15 research outputs found
Efficient Humanoid Contact Planning using Learned Centroidal Dynamics Prediction
Humanoid robots dynamically navigate an environment by interacting with it
via contact wrenches exerted at intermittent contact poses. Therefore, it is
important to consider dynamics when planning a contact sequence. Traditional
contact planning approaches assume a quasi-static balance criterion to reduce
the computational challenges of selecting a contact sequence over a rough
terrain. This however limits the applicability of the approach when dynamic
motions are required, such as when walking down a steep slope or crossing a
wide gap. Recent methods overcome this limitation with the help of efficient
mixed integer convex programming solvers capable of synthesizing dynamic
contact sequences. Nevertheless, its exponential-time complexity limits its
applicability to short time horizon contact sequences within small
environments. In this paper, we go beyond current approaches by learning a
prediction of the dynamic evolution of the robot centroidal momenta, which can
then be used for quickly generating dynamically robust contact sequences for
robots with arms and legs using a search-based contact planner. We demonstrate
the efficiency and quality of the results of the proposed approach in a set of
dynamically challenging scenarios
Topology-Based MPC for Automatic Footstep Placement and Contact Surface Selection
State-of-the-art approaches to footstep planning assume reduced-order
dynamics when solving the combinatorial problem of selecting contact surfaces
in real time. However, in exchange for computational efficiency, these
approaches ignore joint torque limits and limb dynamics. In this work, we
address these limitations by presenting a topology-based approach that
enables~\gls{mpc} to simultaneously plan full-body motions, torque commands,
footstep placements, and contact surfaces in real time. To determine if a
robot's foot is inside a contact surface, we borrow the winding number concept
from topology. We then use this winding number and potential field to create a
contact-surface penalty function. By using this penalty function,~\gls{mpc} can
select a contact surface from all candidate surfaces in the vicinity and
determine footstep placements within it. We demonstrate the benefits of our
approach by showing the impact of considering full-body dynamics, which
includes joint torque limits and limb dynamics, on the selection of footstep
placements and contact surfaces. Furthermore, we validate the feasibility of
deploying our topology-based approach in an~\gls{mpc} scheme and explore its
potential capabilities through a series of experimental and simulation trials.Comment: 7 pages, 6 figure
Learning for Humanoid Multi-Contact Navigation Planning
Humanoids' abilities to navigate uneven terrain make them well-suited for disaster response efforts, but humanoid motion planning in unstructured environments remains a challenging problem. In this dissertation we focus on planning contact sequences for a humanoid robot navigating in large unstructured environments using multi-contact motion, including both foot and palm contacts. In particular, we address the two following questions: (1) How do we efficiently generate a feasible contact sequence? and (2) How do we efficiently generate contact sequences which lead to dynamically-robust motions?
For the first question, we propose a library-based method that retrieves motion plans from a library constructed offline, and adapts them with local trajectory optimization to generate the full motion plan from the start to the goal. This approach outperforms a conventional graph search contact planner when it is difficult to decide which contact is preferable with a simplified robot model and local environment information. We also propose a learning approach to estimate the difficulty to traverse a certain region based on the environment features. By integrating the two approaches, we propose a planning framework that uses graph search planner to find contact sequences around easy regions. When it is necessary to go through a difficult region, the framework switches to use the library-based method around the region to find a feasible contact sequence faster.
For the second question, we consider dynamic motions in contact planning. Most humanoid motion generators do not optimize the dynamic robustness of a contact sequence. By querying a learned model to predict the dynamic feasibility and robustness of each contact transition from a centroidal dynamics optimizer, the proposed planner efficiently finds contact sequences which lead to dynamically-robust motions. We also propose a learning-based footstep planner which takes external disturbances into account. The planner considers not only the poses of the planned contact sequence, but also alternative contacts near the planned contact sequence that can be used to recover from external disturbances. Neural networks are trained to efficiently predict multi-contact zero-step and one-step capturability, which allows the planner to generate contact sequences robust to external disturbances efficiently.PHDRoboticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162908/1/linyuchi_1.pd
Automatic Gait Pattern Selection for Legged Robots
An important issue when synthesizing legged locomotion plans is the combinatorial complexity that arises from gait pattern selection. Though it can be defined manually, the gait pattern plays an important role in the feasibility and optimality of a motion with respect to a task. Replacing human intuition with an automatic and efficient approach for gait pattern selection would allow for more autonomous robots, responsive to task and environment changes. To this end, we propose the idea of building a map from task to gait pattern selection for given environment and performance objective. Indeed, we show that for a 2D half-cheetah model and a quadruped robot, a direct mapping between a given task and an optimal gait pattern can be established. We use supervised learning to capture the structure of this map in a form of gait regions. Furthermore, we propose to construct a warm-starting trajectory for each gait region. We empirically show that these warm-starting trajectories improve the convergence speed of our trajectory optimization problem up to 60 times when compared with random initial guesses. Finally, we conduct experimental trials on the ANYmal robot to validate our method.</p
