289 research outputs found
Keep Rollin' - Whole-Body Motion Control and Planning for Wheeled Quadrupedal Robots
We show dynamic locomotion strategies for wheeled quadrupedal robots, which
combine the advantages of both walking and driving. The developed optimization
framework tightly integrates the additional degrees of freedom introduced by
the wheels. Our approach relies on a zero-moment point based motion
optimization which continuously updates reference trajectories. The reference
motions are tracked by a hierarchical whole-body controller which computes
optimal generalized accelerations and contact forces by solving a sequence of
prioritized tasks including the nonholonomic rolling constraints. Our approach
has been tested on ANYmal, a quadrupedal robot that is fully torque-controlled
including the non-steerable wheels attached to its legs. We conducted
experiments on flat and inclined terrains as well as over steps, whereby we
show that integrating the wheels into the motion control and planning framework
results in intuitive motion trajectories, which enable more robust and dynamic
locomotion compared to other wheeled-legged robots. Moreover, with a speed of 4
m/s and a reduction of the cost of transport by 83 % we prove the superiority
of wheeled-legged robots compared to their legged counterparts.Comment: IEEE Robotics and Automation Letter
RLOC: Terrain-Aware Legged Locomotion using Reinforcement Learning and Optimal Control
We present a unified model-based and data-driven approach for quadrupedal
planning and control to achieve dynamic locomotion over uneven terrain. We
utilize on-board proprioceptive and exteroceptive feedback to map sensory
information and desired base velocity commands into footstep plans using a
reinforcement learning (RL) policy trained in simulation over a wide range of
procedurally generated terrains. When ran online, the system tracks the
generated footstep plans using a model-based controller. We evaluate the
robustness of our method over a wide variety of complex terrains. It exhibits
behaviors which prioritize stability over aggressive locomotion. Additionally,
we introduce two ancillary RL policies for corrective whole-body motion
tracking and recovery control. These policies account for changes in physical
parameters and external perturbations. We train and evaluate our framework on a
complex quadrupedal system, ANYmal version B, and demonstrate transferability
to a larger and heavier robot, ANYmal C, without requiring retraining.Comment: 19 pages, 15 figures, 6 tables, 1 algorithm, submitted to T-RO; under
revie
Whole-Body MPC and Online Gait Sequence Generation for Wheeled-Legged Robots
Our paper proposes a model predictive controller as a single-task formulation
that simultaneously optimizes wheel and torso motions. This online joint
velocity and ground reaction force optimization integrates a kinodynamic model
of a wheeled quadrupedal robot. It defines the single rigid body dynamics along
with the robot's kinematics while treating the wheels as moving ground
contacts. With this approach, we can accurately capture the robot's rolling
constraint and dynamics, enabling automatic discovery of hybrid maneuvers
without needless motion heuristics. The formulation's generality through the
simultaneous optimization over the robot's whole-body variables allows for a
single set of parameters and makes online gait sequence adaptation possible.
Aperiodic gait sequences are automatically found through kinematic leg
utilities without the need for predefined contact and lift-off timings,
reducing the cost of transport by up to 85%. Our experiments demonstrate
dynamic motions on a quadrupedal robot with non-steerable wheels in challenging
indoor and outdoor environments. The paper's findings contribute to evaluating
a decomposed, i.e., sequential optimization of wheel and torso motion, and
single-task motion planner with a novel quantity, the prediction error, which
describes how well a receding horizon planner can predict the robot's future
state. To this end, we report an improvement of up to 71% using our proposed
single-task approach, making fast locomotion feasible and revealing
wheeled-legged robots' full potential.Comment: 8 pages, 6 figures, 1 table, 52 references, 9 equation
Hierarchical Experience-informed Navigation for Multi-modal Quadrupedal Rebar Grid Traversal
This study focuses on a layered, experience-based, multi-modal contact
planning framework for agile quadrupedal locomotion over a constrained rebar
environment. To this end, our hierarchical planner incorporates
locomotion-specific modules into the high-level contact sequence planner and
solves kinodynamically-aware trajectory optimization as the low-level motion
planner. Through quantitative analysis of the experience accumulation process
and experimental validation of the kinodynamic feasibility of the generated
locomotion trajectories, we demonstrate that the experience planning heuristic
offers an effective way of providing candidate footholds for a legged contact
planner. Additionally, we introduce a guiding torso path heuristic at the
global planning level to enhance the navigation success rate in the presence of
environmental obstacles. Our results indicate that the torso-path guided
experience accumulation requires significantly fewer offline trials to
successfully reach the goal compared to regular experience accumulation.
Finally, our planning framework is validated in both dynamics simulations and
real hardware implementations on a quadrupedal robot provided by Skymul Inc
Quadrupedal Footstep Planning using Learned Motion Models of a Black-Box Controller
Legged robots are increasingly entering new domains and applications,
including search and rescue, inspection, and logistics. However, for such
systems to be valuable in real-world scenarios, they must be able to
autonomously and robustly navigate irregular terrains. In many cases, robots
that are sold on the market do not provide such abilities, being able to
perform only blind locomotion. Furthermore, their controller cannot be easily
modified by the end-user, requiring a new and time-consuming control synthesis.
