463 research outputs found
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
Immersive Demonstrations are the Key to Imitation Learning
Achieving successful robotic manipulation is an essential step towards robots
being widely used in industry and home settings. Recently, many learning-based
methods have been proposed to tackle this challenge, with imitation learning
showing great promise. However, imperfect demonstrations and a lack of feedback
from teleoperation systems may lead to poor or even unsafe results. In this
work we explore the effect of demonstrator force feedback on imitation
learning, using a feedback glove and a robot arm to render fingertip-level and
palm-level forces, respectively. 10 participants recorded 5 demonstrations of a
pick-and-place task with 3 grippers, under conditions with no force feedback,
fingertip force feedback, and fingertip and palm force feedback. Results show
that force feedback significantly reduces demonstrator fingertip and palm
forces, leads to a lower variation in demonstrator forces, and recorded
trajectories that a quicker to execute. Using behavioral cloning, we find that
agents trained to imitate these trajectories mirror these benefits, even though
agents have no force data shown to them during training. We conclude that
immersive demonstrations, achieved with force feedback, may be the key to
unlocking safer, quicker to execute dexterous manipulation policies.Comment: This paper is accepted to be presented on IEEE International
Conference on Robotics and Automation (ICRA) 202
Bootstrapping Robotic Skill Learning With Intuitive Teleoperation: Initial Feasibility Study
Robotic skill learning has been increasingly studied but the demonstration
collections are more challenging compared to collecting images/videos in
computer vision and texts in natural language processing. This paper presents a
skill learning paradigm by using intuitive teleoperation devices to generate
high-quality human demonstrations efficiently for robotic skill learning in a
data-driven manner. By using a reliable teleoperation interface, the da Vinci
Research Kit (dVRK) master, a system called dVRK-Simulator-for-Demonstration
(dS4D) is proposed in this paper. Various manipulation tasks show the system's
effectiveness and advantages in efficiency compared to other interfaces. Using
the collected data for policy learning has been investigated, which verifies
the initial feasibility. We believe the proposed paradigm can facilitate robot
learning driven by high-quality demonstrations and efficiency while generating
them.Comment: 10 pages, 4 figures, accepted by ISER202
RoboHive: A Unified Framework for Robot Learning
We present RoboHive, a comprehensive software platform and ecosystem for
research in the field of Robot Learning and Embodied Artificial Intelligence.
Our platform encompasses a diverse range of pre-existing and novel
environments, including dexterous manipulation with the Shadow Hand, whole-arm
manipulation tasks with Franka and Fetch robots, quadruped locomotion, among
others. Included environments are organized within and cover multiple domains
such as hand manipulation, locomotion, multi-task, multi-agent, muscles, etc.
In comparison to prior works, RoboHive offers a streamlined and unified task
interface taking dependency on only a minimal set of well-maintained packages,
features tasks with high physics fidelity and rich visual diversity, and
supports common hardware drivers for real-world deployment. The unified
interface of RoboHive offers a convenient and accessible abstraction for
algorithmic research in imitation, reinforcement, multi-task, and hierarchical
learning. Furthermore, RoboHive includes expert demonstrations and baseline
results for most environments, providing a standard for benchmarking and
comparisons. Details: https://sites.google.com/view/robohiveComment: Accepted at 37th Conference on Neural Information Processing Systems
(NeurIPS 2023) Track on Datasets and Benchmark
GELLO: A General, Low-Cost, and Intuitive Teleoperation Framework for Robot Manipulators
Imitation learning from human demonstrations is a powerful framework to teach
robots new skills. However, the performance of the learned policies is
bottlenecked by the quality, scale, and variety of the demonstration data. In
this paper, we aim to lower the barrier to collecting large and high-quality
human demonstration data by proposing GELLO, a general framework for building
low-cost and intuitive teleoperation systems for robotic manipulation. Given a
target robot arm, we build a GELLO controller that has the same kinematic
structure as the target arm, leveraging 3D-printed parts and off-the-shelf
motors. GELLO is easy to build and intuitive to use. Through an extensive user
study, we show that GELLO enables more reliable and efficient demonstration
collection compared to commonly used teleoperation devices in the imitation
learning literature such as VR controllers and 3D spacemouses. We further
demonstrate the capabilities of GELLO for performing complex bi-manual and
contact-rich manipulation tasks. To make GELLO accessible to everyone, we have
designed and built GELLO systems for 3 commonly used robotic arms: Franka, UR5,
and xArm. All software and hardware are open-sourced and can be found on our
website: https://wuphilipp.github.io/gello/
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