Multi-Contact Inertial Estimation and Localization in Legged Robots

Optimal estimation is a promising tool for multi-contact inertial estimation and localization. To harness its advantages in robotics, it is crucial to solve these large and challenging optimization problems efficiently. To tackle this, we (i) develop a multiple-shooting solver that exploits both temporal and parametric structures through a parametrized Riccati recursion. Additionally, we (ii) propose an inertial local manifold that ensures its full physical consistency. It also enhances convergence compared to the singularity-free log-Cholesky approach. To handle its singularities, we (iii) introduce a nullspace approach in our optimal estimation solver. We (iv) finally develop the analytical derivatives of contact dynamics for both inertial parametrizations. Our framework can successfully solve estimation problems for complex maneuvers such as brachiation in humanoids. We demonstrate its numerical capabilities across various robotics tasks and its benefits in experimental trials with the Go1 robot.

Efficient, Dynamic Locomotion through Step Placement with Straight Legs and Rolling Contacts

For humans, fast, efficient walking over flat ground represents the vast majority of locomotion that an individual experiences on a daily basis, and for an effective, real-world humanoid robot the same will likely be the case. In this work, we propose a locomotion controller for efficient walking over near-flat ground using a relatively simple, model-based controller that utilizes a novel combination of several interesting design features including an ALIP-based step adjustment strategy, stance leg length control as an alternative to center of mass height control, and rolling contact for heel-to-toe motion of the stance foot. We then present the results of this controller on our robot Nadia, both in simulation and on hardware. These results include validation of this controller's ability to perform fast, reliable forward walking at 0.75 m/s along with backwards walking, side-stepping, turning in place, and push recovery. We also present an efficiency comparison between the proposed control strategy and our baseline walking controller over three steady-state walking speeds. Lastly, we demonstrate some of the benefits of utilizing rolling contact in the stance foot, specifically the reduction of necessary positive and negative work throughout the stride.

Authoring and Operating Humanoid Behaviors On the Fly using Coactive Design Principles

Humanoid robots have the potential to perform useful tasks in a world built for humans. However, communicating intention and teaming with a humanoid robot is a multi-faceted and complex problem. In this paper, we tackle the problems associated with quickly and interactively authoring new robot behavior that works on real hardware. We bring the powerful concepts of Affordance Templates and Coactive Design methodology to this problem to attempt to solve and explain it. In our approach we use interactive stance and hand pose goals along with other types of actions to author humanoid robot behavior on the fly. We then describe how our operator interface works to author behaviors on the fly and provide interdependence analysis charts for task approach and door opening. We present timings from real robot performances for traversing a push door and doing a pick and place task on our Nadia humanoid robot.

Reachability Aware Capture Regions with Time Adjustment and Cross-Over for Step Recovery

For humanoid robots to live up to their potential utility, they must be able to robustly recover from instabilities. In this work, we propose a number of balance enhancements to enable the robot to both achieve specific, desired footholds in the world and adjusting the step positions and times as necessary while leveraging ankle and hip. This includes improving the calculation of capture regions for bipedal locomotion to better consider how step constraints affect the ability to recover. We then explore a new strategy for performing cross-over steps to maintain stability, which greatly enhances the variety of tracking error from which the robot may recover. Our last contribution is a strategy for time adaptation during the transfer phase for recovery. We then present these results on our humanoid robot, Nadia, in both simulation and hardware, showing the robot walking over rough terrain, recovering from external disturbances, and taking cross-over steps to maintain balance.

A Virtual-Reality Driven Approach for Generating Humanoid Multi-Contact Trajectories

We present a virtual reality (VR) framework designed to intuitively generate humanoid multi-contact maneuvers for use in unstructured environments. Our framework allows the operator to directly manipulate the inverse kinematics objectives which parameterize a trajectory. Kinematic objectives consisting of spatial poses, center-of-mass position and joint positions are used in an optimization based inverse kinematics solver to compute whole-body configurations while enforcing static contact stability. Virtual ``anchors'' allow the operator to freely drag and constrain the robot as well as modify objective weights and constraint sets. The interface's design novelty is a generalized use of anchors which enables arbitrary posture and contact modes. The operator is aided by visual cues of actuation feasibility and tools for rapid anchor placement. We demonstrate our approach in simulation and hardware on a NASA Valkyrie humanoid, focusing on multi-contact trajectories which are challenging to generate autonomously or through alternative teleoperation approaches.

Angular Center of Mass for Humanoid Robots

The center of mass (CoM) has been widely used in planning and control for humanoid locomotion, because it carries key information about the position of a robot. In contrast, an ''angular center of mass'' (ACoM), which provides an ''average'' orientation of a robot, is less well-known in the community, although the concept has been in the literature for about a decade. In this paper, we introduce the ACoM from a CoM perspective. We optimize for an ACoM on the humanoid robot Nadia, and demonstrate its application in walking with natural upper body motion on hardware.

Proprioceptive State Estimation of Legged Robots with Kinematic Chain Modeling

Legged robot locomotion is a challenging task due to a myriad of sub-problems, such as the hybrid dynamics of foot contact and the effects of the desired gait on the terrain. Accurate and efficient state estimation of the floating base and the feet joints can help alleviate much of these issues by providing feedback information to robot controllers. Current state estimation methods are highly reliant on a conjunction of visual and inertial measurements to provide real-time estimates, thus being handicapped in perceptually poor environments. In this work, we show that by leveraging the kinematic chain model of the robot via a factor graph formulation, we can perform state estimation of the base and the leg joints using primarily proprioceptive inertial data. We perform state estimation using a combination of preintegrated IMU measurements, forward kinematic computations, and contact detections in a factor-graph based framework, allowing our state estimate to be constrained by the robot model. Experimental results in simulation and on hardware show that our approach out-performs current proprioceptive state estimation methods by 27% on average, while being generalizable to a variety of legged robot platforms. We demonstrate our results both quantitatively and qualitatively on a wide variety of trajectories.

A Fast, Autonomous, Bipedal Walking Behavior over Rapid Regions

In trying to build humanoid robots that perform useful tasks in a world built for humans, we address the problem of autonomous locomotion. Humanoid robot planning and control algorithms for walking over rough terrain are becoming increasingly capable. At the same time, commercially available depth cameras have been getting more accurate and GPU computing has become a primary tool in AI research. In this paper, we present a newly constructed behavior control system for achieving fast, autonomous, bipedal walking, without pauses or deliberation. We achieve this using a recently published rapid planar regions perception algorithm, a height map based body path planner, an A* footstep planner, and a momentum-based walking controller. We put these elements together to form a behavior control system supported by modern software development practices and simulation tools.

Humanoid Path Planning over Rough Terrain using Traversability Assessment

We present a planning framework designed for humanoid navigation over challenging terrain. This framework is designed to plan a traversable, smooth, and collision-free path using a 2.5D height map. The planner is comprised of two stages. The first stage consists of an A* planner which reasons about traversability using terrain features. A novel cost function is presented which encodes the bipedal gait directly into the graph structure, enabling natural paths that are robust to small gaps in traversability. The second stage is an optimization framework which smooths the path while further improving traversability. The planner is tested on a variety of terrains in simulation and is combined with a footstep planner and balance controller to create an integrated navigation framework, which is demonstrated on a DRC Boston Dynamics Atlas robot.