1,069 research outputs found
Intuitive Hand Teleoperation by Novice Operators Using a Continuous Teleoperation Subspace
Human-in-the-loop manipulation is useful in when autonomous grasping is not
able to deal sufficiently well with corner cases or cannot operate fast enough.
Using the teleoperator's hand as an input device can provide an intuitive
control method but requires mapping between pose spaces which may not be
similar. We propose a low-dimensional and continuous teleoperation subspace
which can be used as an intermediary for mapping between different hand pose
spaces. We present an algorithm to project between pose space and teleoperation
subspace. We use a non-anthropomorphic robot to experimentally prove that it is
possible for teleoperation subspaces to effectively and intuitively enable
teleoperation. In experiments, novice users completed pick and place tasks
significantly faster using teleoperation subspace mapping than they did using
state of the art teleoperation methods.Comment: ICRA 2018, 7 pages, 7 figures, 2 table
Exploiting Prior Knowledge in Robot Motion Skills Learning
This thesis presents a new robot learning framework, its application to exploit prior knowledge by encoding movement primitives in the form of a novel motion library, and the transfer of such knowledge to other robotic platforms in the form of shared latent spaces.
In robot learning, it is often desirable to have robots that learn and acquire new skills rapidly. However, existing methods are specific to a certain task defined by the user, as well as time consuming to train. This includes for instance end-to-end models that can require a substantial amount of time to learn a certain skill. Such methods often start with no prior knowledge or little, and move slowly from erratic movements to the specific required motion. This is very different from how animals and humans learn motion skills. For instance, zebras in the African Savannah can learn to walk in few minutes just after being born. This suggests that some kind of prior knowledge is encoded into them. Leveraging this information may help improve and accelerate the learning and generation of new skills. These observations raise questions such as: how would this prior knowledge be represented? And how much would it help the learning process? Additionally, once learned, these models often do not transfer well to other robotic platforms requiring to teach to each other robot the same skills. This significantly increases the total training time and render the demonstration phase a tedious process. Would it be possible instead to exploit this prior knowledge to accelerate the learning process of new skills by transferring it to other robots? These are some of the questions that we are interested to investigate in this thesis. However, before examining these questions, a practical tool that allows one to easily test ideas in robot learning is needed. This tool would have to be easy-to-use, intuitive, generic, modular, and would need to let the user easily implement different ideas and compare different models/algorithms. Once implemented, we would then be able to focus on our original questions
Self-Supervised Motion Retargeting with Safety Guarantee
In this paper, we present self-supervised shared latent embedding (S3LE), a
data-driven motion retargeting method that enables the generation of natural
motions in humanoid robots from motion capture data or RGB videos. While it
requires paired data consisting of human poses and their corresponding robot
configurations, it significantly alleviates the necessity of time-consuming
data-collection via novel paired data generating processes. Our self-supervised
learning procedure consists of two steps: automatically generating paired data
to bootstrap the motion retargeting, and learning a projection-invariant
mapping to handle the different expressivity of humans and humanoid robots.
Furthermore, our method guarantees that the generated robot pose is
collision-free and satisfies position limits by utilizing nonparametric
regression in the shared latent space. We demonstrate that our method can
generate expressive robotic motions from both the CMU motion capture database
and YouTube videos
Indirect Methods for Robot Skill Learning
Robot learning algorithms are appealing alternatives for acquiring rational robotic behaviors from data collected during the execution of tasks. Furthermore, most robot learning techniques are stated as isolated stages and focused on directly obtaining rational policies as a result of optimizing only performance measures of single tasks. However, formulating robotic skill acquisition processes in such a way have some disadvantages. For example, if the same skill has to be learned by different robots, independent learning processes should be carried out for acquiring exclusive policies for each robot. Similarly, if a robot has to learn diverse skills, the robot should acquire the policy for each task in separate learning processes, in a sequential order and commonly starting from scratch. In the same way, formulating the learning process in terms of only the performance measure, makes robots to unintentionally avoid situations that should not be repeated, but without any mechanism that captures the necessity of not repeating those wrong behaviors. In contrast, humans and other animals exploit their experience not only for improving the performance of the task they are currently executing, but for constructing indirectly multiple models to help them with that particular task and to generalize to new problems. Accordingly, the models and algorithms proposed in this thesis seek to be more data efficient and extract more information from the interaction data that is collected either from expert\u2019s demonstrations or the robot\u2019s own experience. The first approach encodes robotic skills with shared latent variable models, obtaining latent representations that can be transferred from one robot to others, therefore avoiding to learn the same task from scratch. The second approach learns complex rational policies by representing them as hierarchical models that can perform multiple concurrent tasks, and whose components are learned in the same learning process, instead of separate processes. Finally, the third approach uses the interaction data for learning two alternative and antagonistic policies that capture what to and not to do, and which influence the learning process in addition to the performance measure defined for the task
Unsupervised human-to-robot motion retargeting via expressive latent space
This paper introduces a novel approach for human-to-robot motion retargeting,
enabling robots to mimic human motion with precision while preserving the
semantics of the motion. For that, we propose a deep learning method for direct
translation from human to robot motion. Our method does not require annotated
paired human-to-robot motion data, which reduces the effort when adopting new
robots. To this end, we first propose a cross-domain similarity metric to
compare the poses from different domains (i.e., human and robot). Then, our
method achieves the construction of a shared latent space via contrastive
learning and decodes latent representations to robot motion control commands.
The learned latent space exhibits expressiveness as it captures the motions
precisely and allows direct motion control in the latent space. We showcase how
to generate in-between motion through simple linear interpolation in the latent
space between two projected human poses. Additionally, we conducted a
comprehensive evaluation of robot control using diverse modality inputs, such
as texts, RGB videos, and key-poses, which enhances the ease of robot control
to users of all backgrounds. Finally, we compare our model with existing works
and quantitatively and qualitatively demonstrate the effectiveness of our
approach, enhancing natural human-robot communication and fostering trust in
integrating robots into daily life
ACE: Adversarial Correspondence Embedding for Cross Morphology Motion Retargeting from Human to Nonhuman Characters
Motion retargeting is a promising approach for generating natural and
compelling animations for nonhuman characters. However, it is challenging to
translate human movements into semantically equivalent motions for target
characters with different morphologies due to the ambiguous nature of the
problem. This work presents a novel learning-based motion retargeting
framework, Adversarial Correspondence Embedding (ACE), to retarget human
motions onto target characters with different body dimensions and structures.
Our framework is designed to produce natural and feasible robot motions by
leveraging generative-adversarial networks (GANs) while preserving high-level
motion semantics by introducing an additional feature loss. In addition, we
pretrain a robot motion prior that can be controlled in a latent embedding
space and seek to establish a compact correspondence. We demonstrate that the
proposed framework can produce retargeted motions for three different
characters -- a quadrupedal robot with a manipulator, a crab character, and a
wheeled manipulator. We further validate the design choices of our framework by
conducting baseline comparisons and a user study. We also showcase sim-to-real
transfer of the retargeted motions by transferring them to a real Spot robot
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