55 research outputs found
Whole-Body MPC for a Dynamically Stable Mobile Manipulator
Autonomous mobile manipulation offers a dual advantage of mobility provided
by a mobile platform and dexterity afforded by the manipulator. In this paper,
we present a whole-body optimal control framework to jointly solve the problems
of manipulation, balancing and interaction as one optimization problem for an
inherently unstable robot. The optimization is performed using a Model
Predictive Control (MPC) approach; the optimal control problem is transcribed
at the end-effector space, treating the position and orientation tasks in the
MPC planner, and skillfully planning for end-effector contact forces. The
proposed formulation evaluates how the control decisions aimed at end-effector
tracking and environment interaction will affect the balance of the system in
the future. We showcase the advantages of the proposed MPC approach on the
example of a ball-balancing robot with a robotic manipulator and validate our
controller in hardware experiments for tasks such as end-effector pose tracking
and door opening
TeLeMan: Teleoperation for Legged Robot Loco-Manipulation using Wearable IMU-based Motion Capture
Human life is invaluable. When dangerous or life-threatening tasks need to be completed, robotic platforms could be ideal in replacing human operators. Such a task that we focus on in this work is the Explosive Ordnance Disposal. Robot telepresence has the potential to provide safety solutions, given that mobile robots have shown robust capabilities when operating in several environments. However, autonomy may be challenging and risky at this stage, compared to human operation. Teleoperation could be a compromise between full robot autonomy and human presence. In this paper, we present a relatively cheap solution for telepresence and robot teleoperation, to assist with Explosive Ordnance Disposal, using a legged manipulator (i.e., a legged quadruped robot, embedded with a manipulator and RGB-D sensing). We propose a novel system integration for the non-trivial problem of quadruped manipulator whole-body control. Our system is based on a wearable IMU-based motion capture system that is used for teleoperation and a VR headset for visual telepresence. We experimentally validate our method in real-world, for loco-manipulation tasks that require whole-body robot control and visual telepresence
Learning Whole-body Manipulation for Quadrupedal Robot
We propose a learning-based system for enabling quadrupedal robots to
manipulate large, heavy objects using their whole body. Our system is based on
a hierarchical control strategy that uses the deep latent variable embedding
which captures manipulation-relevant information from interactions,
proprioception, and action history, allowing the robot to implicitly understand
object properties. We evaluate our framework in both simulation and real-world
scenarios. In the simulation, it achieves a success rate of 93.6 % in
accurately re-positioning and re-orienting various objects within a tolerance
of 0.03 m and 5 {\deg}. Real-world experiments demonstrate the successful
manipulation of objects such as a 19.2 kg water-filled drum and a 15.3 kg
plastic box filled with heavy objects while the robot weighs 27 kg. Unlike
previous works that focus on manipulating small and light objects using
prehensile manipulation, our framework illustrates the possibility of using
quadrupeds for manipulating large and heavy objects that are ungraspable with
the robot's entire body. Our method does not require explicit object modeling
and offers significant computational efficiency compared to optimization-based
methods. The video can be found at https://youtu.be/fO_PVr27QxU
Pedipulate: Enabling Manipulation Skills using a Quadruped Robot's Leg
Legged robots have the potential to become vital in maintenance, home
support, and exploration scenarios. In order to interact with and manipulate
their environments, most legged robots are equipped with a dedicated robot arm,
which means additional mass and mechanical complexity compared to standard
legged robots. In this work, we explore pedipulation - using the legs of a
legged robot for manipulation. By training a reinforcement learning policy that
tracks position targets for one foot, we enable a dedicated pedipulation
controller that is robust to disturbances, has a large workspace through
whole-body behaviors, and can reach far-away targets with gait emergence,
enabling loco-pedipulation. By deploying our controller on a quadrupedal robot
using teleoperation, we demonstrate various real-world tasks such as door
opening, sample collection, and pushing obstacles. We demonstrate load carrying
of more than 2.0 kg at the foot. Additionally, the controller is robust to
interaction forces at the foot, disturbances at the base, and slippery contact
surfaces. Videos of the experiments are available at
https://sites.google.com/leggedrobotics.com/pedipulate.Comment: Project website:
https://sites.google.com/leggedrobotics.com/pedipulat
Dynamic Object Tracking for Quadruped Manipulator with Spherical Image-Based Approach
Exactly estimating and tracking the motion of surrounding dynamic objects is
one of important tasks for the autonomy of a quadruped manipulator. However,