Inverse-Dynamics MPC via Nullspace Resolution
Optimal control (OC) using inverse dynamics provides numerical benefits such
as coarse optimization, cheaper computation of derivatives, and a high
convergence rate. However, in order to take advantage of these benefits in
model predictive control (MPC) for legged robots, it is crucial to handle its
large number of equality constraints efficiently. To accomplish this, we first
(i) propose a novel approach to handle equality constraints based on nullspace
parametrization. Our approach balances optimality, and both dynamics and
equality-constraint feasibility appropriately, which increases the basin of
attraction to good local minima. To do so, we then (ii) adapt our
feasibility-driven search by incorporating a merit function. Furthermore, we
introduce (iii) a condensed formulation of the inverse dynamics that considers
arbitrary actuator models. We also develop (iv) a novel MPC based on inverse
dynamics within a perception locomotion framework. Finally, we present (v) a
theoretical comparison of optimal control with the forward and inverse
dynamics, and evaluate both numerically. Our approach enables the first
application of inverse-dynamics MPC on hardware, resulting in state-of-the-art
dynamic climbing on the ANYmal robot. We benchmark it over a wide range of
robotics problems and generate agile and complex maneuvers. We show the
computational reduction of our nullspace resolution and condensed formulation
(up to 47.3%). We provide evidence of the benefits of our approach by solving
coarse optimization problems with a high convergence rate (up to 10 Hz of
discretization). Our algorithm is publicly available inside CROCODDYL.Comment: 17 pages, 14 figures, under-revie
Recent Progress in Legged Robots Locomotion Control
International audiencePurpose of review. In recent years, legged robots locomotion has been transitioning from mostly flat ground in controlled settings to generic indoor and outdoor environments, approaching now real industrial scenarios. This paper aims at documenting some of the key progress made in legged locomotion control that enabled this transition. Recent findings. Legged locomotion control makes extensive use of numerical trajectory optimization and its online implementation, Model Predictive Control. A key progress has been how this optimization is handled, with refined models and refined numerical methods. This led the legged locomotion research community to heavily invest in and contribute to the development of new optimization methods and efficient numerical software
Optimization-Based Control for Dynamic Legged Robots
In a world designed for legs, quadrupeds, bipeds, and humanoids have the
opportunity to impact emerging robotics applications from logistics, to
agriculture, to home assistance. The goal of this survey is to cover the recent
progress toward these applications that has been driven by model-based
optimization for the real-time generation and control of movement. The majority
of the research community has converged on the idea of generating locomotion
control laws by solving an optimal control problem (OCP) in either a
model-based or data-driven manner. However, solving the most general of these
problems online remains intractable due to complexities from intermittent
unidirectional contacts with the environment, and from the many degrees of
freedom of legged robots. This survey covers methods that have been pursued to
make these OCPs computationally tractable, with specific focus on how
environmental contacts are treated, how the model can be simplified, and how
these choices affect the numerical solution methods employed. The survey
focuses on model-based optimization, covering its recent use in a stand alone
fashion, and suggesting avenues for combination with learning-based
formulations to further accelerate progress in this growing field.Comment: submitted for initial review; comments welcom
Learning-based methods for planning and control of humanoid robots
Nowadays, humans and robots are more and more likely to coexist as time goes by. The anthropomorphic nature of humanoid robots facilitates physical human-robot interaction, and makes social human-robot interaction more natural. Moreover, it makes humanoids ideal candidates for many applications related to tasks and environments designed for humans.
No matter the application, an ubiquitous requirement for the humanoid is to possess proper locomotion skills. Despite long-lasting research, humanoid locomotion is still far from being a trivial task. A common approach to address humanoid locomotion consists in decomposing its complexity by means of a model-based hierarchical control architecture. To cope with computational constraints, simplified models for the humanoid are employed in some of the architectural layers. At the same time, the redundancy of the humanoid with respect to the locomotion task as well as the closeness of such a task to human locomotion suggest a data-driven approach to learn it directly from experience.
This thesis investigates the application of learning-based techniques to planning and control of humanoid locomotion. In particular, both deep reinforcement learning and deep supervised learning are considered to address humanoid locomotion tasks in a crescendo of complexity.
First, we employ deep reinforcement learning to study the spontaneous emergence of balancing and push recovery strategies for the humanoid, which represent essential prerequisites for more complex locomotion tasks.
Then, by making use of motion capture data collected from human subjects, we employ deep supervised learning to shape the robot walking trajectories towards an improved human-likeness.
The proposed approaches are validated on real and simulated humanoid robots. Specifically, on two versions of the iCub humanoid: iCub v2.7 and iCub v3