720 research outputs found
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
Deploying the NASA Valkyrie Humanoid for IED Response: An Initial Approach and Evaluation Summary
As part of a feasibility study, this paper shows the NASA Valkyrie humanoid
robot performing an end-to-end improvised explosive device (IED) response task.
To demonstrate and evaluate robot capabilities, sub-tasks highlight different
locomotion, manipulation, and perception requirements: traversing uneven
terrain, passing through a narrow passageway, opening a car door, retrieving a
suspected IED, and securing the IED in a total containment vessel (TCV). For
each sub-task, a description of the technical approach and the hidden
challenges that were overcome during development are presented. The discussion
of results, which explicitly includes existing limitations, is aimed at
motivating continued research and development to enable practical deployment of
humanoid robots for IED response. For instance, the data shows that operator
pauses contribute to 50\% of the total completion time, which implies that
further work is needed on user interfaces for increasing task completion
efficiency.Comment: 2019 IEEE-RAS International Conference on Humanoid Robot
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
A behavior-based framework for safe deployment of humanoid robots
We present a complete framework for the safe deployment of humanoid robots in environments containing humans. Proceeding from some general guidelines, we propose several safety behaviors, classified in three categories, i.e., override, temporary override, and proactive. Activation and deactivation of these behaviors is triggered by information coming from the robot sensors and is handled by a state machine. The implementation of our safety framework is discussed with respect to a reference control architecture. In particular, it is shown that an MPC-based gait generator is ideal for realizing all behaviors related to locomotion. Simulation and experimental results on the HRP-4 and NAO humanoids, respectively, are presented to confirm the effectiveness of the proposed method
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
A framework for safe human-humanoid coexistence
This work is focused on the development of a safety framework for Human-Humanoid coexistence, with emphasis on humanoid locomotion. After a brief introduction to the fundamental concepts of humanoid locomotion, the two most common approaches for gait generation are presented, and are extended with the inclusion of a stability condition to guarantee the boundedness of the generated trajectories. Then the safety framework is presented, with the introduction of different safety behaviors. These behaviors are meant to enhance the overall level of safety during any robot operation. Proactive behaviors will enhance or adapt the current robot operations to reduce the risk of danger, while override behaviors will stop the current robot activity in order to take action against a particularly dangerous situation. A state
machine is defined to control the transitions between the behaviors. The behaviors that are strictly related to locomotion are subsequently detailed, and an implementation is proposed and validated. A possible implementation of the remaining behaviors is proposed through the review of related works that can be found in literature
Analyzing Whole-Body Pose Transitions in Multi-Contact Motions
Abstract-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 largescale dataset of loco-manipulation motions involving multicontact 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 wholebody 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
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