1,978 research outputs found
Methods of automated detection of travel points when training a collaborative robot
An algorithm has been developed and implemented in this paper, which allows automating the process of forming and controlling scenarios for the movement of a collaborative robot (“Cobot”) through a database of points without specific interfaces, services, and software tools characteristic of each Cobot model. The unification of the developed single graphical interface is achieved by automating the work with Cobot controllers through specialised structured file formats and Robot Operation System (ROS), and by automatically detecting marks as movement points in the image received from the stereo camera using neural network-based models and image processing techniques. Research based on a series of experiments ensured the selection of the most effective image processing method and neural network model in terms of accuracy, speed, resource consumption. The approach formalised in the paper and the graphical interface allowed to implement a classical set of industrial tasks of Cobot motion control
Robustness analysis of Cohen-Grossberg neural network with piecewise constant argument and stochastic disturbances
Robustness of neural networks has been a hot topic in recent years. This paper mainly studies the robustness of the global exponential stability of Cohen-Grossberg neural networks with a piecewise constant argument and stochastic disturbances, and discusses the problem of whether the Cohen-Grossberg neural networks can still maintain global exponential stability under the perturbation of the piecewise constant argument and stochastic disturbances. By using stochastic analysis theory and inequality techniques, the interval length of the piecewise constant argument and the upper bound of the noise intensity are derived by solving transcendental equations. In the end, we offer several examples to illustrate the efficacy of the findings
Probabilistic inverse optimal control with local linearization for non-linear partially observable systems
Inverse optimal control methods can be used to characterize behavior in
sequential decision-making tasks. Most existing work, however, requires the
control signals to be known, or is limited to fully-observable or linear
systems. This paper introduces a probabilistic approach to inverse optimal
control for stochastic non-linear systems with missing control signals and
partial observability that unifies existing approaches. By using an explicit
model of the noise characteristics of the sensory and control systems of the
agent in conjunction with local linearization techniques, we derive an
approximate likelihood for the model parameters, which can be computed within a
single forward pass. We evaluate our proposed method on stochastic and
partially observable version of classic control tasks, a navigation task, and a
manual reaching task. The proposed method has broad applicability, ranging from
imitation learning to sensorimotor neuroscience
RGB-Only Reconstruction of Tabletop Scenes for Collision-Free Manipulator Control
We present a system for collision-free control of a robot manipulator that
uses only RGB views of the world. Perceptual input of a tabletop scene is
provided by multiple images of an RGB camera (without depth) that is either
handheld or mounted on the robot end effector. A NeRF-like process is used to
reconstruct the 3D geometry of the scene, from which the Euclidean full signed
distance function (ESDF) is computed. A model predictive control algorithm is
then used to control the manipulator to reach a desired pose while avoiding
obstacles in the ESDF. We show results on a real dataset collected and
annotated in our lab.Comment: ICRA 2023. Project page at https://ngp-mpc.github.io
Bridging RL Theory and Practice with the Effective Horizon
Deep reinforcement learning (RL) works impressively in some environments and
fails catastrophically in others. Ideally, RL theory should be able to provide
an understanding of why this is, i.e. bounds predictive of practical
performance. Unfortunately, current theory does not quite have this ability. We
compare standard deep RL algorithms to prior sample complexity prior bounds by
introducing a new dataset, BRIDGE. It consists of 155 MDPs from common deep RL
benchmarks, along with their corresponding tabular representations, which
enables us to exactly compute instance-dependent bounds. We find that prior
bounds do not correlate well with when deep RL succeeds vs. fails, but discover
a surprising property that does. When actions with the highest Q-values under
the random policy also have the highest Q-values under the optimal policy, deep
RL tends to succeed; when they don't, deep RL tends to fail. We generalize this
property into a new complexity measure of an MDP that we call the effective
horizon, which roughly corresponds to how many steps of lookahead search are
needed in order to identify the next optimal action when leaf nodes are
evaluated with random rollouts. Using BRIDGE, we show that the effective
horizon-based bounds are more closely reflective of the empirical performance
of PPO and DQN than prior sample complexity bounds across four metrics. We also
show that, unlike existing bounds, the effective horizon can predict the
effects of using reward shaping or a pre-trained exploration policy
Manipulate by Seeing: Creating Manipulation Controllers from Pre-Trained Representations
The field of visual representation learning has seen explosive growth in the
past years, but its benefits in robotics have been surprisingly limited so far.
Prior work uses generic visual representations as a basis to learn
(task-specific) robot action policies (e.g., via behavior cloning). While the
visual representations do accelerate learning, they are primarily used to
encode visual observations. Thus, action information has to be derived purely
from robot data, which is expensive to collect! In this work, we present a
scalable alternative where the visual representations can help directly infer
robot actions. We observe that vision encoders express relationships between
image observations as distances (e.g., via embedding dot product) that could be
used to efficiently plan robot behavior. We operationalize this insight and
develop a simple algorithm for acquiring a distance function and dynamics
predictor, by fine-tuning a pre-trained representation on human collected video
sequences. The final method is able to substantially outperform traditional
robot learning baselines (e.g., 70% success v.s. 50% for behavior cloning on
pick-place) on a suite of diverse real-world manipulation tasks. It can also
generalize to novel objects, without using any robot demonstrations during
train time. For visualizations of the learned policies please check:
https://agi-labs.github.io/manipulate-by-seeing/.Comment: Oral Presentation at the International Conference on Computer Vision
(ICCV), 202
Structured World Models from Human Videos
We tackle the problem of learning complex, general behaviors directly in the
real world. We propose an approach for robots to efficiently learn manipulation
skills using only a handful of real-world interaction trajectories from many
different settings. Inspired by the success of learning from large-scale
datasets in the fields of computer vision and natural language, our belief is
that in order to efficiently learn, a robot must be able to leverage
internet-scale, human video data. Humans interact with the world in many
interesting ways, which can allow a robot to not only build an understanding of
useful actions and affordances but also how these actions affect the world for
manipulation. Our approach builds a structured, human-centric action space
grounded in visual affordances learned from human videos. Further, we train a
world model on human videos and fine-tune on a small amount of robot
interaction data without any task supervision. We show that this approach of
affordance-space world models enables different robots to learn various
manipulation skills in complex settings, in under 30 minutes of interaction.
