17 research outputs found
Multi-modal Skill Memories for Online Learning of Interactive Robot Movement Generation
Queißer J. Multi-modal Skill Memories for Online Learning of Interactive Robot Movement Generation. Bielefeld: Universität Bielefeld; 2018.Modern robotic applications pose complex requirements with respect to the adaptation of
actions regarding the variability in a given task. Reinforcement learning can optimize for
changing conditions, but relearning from scratch is hardly feasible due to the high number of
required rollouts. This work proposes a parameterized skill that generalizes to new actions
for changing task parameters. The actions are encoded by a meta-learner that provides
parameters for task-specific dynamic motion primitives. Experimental evaluation shows that
the utilization of parameterized skills for initialization of the optimization process leads to a
more effective incremental task learning. A proposed hybrid optimization method combines
a fast coarse optimization on a manifold of policy parameters with a fine-grained parameter
search in the unrestricted space of actions. It is shown that the developed algorithm reduces
the number of required rollouts for adaptation to new task conditions. Further, this work
presents a transfer learning approach for adaptation of learned skills to new situations.
Application in illustrative toy scenarios, for a 10-DOF planar arm, a humanoid robot point
reaching task and parameterized drumming on a pneumatic robot validate the approach.
But parameterized skills that are applied on complex robotic systems pose further
challenges: the dynamics of the robot and the interaction with the environment introduce
model inaccuracies. In particular, high-level skill acquisition on highly compliant robotic
systems such as pneumatically driven or soft actuators is hardly feasible. Since learning of
the complete dynamics model is not feasible due to the high complexity, this thesis examines
two alternative approaches: First, an improvement of the low-level control based on an
equilibrium model of the robot. Utilization of an equilibrium model reduces the learning
complexity and this thesis evaluates its applicability for control of pneumatic and industrial
light-weight robots. Second, an extension of parameterized skills to generalize for forward
signals of action primitives that result in an enhanced control quality of complex robotic
systems. This thesis argues for a shift in the complexity of learning the full dynamics of the
robot to a lower dimensional task-related learning problem. Due to the generalization in
relation to the task variability, online learning for complex robots as well as complex scenarios
becomes feasible. An experimental evaluation investigates the generalization capabilities of
the proposed online learning system for robot motion generation. Evaluation is performed
through simulation of a compliant 2-DOF arm and scalability to a complex robotic system
is demonstrated for a pneumatically driven humanoid robot with 8-DOF
Bootstrapping of parameterized skills through hybrid optimization in task and policy spaces
Queißer J, Steil JJ. Bootstrapping of parameterized skills through hybrid optimization in task and policy spaces. Frontiers in Robotics and AI. 2018;5:49.Modern robotic applications create high demands on adaptation of actions with respect to
variance in a given task. Reinforcement learning is able to optimize for these changing conditions,
but relearning from scratch is hardly feasible due to the high number of required rollouts. We
propose a parameterized skill that generalizes to new actions for changing task parameters,
which is encoded as a meta-learner that provides parameters for task-specific dynamic motion
primitives. Our work shows that utilizing parameterized skills for initialization of the optimization
process leads to a more effective incremental task learning. In addition, we introduce a hybrid
optimization method that combines a fast coarse optimization on a manifold of policy parameters
with a fine grained parameter search in the unrestricted space of actions. The proposed algorithm
reduces the number of required rollouts for adaptation to new task conditions. Application in
illustrative toy scenarios, for a 10-DOF planar arm, and a humanoid robot point reaching task
validate the approach
Learning the end-effector pose from demonstration for the Bionic Handling Assistant robot
Malekzadeh M, Queißer J, Steil JJ. Learning the end-effector pose from demonstration for the Bionic Handling Assistant robot. Presented at the 9th Int. Workshop on Human-Friendly Robotics, Genoa
Incremental Bootstrapping of Parameterized Motor Skills
Queißer J, Reinhart F, Steil JJ. Incremental Bootstrapping of Parameterized Motor Skills. In: Proc. IEEE Humanoids. IEEE; 2016.Many motor skills have an intrinsic, low-dimensional parameterization,
e.g. reaching through a grid to different targets. Repeated policy search
for new parameterizations of such a skill is inefficient, because the structure
of the skill variability is not exploited.
This issue has been previously addressed by learning mappings from task
parameters to policy parameters. In this work, we introduce a bootstrapping
technique that establishes such parameterized skills incrementally.
The approach combines iterative learning with state-of-the-art
black-box policy optimization. We investigate the benefits of
incrementally learning parameterized skills for efficient policy
retrieval and show that the number of required rollouts can be
significantly reduced when optimizing policies for novel tasks.
