827 research outputs found
Motion Imitation Based on Sparsely Sampled Correspondence
Existing techniques for motion imitation often suffer a certain level of
latency due to their computational overhead or a large set of correspondence
samples to search. To achieve real-time imitation with small latency, we
present a framework in this paper to reconstruct motion on humanoids based on
sparsely sampled correspondence. The imitation problem is formulated as finding
the projection of a point from the configuration space of a human's poses into
the configuration space of a humanoid. An optimal projection is defined as the
one that minimizes a back-projected deviation among a group of candidates,
which can be determined in a very efficient way. Benefited from this
formulation, effective projections can be obtained by using sparse
correspondence. Methods for generating these sparse correspondence samples have
also been introduced. Our method is evaluated by applying the human's motion
captured by a RGB-D sensor to a humanoid in real-time. Continuous motion can be
realized and used in the example application of tele-operation.Comment: 8 pages, 8 figures, technical repor
Analyzing Whole-Body Pose Transitions in Multi-Contact Motions
When executing whole-body motions, humans are able to use a large variety of
support poses which not only utilize the feet, but also hands, knees and elbows
to enhance stability. While there are many works analyzing the transitions
involved in walking, very few works analyze human motion where more complex
supports occur.
In this work, we analyze complex support pose transitions in human motion
involving locomotion and manipulation tasks (loco-manipulation). We have
applied a method for the detection of human support contacts from motion
capture data to a large-scale dataset of loco-manipulation motions involving
multi-contact supports, providing a semantic representation of them. Our
results provide a statistical analysis of the used support poses, their
transitions and the time spent in each of them. In addition, our data partially
validates our taxonomy of whole-body support poses presented in our previous
work.
We believe that this work extends our understanding of human motion for
humanoids, with a long-term objective of developing methods for autonomous
multi-contact motion planning.Comment: 8 pages, IEEE-RAS International Conference on Humanoid Robots
(Humanoids) 201
A Whole-Body Pose Taxonomy for Loco-Manipulation Tasks
Exploiting interaction with the environment is a promising and powerful way
to enhance stability of humanoid robots and robustness while executing
locomotion and manipulation tasks. Recently some works have started to show
advances in this direction considering humanoid locomotion with multi-contacts,
but to be able to fully develop such abilities in a more autonomous way, we
need to first understand and classify the variety of possible poses a humanoid
robot can achieve to balance. To this end, we propose the adaptation of a
successful idea widely used in the field of robot grasping to the field of
humanoid balance with multi-contacts: a whole-body pose taxonomy classifying
the set of whole-body robot configurations that use the environment to enhance
stability. We have revised criteria of classification used to develop grasping
taxonomies, focusing on structuring and simplifying the large number of
possible poses the human body can adopt. We propose a taxonomy with 46 poses,
containing three main categories, considering number and type of supports as
well as possible transitions between poses. The taxonomy induces a
classification of motion primitives based on the pose used for support, and a
set of rules to store and generate new motions. We present preliminary results
that apply known segmentation techniques to motion data from the KIT whole-body
motion database. Using motion capture data with multi-contacts, we can identify
support poses providing a segmentation that can distinguish between locomotion
and manipulation parts of an action.Comment: 8 pages, 7 figures, 1 table with full page figure that appears in
landscape page, 2015 IEEE/RSJ International Conference on Intelligent Robots
and System
HumanMimic: Learning Natural Locomotion and Transitions for Humanoid Robot via Wasserstein Adversarial Imitation
Transferring human motion skills to humanoid robots remains a significant
challenge. In this study, we introduce a Wasserstein adversarial imitation
learning system, allowing humanoid robots to replicate natural whole-body
locomotion patterns and execute seamless transitions by mimicking human
motions. First, we present a unified primitive-skeleton motion retargeting to
mitigate morphological differences between arbitrary human demonstrators and
humanoid robots. An adversarial critic component is integrated with
Reinforcement Learning (RL) to guide the control policy to produce behaviors
aligned with the data distribution of mixed reference motions. Additionally, we
employ a specific Integral Probabilistic Metric (IPM), namely the Wasserstein-1
distance with a novel soft boundary constraint to stabilize the training
process and prevent model collapse. Our system is evaluated on a full-sized
humanoid JAXON in the simulator. The resulting control policy demonstrates a
wide range of locomotion patterns, including standing, push-recovery, squat