Videos can be found at https://human-world-model.github.ioComment: RSS 2023. Website at https://human-world-model.github.i
A Bibliographic Study on Artificial Intelligence Research: Global Panorama and Indian Appearance
The present study identifies and assesses the bibliographic trend in
Artificial Intelligence (AI) research for the years 2015-2020 using the science
mapping method of bibliometric study. The required data has been collected from
the Scopus database. To make the collected data analysis-ready, essential data
transformation was performed manually and with the help of a tool viz.
OpenRefine. For determining the trend and performing the mapping techniques,
top five open access and commercial journals of AI have been chosen based on
their citescore driven ranking. The work includes 6880 articles published in
the specified period for analysis. The trend is based on Country-wise
publications, year-wise publications, topical terms in AI, top-cited articles,
prominent authors, major institutions, involvement of industries in AI and
Indian appearance. The results show that compared to open access journals;
commercial journals have a higher citescore and number of articles published
over the years. Additionally, IEEE is the prominent publisher which publishes
84% of the top-cited publications. Further, China and the United States are the
major contributors to literature in the AI domain. The study reveals that
neural networks and deep learning are the major topics included in top AI
research publications. Recently, not only public institutions but also private
bodies are investing their resources in AI research. The study also
investigates the relative position of Indian researchers in terms of AI
research. Present work helps in understanding the initial development, current
stand and future direction of AI.Comment: 21 pages, 9 figures, 6 table
MyoDex: A Generalizable Prior for Dexterous Manipulation
Human dexterity is a hallmark of motor control. Our hands can rapidly
synthesize new behaviors despite the complexity (multi-articular and
multi-joints, with 23 joints controlled by more than 40 muscles) of
musculoskeletal sensory-motor circuits. In this work, we take inspiration from
how human dexterity builds on a diversity of prior experiences, instead of
being acquired through a single task. Motivated by this observation, we set out
to develop agents that can build upon their previous experience to quickly
acquire new (previously unattainable) behaviors. Specifically, our approach
leverages multi-task learning to implicitly capture task-agnostic behavioral
priors (MyoDex) for human-like dexterity, using a physiologically realistic
human hand model - MyoHand. We demonstrate MyoDex's effectiveness in few-shot
generalization as well as positive transfer to a large repertoire of unseen
dexterous manipulation tasks. Agents leveraging MyoDex can solve approximately
3x more tasks, and 4x faster in comparison to a distillation baseline. While
prior work has synthesized single musculoskeletal control behaviors, MyoDex is
the first generalizable manipulation prior that catalyzes the learning of
dexterous physiological control across a large variety of contact-rich
behaviors. We also demonstrate the effectiveness of our paradigms beyond
musculoskeletal control towards the acquisition of dexterity in 24 DoF Adroit
Hand. Website: https://sites.google.com/view/myodexComment: Accepted to the 40th International Conference on Machine Learning
(2023
R^3: On-device Real-Time Deep Reinforcement Learning for Autonomous Robotics
Autonomous robotic systems, like autonomous vehicles and robotic search and
rescue, require efficient on-device training for continuous adaptation of Deep
Reinforcement Learning (DRL) models in dynamic environments. This research is
fundamentally motivated by the need to understand and address the challenges of
on-device real-time DRL, which involves balancing timing and algorithm
performance under memory constraints, as exposed through our extensive
empirical studies. This intricate balance requires co-optimizing two pivotal
parameters of DRL training -- batch size and replay buffer size. Configuring
these parameters significantly affects timing and algorithm performance, while
both (unfortunately) require substantial memory allocation to achieve
near-optimal performance.
This paper presents R^3, a holistic solution for managing timing, memory, and
algorithm performance in on-device real-time DRL training. R^3 employs (i) a
deadline-driven feedback loop with dynamic batch sizing for optimizing timing,
(ii) efficient memory management to reduce memory footprint and allow larger
replay buffer sizes, and (iii) a runtime coordinator guided by heuristic
analysis and a runtime profiler for dynamically adjusting memory resource
reservations. These components collaboratively tackle the trade-offs in
on-device DRL training, improving timing and algorithm performance while
minimizing the risk of out-of-memory (OOM) errors.
We implemented and evaluated R^3 extensively across various DRL frameworks
and benchmarks on three hardware platforms commonly adopted by autonomous
robotic systems. Additionally, we integrate R^3 with a popular realistic
autonomous car simulator to demonstrate its real-world applicability.
Evaluation results show that R^3 achieves efficacy across diverse platforms,
ensuring consistent latency performance and timing predictability with minimal
overhead.Comment: Accepted by RTSS 202
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