In this work, we present a fast local motion planning pipeline that extends the
capabilities of a black-box walking controller that is only able to track
high-level reference velocities. More precisely, we learn a set of motion
models for such a controller that maps high-level velocity commands to Center
of Mass (CoM) and footstep motions. We then integrate these models with a
variant of the A star algorithm to plan the CoM trajectory, footstep sequences,
and corresponding high-level velocity commands based on visual information,
allowing the quadruped to safely traverse irregular terrains at demand
Receding-horizon motion planning of quadrupedal robot locomotion
Quadrupedal robots are designed to offer efficient and robust mobility on uneven terrain. This thesis investigates combining numerical optimization and machine learning methods to achieve interpretable kinodynamic planning of natural and agile locomotion.
The proposed algorithm, called Receding-Horizon Experience-Controlled Adaptive Legged Locomotion (RHECALL), uses nonlinear programming (NLP) with learned initialization to produce long-horizon, high-fidelity, terrain-aware, whole-body trajectories. RHECALL has been implemented and validated on the ANYbotics ANYmal B and C quadrupeds on complex terrain.
The proposed optimal control problem formulation uses the single-rigid-body dynamics (SRBD) model and adopts a direct collocation transcription method which enables the discovery of aperiodic contact sequences. To generate reliable trajectories, we propose fast-to-compute analytical costs that leverage the discretization and terrain-dependent kinematic constraints.
To extend the formulation to receding-horizon planning, we propose a segmentation approach with asynchronous centre of mass (COM) and end-effector timings and a heuristic initialization scheme which reuses the previous solution. We integrate real-time 2.5D perception data for online foothold selection. Additionally, we demonstrate that a learned stability criterion can be incorporated into the planning framework.
To accelerate the convergence of the NLP solver to locally optimal solutions, we propose data-driven initialization schemes trained using supervised and unsupervised behaviour cloning. We demonstrate the computational advantage of the schemes and the ability to leverage latent space to reconstruct dynamic segments of plans which are several seconds long.
Finally, in order to apply RHECALL to quadrupeds with significant leg inertias, we derive the more accurate lump leg single-rigid-body dynamics (LL-SRBD) and centroidal dynamics (CD) models and their first-order partial derivatives. To facilitate intuitive usage of costs, constraints and initializations, we parameterize these models by Euclidean-space variables. We show the models have the ability to shape rotational inertia of the robot which offers potential to further improve agility
When and Where to Step: Terrain-Aware Real-Time Footstep Location and Timing Optimization for Bipedal Robots
Online footstep planning is essential for bipedal walking robots, allowing
them to walk in the presence of disturbances and sensory noise. Most of the
literature on the topic has focused on optimizing the footstep placement while
keeping the step timing constant. In this work, we introduce a footstep planner
capable of optimizing footstep placement and step time online. The proposed
planner, consisting of an Interior Point Optimizer (IPOPT) and an optimizer
based on Augmented Lagrangian (AL) method with analytical gradient descent,
solves the full dynamics of the Linear Inverted Pendulum (LIP) model in real
time to optimize for footstep location as well as step timing at the rate of
200~Hz. We show that such asynchronous real-time optimization with the AL
method (ARTO-AL) provides the required robustness and speed for successful
online footstep planning. Furthermore, ARTO-AL can be extended to plan
footsteps in 3D, allowing terrain-aware footstep planning on uneven terrains.
Compared to an algorithm with no footstep time adaptation, our proposed ARTO-AL
demonstrates increased stability in simulated walking experiments as it can
resist pushes on flat ground and on a ramp up to 120 N and 100 N
respectively. For the video, see https://youtu.be/ABdnvPqCUu4. For code, see
https://github.com/WangKeAlchemist/ARTO-AL/tree/master.Comment: 32 pages, 15 figures. Submitted to Robotics and Autonomous System
Reactive Stepping for Humanoid Robots using Reinforcement Learning: Application to Standing Push Recovery on the Exoskeleton Atalante
State-of-the-art reinforcement learning is now able to learn versatile
locomotion, balancing and push-recovery capabilities for bipedal robots in
simulation. Yet, the reality gap has mostly been overlooked and the simulated
results hardly transfer to real hardware. Either it is unsuccessful in practice
because the physics is over-simplified and hardware limitations are ignored, or
regularity is not guaranteed, and unexpected hazardous motions can occur. This
paper presents a reinforcement learning framework capable of learning robust
standing push recovery for bipedal robots that smoothly transfer to reality,
providing only instantaneous proprioceptive observations. By combining original
termination conditions and policy smoothness conditioning, we achieve stable
learning, sim-to-real transfer and safety using a policy without memory nor
explicit history. Reward engineering is then used to give insights into how to
keep balance. We demonstrate its performance in reality on the lower-limb
medical exoskeleton Atalante
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