1,544 research outputs found
Egocentric Planning for Scalable Embodied Task Achievement
Embodied agents face significant challenges when tasked with performing
actions in diverse environments, particularly in generalizing across object
types and executing suitable actions to accomplish tasks. Furthermore, agents
should exhibit robustness, minimizing the execution of illegal actions. In this
work, we present Egocentric Planning, an innovative approach that combines
symbolic planning and Object-oriented POMDPs to solve tasks in complex
environments, harnessing existing models for visual perception and natural
language processing. We evaluated our approach in ALFRED, a simulated
environment designed for domestic tasks, and demonstrated its high scalability,
achieving an impressive 36.07% unseen success rate in the ALFRED benchmark and
winning the ALFRED challenge at CVPR Embodied AI workshop. Our method requires
reliable perception and the specification or learning of a symbolic description
of the preconditions and effects of the agent's actions, as well as what object
types reveal information about others. It is capable of naturally scaling to
solve new tasks beyond ALFRED, as long as they can be solved using the
available skills. This work offers a solid baseline for studying end-to-end and
hybrid methods that aim to generalize to new tasks, including recent approaches
relying on LLMs, but often struggle to scale to long sequences of actions or
produce robust plans for novel tasks
Human-Machine Collaborative Optimization via Apprenticeship Scheduling
Coordinating agents to complete a set of tasks with intercoupled temporal and
resource constraints is computationally challenging, yet human domain experts
can solve these difficult scheduling problems using paradigms learned through
years of apprenticeship. A process for manually codifying this domain knowledge
within a computational framework is necessary to scale beyond the
``single-expert, single-trainee" apprenticeship model. However, human domain
experts often have difficulty describing their decision-making processes,
causing the codification of this knowledge to become laborious. We propose a
new approach for capturing domain-expert heuristics through a pairwise ranking
formulation. Our approach is model-free and does not require enumerating or
iterating through a large state space. We empirically demonstrate that this
approach accurately learns multifaceted heuristics on a synthetic data set
incorporating job-shop scheduling and vehicle routing problems, as well as on
two real-world data sets consisting of demonstrations of experts solving a
weapon-to-target assignment problem and a hospital resource allocation problem.
We also demonstrate that policies learned from human scheduling demonstration
via apprenticeship learning can substantially improve the efficiency of a
branch-and-bound search for an optimal schedule. We employ this human-machine
collaborative optimization technique on a variant of the weapon-to-target
assignment problem. We demonstrate that this technique generates solutions
substantially superior to those produced by human domain experts at a rate up
to 9.5 times faster than an optimization approach and can be applied to
optimally solve problems twice as complex as those solved by a human
demonstrator.Comment: Portions of this paper were published in the Proceedings of the
International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and
in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper
consists of 50 pages with 11 figures and 4 table
Sense, Think, Grasp: A study on visual and tactile information processing for autonomous manipulation
Interacting with the environment using hands is one of the distinctive
abilities of humans with respect to other species. This aptitude reflects on
the crucial role played by objects\u2019 manipulation in the world that we have
shaped for us. With a view of bringing robots outside industries for supporting
people during everyday life, the ability of manipulating objects
autonomously and in unstructured environments is therefore one of the basic
skills they need. Autonomous manipulation is characterized by great
complexity especially regarding the processing of sensors information to
perceive the surrounding environment. Humans rely on vision for wideranging
tridimensional information, prioprioception for the awareness of
the relative position of their own body in the space and the sense of touch
for local information when physical interaction with objects happens. The
study of autonomous manipulation in robotics aims at transferring similar
perceptive skills to robots so that, combined with state of the art control
techniques, they could be able to achieve similar performance in manipulating
objects. The great complexity of this task makes autonomous
manipulation one of the open problems in robotics that has been drawing
increasingly the research attention in the latest years.
In this work of Thesis, we propose possible solutions to some key components
of autonomous manipulation, focusing in particular on the perception
problem and testing the developed approaches on the humanoid robotic platform iCub. When available, vision is the first source of information
to be processed for inferring how to interact with objects. The object
modeling and grasping pipeline based on superquadric functions we designed
meets this need, since it reconstructs the object 3D model from partial
point cloud and computes a suitable hand pose for grasping the object.