The approach is demonstrated for several parameterized motor
tasks including upper-body reaching motion generation for the
humanoid robot COMAN
Adaptive Handling Assistance for Industrial Lightweight Robots in Simulation
Balayn A, Queißer J, Wojtynek M, Wrede S. Adaptive Handling Assistance for Industrial Lightweight Robots in Simulation. In: 2016 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR). 2016.With the growth of lightweight robots in industry handling assistance becomes more and more important for solving industrial tasks.
We present an adaptive compliance control mode for industrial robots based on a learned equilibrium model.
This enables us to cope with changing manufacturing environments, attached grippers as well as devices with inaccurate dynamic models, e.g. stiff tubes, wires, protection shields or hose packages.
A further feature of the proposed method is the expandability by an additional parameterization that allows to deal with task variability.
In this work we evaluate our approach using the example of a task that incorporates variable payloads. All experiments are conducted in a simulation framework to evaluate the feasibility of the proposed approach for industrial robot scenarios
Transfer Learning of Complex Motor Skills on the Humanoid Robot Affetto
Schulz A, Queißer J, Ishihara H, Asada M. Transfer Learning of Complex Motor Skills on the Humanoid Robot Affetto. Presented at the International Conference on Development and Learning and on Epigenetic Robotics 2018 (ICDL-EPIROB2018), Tokyo (In Press)
Imitation learning for a continuum trunk robot
Malekzadeh M, Queißer J, Steil JJ. Imitation learning for a continuum trunk robot. In: Verleysen M, ed. Proceedings of the 25. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. ESANN 2017. Louvain-la-Neuve: Ciaco; 2017
Emergence of Content-Agnostic Information Processing by a Robot Using Active Inference, Visual Attention, Working Memory, and Planning
Generalization by learning is an essential cognitive competency for humans. For example, we can manipulate even unfamiliar objects and can generate mental images before enacting a preplan. How is this possible? Our study investigated this problem by revisiting our previous study (Jung, Matsumoto, & Tani, 2019), which examined the problem of vision-based, goal-directed planning by robots performing a task of block stacking. By extending the previous study, our work introduces a large network comprising dynamically interacting submodules, including visual working memory (VWMs), a visual attention module, and an executive network. The executive network predicts motor signals, visual images, and various controls for attention, as well as masking of visual information. The most significant difference from the previous study is that our current model contains an additional VWM. The entire network is trained by using predictive coding and an optimal visuomotor plan to achieve a given goal state is inferred using active inference. Results indicate that our current model performs significantly better than that used in Jung et al. (2019), especially when manipulating blocks with unlearned colors and textures. Simulation results revealed that the observed generalization was achieved because content-agnostic information processing developed through synergistic interaction between the second VWM and other modules during the course of learning, in which memorizing image contents and transforming them are dissociated. This letter verifies this claim by conducting both qualitative and quantitative analysis of simulation results
Skill Memories for Parameterized Dynamic Action Primitives on the Pneumatically Driven Humanoid Robot Child Affetto
Queißer J, Ishihara H, Hammer B, Steil JJ, Asada M. Skill Memories for Parameterized Dynamic Action Primitives on the Pneumatically Driven Humanoid Robot Child Affetto. Presented at the International Conference on Development and Learning and on Epigenetic Robotics 2018 (ICDL-EPIROB2018), Tokyo
An Active Compliant Control Mode for Interaction with a Pneumatic Soft Robot
Queißer J, Neumann K, Rolf M, Reinhart F, Steil JJ. An Active Compliant Control Mode for Interaction with a Pneumatic Soft Robot. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014). IEEE; 2014: 573-579.Bionic soft robots offer exciting perspectives for
more flexible and safe physical interaction with the world and
humans. Unfortunately, their hardware design often prevents
analytical modeling, which in turn is a prerequisite to apply
classical automatic control approaches. On the other hand,
also modeling by means of learning is hardly feasible due to
many degrees of freedom, high-dimensional state spaces and the
softness properties like e.g. mechanical elasticity, which cause
limited repeatability and complex dynamics. Nevertheless, the
realization of basic control modes is important to leverage the
potential of soft robots for applications. We therefore propose
a hybrid approach combining classical and learning elements
for the realization of an interactive control mode for an elastic
bionic robot. It superimposes a low-gain feedback control with a
feed-forward control based on a learned simplified model of the
inverse dynamics which considers only equilibria of the robot’s
dynamics. We demonstrate on the Bionic Handling Assistant
how a respective inverse equilibrium model can be learned and
effectively exploited for quick and agile control. In a second
step, the control scheme is extended to an active compliant
control mode. It implements a kind of gravitation compensation
to allow for kinesthetic teaching of the robot based on the
implicit knowledge of gravitational and mechanical forces that
are encoded in the learned equilibrium model.We finally discuss
that this control scheme may be implemented also on other
soft robots to provide the avenue towards their applications in
general manipulation tasks