion efficiency. * The authors are with the 1 NASA Johnson Space Center, 2 TRACLabs, the 3 Institute for Human Machine and Cognition (IHMC), 4 Jacobs Technology, 5 METECS, 6 CACI, and 7 The University of Texas at Austin.

Fine-scale evolutionary dynamics can be challenging to tease out when focused on the broad brush strokes of whole populations over long time spans. We propose a suite of diagnostic analysis techniques that operate on lineages and phylogenies in digital evolution experiments, with the aim of improving our capacity to quantitatively explore the nuances of evolutionary histories in digital evolution experiments. We present three types of lineage measurements: lineage length, mutation accumulation, and phenotypic volatility. Additionally, we suggest the adoption of four phylogeny measurements from biology: phylogenetic richness, phylogenetic divergence, phylogenetic regularity, and depth of the most-recent common ancestor. In addition to quantitative metrics, we also discuss several existing data visualizations that are useful for understanding lineages and phylogenies: state sequence visualizations, fitness landscape overlays, phylogenetic trees, and Muller plots. We examine the behavior of these metrics (with the aid of data visualizations) in two well-studied computational contexts: (1) a set of two-dimensional, real-valued optimization problems under a range of mutation rates and selection strengths, and (2) a set of qualitatively different environments in the Avida digital evolution platform. These results confirm our intuition about how these metrics respond to various evolutionary conditions and indicate their broad value.