with only an onboard RGB camera, it is still a challenging work for a quadruped
manipulator to track the motion of a dynamic object moving with unknown and
changing velocities. To address this problem, this manuscript proposes a novel
image-based visual servoing (IBVS) approach consisting of three elements: a
spherical projection model, a robust super-twisting observer, and a model
predictive controller (MPC). The spherical projection model decouples the
visual error of the dynamic target into linear and angular ones. Then, with the
presence of the visual error, the robustness of the observer is exploited to
estimate the unknown and changing velocities of the dynamic target without
depth estimation. Finally, the estimated velocity is fed into the model
predictive controller (MPC) to generate joint torques for the quadruped
manipulator to track the motion of the dynamical target. The proposed approach
is validated through hardware experiments and the experimental results
illustrate the approach's effectiveness in improving the autonomy of the
quadruped manipulator
Whole-body MPC for highly redundant legged manipulators: experimental evaluation with a 37 DoF dual-arm quadruped
Recent progress in legged locomotion has rendered quadruped manipulators a
promising solution for performing tasks that require both mobility and
manipulation (loco-manipulation). In the real world, task specifications and/or
environment constraints may require the quadruped manipulator to be equipped
with high redundancy as well as whole-body motion coordination capabilities.
This work presents an experimental evaluation of a whole-body Model Predictive
Control (MPC) framework achieving real-time performance on a dual-arm quadruped
platform consisting of 37 actuated joints. To the best of our knowledge this is
the legged manipulator with the highest number of joints to be controlled with
real-time whole-body MPC so far. The computational efficiency of the MPC while
considering the full robot kinematics and the centroidal dynamics model builds
upon an open-source DDP-variant solver and a state-of-the-art optimal control
problem formulation. Differently from previous works on quadruped manipulators,
the MPC is directly interfaced with the low-level joint impedance controllers
without the need of designing an instantaneous whole-body controller. The
feasibility on the real hardware is showcased using the CENTAURO platform for
the challenging task of picking a heavy object from the ground. Dynamic
stepping (trotting) is also showcased for first time with this robot. The
results highlight the potential of replanning with whole-body information in a
predictive control loop.Comment: Accepted at the 2023 IEEE-RAS International Conference on Humanoid
Robots (Humanoids 2023), final version with video and acknowledgement
Kinematically-Decoupled Impedance Control for Fast Object Visual Servoing and Grasping on Quadruped Manipulators
We propose a control pipeline for SAG (Searching, Approaching, and Grasping)
of objects, based on a decoupled arm kinematic chain and impedance control,
which integrates image-based visual servoing (IBVS). The kinematic decoupling
allows for fast end-effector motions and recovery that leads to robust visual
servoing. The whole approach and pipeline can be generalized for any mobile
platform (wheeled or tracked vehicles), but is most suitable for dynamically
moving quadruped manipulators thanks to their reactivity against disturbances.
The compliance of the impedance controller makes the robot safer for
interactions with humans and the environment. We demonstrate the performance
and robustness of the proposed approach with various experiments on our 140 kg
HyQReal quadruped robot equipped with a 7-DoF manipulator arm. The experiments
consider dynamic locomotion, tracking under external disturbances, and fast
motions of the target object.Comment: Accepted as contributed paper at 2023 IEEE/RSJ International
Conference on Intelligent Robots and Systems (IROS 2023
Contact Optimization for Non-Prehensile Loco-Manipulation via Hierarchical Model Predictive Control
Recent studies on quadruped robots have focused on either locomotion or
mobile manipulation using a robotic arm. Legged robots can manipulate heavier
and larger objects using non-prehensile manipulation primitives, such as planar
pushing, to drive the object to the desired location. In this paper, we present
a novel hierarchical model predictive control (MPC) for contact optimization of
the manipulation task. Using two cascading MPCs, we split the loco-manipulation
problem into two parts: the first to optimize both contact force and contact
location between the robot and the object, and the second to regulate the
desired interaction force through the robot locomotion. Our method is
successfully validated in both simulation and hardware experiments. While the
baseline locomotion MPC fails to follow the desired trajectory of the object,
our proposed approach can effectively control both object's position and
orientation with minimal tracking error. This capability also allows us to
perform obstacle avoidance for both the robot and the object during the
loco-manipulation task.Comment: 7 pages, 9 figure
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