1,396 research outputs found
Learning to Represent Haptic Feedback for Partially-Observable Tasks
The sense of touch, being the earliest sensory system to develop in a human
body [1], plays a critical part of our daily interaction with the environment.
In order to successfully complete a task, many manipulation interactions
require incorporating haptic feedback. However, manually designing a feedback
mechanism can be extremely challenging. In this work, we consider manipulation
tasks that need to incorporate tactile sensor feedback in order to modify a
provided nominal plan. To incorporate partial observation, we present a new
framework that models the task as a partially observable Markov decision
process (POMDP) and learns an appropriate representation of haptic feedback
which can serve as the state for a POMDP model. The model, that is parametrized
by deep recurrent neural networks, utilizes variational Bayes methods to
optimize the approximate posterior. Finally, we build on deep Q-learning to be
able to select the optimal action in each state without access to a simulator.
We test our model on a PR2 robot for multiple tasks of turning a knob until it
clicks.Comment: IEEE International Conference on Robotics and Automation (ICRA), 201
A Robotics Framework for Simulation and Control of a Robotic Arm for Use in Higher Education
Robotic arms have been in common use for a several decades now in many areas from manufacturing and industrial uses to hobby projects and amusement park rides. However, there have been very few attempts to make an inexpensive robot arm with a software stack for use in higher education. This paper will outline a control and interfacing software stack built on the Robot Operating System (ROS) and a simulation of the 5 degree of freedom (DoF) robotic arm
Automated sequence and motion planning for robotic spatial extrusion of 3D trusses
While robotic spatial extrusion has demonstrated a new and efficient means to
fabricate 3D truss structures in architectural scale, a major challenge remains
in automatically planning extrusion sequence and robotic motion for trusses
with unconstrained topologies. This paper presents the first attempt in the
field to rigorously formulate the extrusion sequence and motion planning (SAMP)
problem, using a CSP encoding. Furthermore, this research proposes a new
hierarchical planning framework to solve the extrusion SAMP problems that
usually have a long planning horizon and 3D configuration complexity. By
decoupling sequence and motion planning, the planning framework is able to
efficiently solve the extrusion sequence, end-effector poses, joint
configurations, and transition trajectories for spatial trusses with
nonstandard topologies. This paper also presents the first detailed computation
data to reveal the runtime bottleneck on solving SAMP problems, which provides
insight and comparing baseline for future algorithmic development. Together
with the algorithmic results, this paper also presents an open-source and
modularized software implementation called Choreo that is machine-agnostic. To
demonstrate the power of this algorithmic framework, three case studies,
including real fabrication and simulation results, are presented.Comment: 24 pages, 16 figure
Reinforcement Learning Experiments and Benchmark for Solving Robotic Reaching Tasks
Reinforcement learning has shown great promise in robotics thanks to its
ability to develop efficient robotic control procedures through self-training.
In particular, reinforcement learning has been successfully applied to solving
the reaching task with robotic arms. In this paper, we define a robust,
reproducible and systematic experimental procedure to compare the performance
of various model-free algorithms at solving this task. The policies are trained
in simulation and are then transferred to a physical robotic manipulator. It is
shown that augmenting the reward signal with the Hindsight Experience Replay
exploration technique increases the average return of off-policy agents between
7 and 9 folds when the target position is initialised randomly at the beginning
of each episode
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Integration of visual and joint information to enable linear reaching motions
A new dynamics-driven control law was developed for a robot arm, based on the feedback control law which uses the linear transformation directly from work space to joint space. This was validated using a simulation of a two-joint planar robot arm and an optimisation algorithm was used to find the optimum matrix to generate straight trajectories of the end-effector in the work space. We found that this linear matrix can be decomposed into the rotation matrix representing the orientation of the goal direction and the joint relation matrix (MJRM) representing the joint response to errors in the Cartesian work space. The decomposition of the linear matrix indicates the separation of path planning in terms of the direction of the reaching motion and the synergies of joint coordination. Once the MJRM is numerically
obtained, the feedfoward planning of reaching direction allows us to provide asymptotically stable, linear trajectories in the entire work space through rotational transformation, completely avoiding the use of inverse kinematics. Our dynamics-driven control law suggests an interesting framework for interpreting human reaching motion control alternative to the dominant inverse method based explanations, avoiding expensive computation of the inverse kinematics and the point-to-point control along the desired trajectories
Off-Policy Deep Reinforcement Learning Algorithms for Handling Various Robotic Manipulator Tasks
In order to avoid conventional controlling methods which created obstacles
due to the complexity of systems and intense demand on data density, developing
modern and more efficient control methods are required. In this way,
reinforcement learning off-policy and model-free algorithms help to avoid
working with complex models. In terms of speed and accuracy, they become
prominent methods because the algorithms use their past experience to learn the
optimal policies. In this study, three reinforcement learning algorithms; DDPG,
TD3 and SAC have been used to train Fetch robotic manipulator for four
different tasks in MuJoCo simulation environment. All of these algorithms are
off-policy and able to achieve their desired target by optimizing both policy
and value functions. In the current study, the efficiency and the speed of
these three algorithms are analyzed in a controlled environment
A Survey on Causal Reinforcement Learning
While Reinforcement Learning (RL) achieves tremendous success in sequential
decision-making problems of many domains, it still faces key challenges of data
inefficiency and the lack of interpretability. Interestingly, many researchers
have leveraged insights from the causality literature recently, bringing forth
flourishing works to unify the merits of causality and address well the
challenges from RL. As such, it is of great necessity and significance to
collate these Causal Reinforcement Learning (CRL) works, offer a review of CRL
methods, and investigate the potential functionality from causality toward RL.
In particular, we divide existing CRL approaches into two categories according
to whether their causality-based information is given in advance or not. We
further analyze each category in terms of the formalization of different
models, ranging from the Markov Decision Process (MDP), Partially Observed
Markov Decision Process (POMDP), Multi-Arm Bandits (MAB), and Dynamic Treatment
Regime (DTR). Moreover, we summarize the evaluation matrices and open sources
while we discuss emerging applications, along with promising prospects for the
future development of CRL.Comment: 29 pages, 20 figure
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