1,110 research outputs found
Dynamic Mobile Manipulation via Whole-Body Bilateral Teleoperation of a Wheeled Humanoid
Humanoid robots have the potential to help human workers by realizing
physically demanding manipulation tasks such as moving large boxes within
warehouses. We define such tasks as Dynamic Mobile Manipulation (DMM). This
paper presents a framework for DMM via whole-body teleoperation, built upon
three key contributions: Firstly, a teleoperation framework employing a Human
Machine Interface (HMI) and a bi-wheeled humanoid, SATYRR, is proposed.
Secondly, the study introduces a dynamic locomotion mapping, utilizing
human-robot reduced order models, and a kinematic retargeting strategy for
manipulation tasks. Additionally, the paper discusses the role of whole-body
haptic feedback for wheeled humanoid control. Finally, the system's
effectiveness and mappings for DMM are validated through locomanipulation
experiments and heavy box pushing tasks. Here we show two forms of DMM:
grasping a target moving at an average speed of 0.4 m/s, and pushing boxes
weighing up to 105\% of the robot's weight. By simultaneously adjusting their
pitch and using their arms, the pilot adjusts the robot pose to apply larger
contact forces and move a heavy box at a constant velocity of 0.2 m/s
Supervised Autonomous Locomotion and Manipulation for Disaster Response with a Centaur-like Robot
Mobile manipulation tasks are one of the key challenges in the field of
search and rescue (SAR) robotics requiring robots with flexible locomotion and
manipulation abilities. Since the tasks are mostly unknown in advance, the
robot has to adapt to a wide variety of terrains and workspaces during a
mission. The centaur-like robot Centauro has a hybrid legged-wheeled base and
an anthropomorphic upper body to carry out complex tasks in environments too
dangerous for humans. Due to its high number of degrees of freedom, controlling
the robot with direct teleoperation approaches is challenging and exhausting.
Supervised autonomy approaches are promising to increase quality and speed of
control while keeping the flexibility to solve unknown tasks. We developed a
set of operator assistance functionalities with different levels of autonomy to
control the robot for challenging locomotion and manipulation tasks. The
integrated system was evaluated in disaster response scenarios and showed
promising performance.Comment: In Proceedings of IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS), Madrid, Spain, October 201
Deep Imitation Learning for Humanoid Loco-manipulation through Human Teleoperation
We tackle the problem of developing humanoid loco-manipulation skills with
deep imitation learning. The difficulty of collecting task demonstrations and
training policies for humanoids with a high degree of freedom presents
substantial challenges. We introduce TRILL, a data-efficient framework for
training humanoid loco-manipulation policies from human demonstrations. In this
framework, we collect human demonstration data through an intuitive Virtual
Reality (VR) interface. We employ the whole-body control formulation to
transform task-space commands by human operators into the robot's joint-torque
actuation while stabilizing its dynamics. By employing high-level action
abstractions tailored for humanoid loco-manipulation, our method can
efficiently learn complex sensorimotor skills. We demonstrate the effectiveness
of TRILL in simulation and on a real-world robot for performing various
loco-manipulation tasks. Videos and additional materials can be found on the
project page: https://ut-austin-rpl.github.io/TRILL.Comment: Submitted to Humanoids 202
Development of a Whole-body Work Imitation Learning System by a Biped and Bi-armed Humanoid
Imitation learning has been actively studied in recent years. In particular,
skill acquisition by a robot with a fixed body, whose root link position and
posture and camera angle of view do not change, has been realized in many
cases. On the other hand, imitation of the behavior of robots with floating
links, such as humanoid robots, is still a difficult task. In this study, we
develop an imitation learning system using a biped robot with a floating link.
There are two main problems in developing such a system. The first is a
teleoperation device for humanoids, and the second is a control system that can
withstand heavy workloads and long-term data collection. For the first point,
we use the whole body control device TABLIS. It can control not only the arms
but also the legs and can perform bilateral control with the robot. By
connecting this TABLIS with the high-power humanoid robot JAXON, we construct a
control system for imitation learning. For the second point, we will build a
system that can collect long-term data based on posture optimization, and can
simultaneously move the robot's limbs. We combine high-cycle posture generation
with posture optimization methods, including whole-body joint torque
minimization and contact force optimization. We designed an integrated system
with the above two features to achieve various tasks through imitation
learning. Finally, we demonstrate the effectiveness of this system by
experiments of manipulating flexible fabrics such that not only the hands but
also the head and waist move simultaneously, manipulating objects using legs
characteristic of humanoids, and lifting heavy objects that require large
forces.Comment: accepted at IROS202
Multicontact Motion Retargeting Using Whole-Body Optimization of Full Kinematics and Sequential Force Equilibrium
This article presents a multicontact motion adaptation framework that enables teleoperation of high degree-of-freedom robots, such as quadrupeds and humanoids, for loco-manipulation tasks in multicontact settings. Our proposed algorithms optimize whole-body configurations and formulate the retargeting of multicontact motions as sequential quadratic programming, which is robust and stable near the edges of feasibility constraints. Our framework allows real-time operation of the robot and reduces cognitive load for the operator because infeasible commands are automatically adapted into physically stable and viable motions on the robot. The results in simulations with full dynamics demonstrated the effectiveness of teleoperating different legged robots interactively and generating rich multicontact movements. We evaluated the computational efficiency of the proposed algorithms, and further validated and analyzed multicontact loco-manipulation tasks on humanoid and quadruped robots by reaching, active pushing, and various traversal on uneven terrains
Teaching humanoid robotics by means of human teleoperation through RGB-D sensors
This paper presents a graduate course project on humanoid robotics offered by the University of Padova. The target is to safely lift an object by teleoperating a small humanoid. Students have to map human limbs into robot joints, guarantee the robot stability during the motion, and teleoperate the robot to perform the correct movement. We introduce the following innovative aspects with respect to classical robotic classes: i) the use of humanoid robots as teaching tools; ii) the simplification of the stable locomotion problem by exploiting the potential of teleoperation; iii) the adoption of a Project-Based Learning constructivist approach as teaching methodology. The learning objectives of both course and project are introduced and compared with the students\u2019 background. Design and constraints students have to deal with are reported, together with the amount of time they and their instructors dedicated to solve tasks. A set of evaluation results are provided in order to validate the authors\u2019 purpose, including the students\u2019 personal feedback. A discussion about possible future improvements is reported, hoping to encourage further spread of educational robotics in schools at all levels
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