Retrieving objects information with touch sensors only is a relevant skill
that becomes crucial when vision is occluded, as happens for instance during
physical interaction with the object. We addressed this problem with
the design of a novel tactile localization algorithm, named Memory Unscented
Particle Filter, capable of localizing and recognizing objects relying solely
on 3D contact points collected on the object surface. Another key point of
autonomous manipulation we report on in this Thesis work is bi-manual
coordination. The execution of more advanced manipulation tasks in fact
might require the use and coordination of two arms. Tool usage for instance
often requires a proper in-hand object pose that can be obtained via
dual-arm re-grasping. In pick-and-place tasks sometimes the initial and
target position of the object do not belong to the same arm workspace, then
requiring to use one hand for lifting the object and the other for locating it
in the new position. At this regard, we implemented a pipeline for executing
the handover task, i.e. the sequences of actions for autonomously passing an
object from one robot hand on to the other.
The contributions described thus far address specific subproblems of
the more complex task of autonomous manipulation. This actually differs
from what humans do, in that humans develop their manipulation
skills by learning through experience and trial-and-error strategy. Aproper
mathematical formulation for encoding this learning approach is given by
Deep Reinforcement Learning, that has recently proved to be successful in
many robotics applications. For this reason, in this Thesis we report also
on the six month experience carried out at Berkeley Artificial Intelligence
Research laboratory with the goal of studying Deep Reinforcement Learning
and its application to autonomous manipulation
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Intuitive Human-Machine Interfaces for Non-Anthropomorphic Robotic Hands
As robots become more prevalent in our everyday lives, both in our workplaces and in our homes, it becomes increasingly likely that people who are not experts in robotics will be asked to interface with robotic devices. It is therefore important to develop robotic controls that are intuitive and easy for novices to use. Robotic hands, in particular, are very useful, but their high dimensionality makes creating intuitive human-machine interfaces for them complex. In this dissertation, we study the control of non-anthropomorphic robotic hands by non-roboticists in two contexts: collaborative manipulation and assistive robotics.
In the field of collaborative manipulation, the human and the robot work side by side as independent agents. Teleoperation allows the human to assist the robot 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 finding a mapping between a human hand and a non-anthropomorphic robot hand can be difficult, due to the hands’ dissimilar kinematics. In this dissertation, we seek to create a mapping between the human hand and a fully actuated, non-anthropomorphic robot hand that is intuitive enough to enable effective real-time teleoperation, even for novice users.
We propose a low-dimensional and continuous teleoperation subspace which can be used as an intermediary for mapping between different hand pose spaces. We first propose the general concept of the subspace, its properties and the variables needed to map from the human hand to a robot hand. We then propose three ways to populate the teleoperation subspace mapping. Two of our mappings use a dataglove to harvest information about the user's hand. We define the mapping between joint space and teleoperation subspace with an empirical definition, which requires a person to define hand motions in an intuitive, hand-specific way, and with an algorithmic definition, which is kinematically independent, and uses objects to define the subspace. Our third mapping for the teleoperation subspace uses forearm electromyography (EMG) as a control input.
Assistive orthotics is another area of robotics where human-machine interfaces are critical, since, in this field, the robot is attached to the hand of the human user. In this case, the goal is for the robot to assist the human with movements they would not otherwise be able to achieve. Orthotics can improve the quality of life of people who do not have full use of their hands. Human-machine interfaces for assistive hand orthotics that use EMG signals from the affected forearm as input are intuitive and repeated use can strengthen the muscles of the user's affected arm. In this dissertation, we seek to create an EMG based control for an orthotic device used by people who have had a stroke. We would like our control to enable functional motions when used in conjunction with a orthosis and to be robust to changes in the input signal.
We propose a control for a wearable hand orthosis which uses an easy to don, commodity forearm EMG band. We develop an supervised algorithm to detect a user’s intent to open and close their hand, and pair this algorithm with a training protocol which makes our intent detection robust to changes in the input signal. We show that this algorithm, when used in conjunction with an orthosis over several weeks, can improve distal function in users. Additionally, we propose two semi-supervised intent detection algorithms designed to keep our control robust to changes in the input data while reducing the length and frequency of our training protocol
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