walking, human-like straight-leg walking, and dynamic running. Notably, even in
the absence of transition motions in the demonstration dataset, robots showcase
an emerging ability to transit naturally between distinct locomotion patterns
as desired speed changes
Imitating human motion using humanoid upper body models
Includes abstract.Includes bibliographical references.This thesis investigates human motion imitation of five different humanoid upper bodies (comprised of the torso and upper limbs) using human dance motion as a case study. The humanoid models are based on five existing humanoids, namely, ARMAR, HRP-2, SURALP, WABIAN-2, and WE-4RII. These humanoids are chosen for their different structures and range of joint motion
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
Learning Task Priorities from Demonstrations
Bimanual operations in humanoids offer the possibility to carry out more than
one manipulation task at the same time, which in turn introduces the problem of
task prioritization. We address this problem from a learning from demonstration
perspective, by extending the Task-Parameterized Gaussian Mixture Model
(TP-GMM) to Jacobian and null space structures. The proposed approach is tested
on bimanual skills but can be applied in any scenario where the prioritization
between potentially conflicting tasks needs to be learned. We evaluate the
proposed framework in: two different tasks with humanoids requiring the
learning of priorities and a loco-manipulation scenario, showing that the
approach can be exploited to learn the prioritization of multiple tasks in
parallel.Comment: Accepted for publication at the IEEE Transactions on Robotic
Skeleton2Humanoid: Animating Simulated Characters for Physically-plausible Motion In-betweening
Human motion synthesis is a long-standing problem with various applications
in digital twins and the Metaverse. However, modern deep learning based motion
synthesis approaches barely consider the physical plausibility of synthesized
motions and consequently they usually produce unrealistic human motions. In
order to solve this problem, we propose a system ``Skeleton2Humanoid'' which
performs physics-oriented motion correction at test time by regularizing
synthesized skeleton motions in a physics simulator. Concretely, our system
consists of three sequential stages: (I) test time motion synthesis network
adaptation, (II) skeleton to humanoid matching and (III) motion imitation based
on reinforcement learning (RL). Stage I introduces a test time adaptation
strategy, which improves the physical plausibility of synthesized human
skeleton motions by optimizing skeleton joint locations. Stage II performs an
analytical inverse kinematics strategy, which converts the optimized human
skeleton motions to humanoid robot motions in a physics simulator, then the
converted humanoid robot motions can be served as reference motions for the RL
policy to imitate. Stage III introduces a curriculum residual force control
policy, which drives the humanoid robot to mimic complex converted reference
motions in accordance with the physical law. We verify our system on a typical
human motion synthesis task, motion-in-betweening. Experiments on the
challenging LaFAN1 dataset show our system can outperform prior methods
significantly in terms of both physical plausibility and accuracy. Code will be
released for research purposes at:
https://github.com/michaelliyunhao/Skeleton2HumanoidComment: Accepted by ACMMM202
Scaled Autonomy for Networked Humanoids
Humanoid robots have been developed with the intention of aiding in environments designed for humans. As such, the control of humanoid morphology and effectiveness of human robot interaction form the two principal research issues for deploying these robots in the real world. In this thesis work, the issue of humanoid control is coupled with human robot interaction under the framework of scaled autonomy, where the human and robot exchange levels of control depending on the environment and task at hand. This scaled autonomy is approached with control algorithms for reactive stabilization of human commands and planned trajectories that encode semantically meaningful motion preferences in a sequential convex optimization framework.
The control and planning algorithms have been extensively tested in the field for robustness and system verification. The RoboCup competition provides a benchmark competition for autonomous agents that are trained with a human supervisor. The kid-sized and adult-sized humanoid robots coordinate over a noisy network in a known environment with adversarial opponents, and the software and routines in this work allowed for five consecutive championships. Furthermore, the motion planning and user interfaces developed in the work have been tested in the noisy network of the DARPA Robotics Challenge (DRC) Trials and Finals in an unknown environment.
Overall, the ability to extend simplified locomotion models to aid in semi-autonomous manipulation allows untrained humans to operate complex, high dimensional robots. This represents another step in the path to deploying humanoids in the real world, based on the low dimensional motion abstractions and proven performance in real world tasks like RoboCup and the DRC
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