28 research outputs found
Modeling and Learning of Complex Motor Tasks: A Case Study with Robot Table Tennis
Most tasks that humans need to accomplished in their everyday life require certain motor skills. Although most motor skills seem to rely on the same elementary movements, humans are able to accomplish
many different tasks. Robots, on the other hand, are still limited to a small number of skills and depend on well-defined environments. Modeling new motor behaviors is therefore an important research area
in robotics. Computational models of human motor control are an essential step to construct robotic systems that are able to solve complex tasks in a human inhabited environment. These models can be
the key for robust, efficient, and human-like movement plans. In turn, the reproduction of human-like behavior on a robotic system can be also beneficial for computational neuroscientists to verify their
hypotheses. Although biomimetic models can be of great help in order to close the gap between human and robot motor abilities, these models are usually limited to the scenarios considered. However, one
important property of human motor behavior is the ability to adapt skills to new situations and to learn new motor skills with relatively few trials. Domain-appropriate machine learning techniques, such as supervised and reinforcement learning, have a great potential to enable robotic systems to autonomously
learn motor skills. In this thesis, we attempt to model and subsequently learn a complex motor task. As a test case
for a complex motor task, we chose robot table tennis throughout this thesis. Table tennis requires a series of time critical movements which have to be selected and adapted according to environmental
stimuli as well as the desired targets. We first analyze how humans play table tennis and create a computational model that results in human-like hitting motions on a robot arm. Our focus lies on
generating motor behavior capable of adapting to variations and uncertainties in the environmental conditions. We evaluate the resulting biomimetic model both in a physically realistic simulation and on a real anthropomorphic seven degrees of freedom Barrett WAM robot arm. This biomimetic model based purely on analytical methods produces successful hitting motions, but does not feature the flexibility found in human motor behavior. We therefore suggest a new framework that allows a robot to learn cooperative table tennis from and with a human. Here, the robot first learns a set of elementary hitting movements from a human teacher by kinesthetic teach-in, which is compiled into a set of motor primitives. To generalize these movements to a wider range of situations we introduce the mixture of motor primitives algorithm. The resulting motor policy enables the robot to select appropriate motor primitives as well as to generalize between them. Furthermore, it also allows to adapt the selection process of the hitting movements based on the outcome of previous trials. The framework is evaluated both in simulation and on a real Barrett WAM robot. In consecutive experiments, we show that our approach allows the robot to return balls from a ball launcher and furthermore to play table tennis with a human partner.
Executing robot movements using a biomimetic or learned approach enables the robot to return balls successfully. However, in motor tasks with a competitive goal such as table tennis, the robot not
only needs to return the balls successfully in order to accomplish the task, it also needs an adaptive strategy. Such a higher-level strategy cannot be programed manually as it depends on the opponent and the abilities of the robot. We therefore make a first step towards the goal of acquiring such a strategy and investigate the possibility of inferring strategic information from observing humans playing table tennis. We model table tennis as a Markov decision problem, where the reward function captures the goal of the task as well as knowledge on effective elements of a basic strategy. We show how this reward function, and therefore the strategic information can be discovered with model-free inverse reinforcement learning from human table tennis matches. The approach is evaluated on data collected from players with different playing styles and skill levels. We show that the resulting reward functions are able to capture expert-specific strategic information that allow to distinguish the expert among players with different playing skills as well as different playing styles. To summarize, in this thesis, we have derived a computational model for table tennis that was
successfully implemented on a Barrett WAM robot arm and that has proven to produce human-like hitting motions. We also introduced a framework for learning a complex motor task based on a library
of demonstrated hitting primitives. To select and generalize these hitting movements we developed the mixture of motor primitives algorithm where the selection process can be adapted online based
on the success of the synthesized hitting movements. The setup was tested on a real robot, which showed that the resulting robot table tennis player is able to play a cooperative game against an human
opponent. Finally, we could show that it is possible to infer basic strategic information in table tennis from observing matches of human players using model-free inverse reinforcement learning
PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-based Planning
We present PRM-RL, a hierarchical method for long-range navigation task
completion that combines sampling based path planning with reinforcement
learning (RL). The RL agents learn short-range, point-to-point navigation
policies that capture robot dynamics and task constraints without knowledge of
the large-scale topology. Next, the sampling-based planners provide roadmaps
which connect robot configurations that can be successfully navigated by the RL
agent. The same RL agents are used to control the robot under the direction of
the planning, enabling long-range navigation. We use the Probabilistic Roadmaps
(PRMs) for the sampling-based planner. The RL agents are constructed using
feature-based and deep neural net policies in continuous state and action
spaces. We evaluate PRM-RL, both in simulation and on-robot, on two navigation
tasks with non-trivial robot dynamics: end-to-end differential drive indoor
navigation in office environments, and aerial cargo delivery in urban
environments with load displacement constraints. Our results show improvement
in task completion over both RL agents on their own and traditional
sampling-based planners. In the indoor navigation task, PRM-RL successfully
completes up to 215 m long trajectories under noisy sensor conditions, and the
aerial cargo delivery completes flights over 1000 m without violating the task
constraints in an environment 63 million times larger than used in training.Comment: 9 pages, 7 figure
Real Time Trajectory Prediction Using Deep Conditional Generative Models
Data driven methods for time series forecasting that quantify uncertainty
open new important possibilities for robot tasks with hard real time
constraints, allowing the robot system to make decisions that trade off between
reaction time and accuracy in the predictions. Despite the recent advances in
deep learning, it is still challenging to make long term accurate predictions
with the low latency required by real time robotic systems. In this paper, we
propose a deep conditional generative model for trajectory prediction that is
learned from a data set of collected trajectories. Our method uses encoder and
decoder deep networks that maps complete or partial trajectories to a Gaussian
distributed latent space and back, allowing for fast inference of the future
values of a trajectory given previous observations. The encoder and decoder
networks are trained using stochastic gradient variational Bayes. In the
experiments, we show that our model provides more accurate long term
predictions with a lower latency that popular models for trajectory forecasting
like recurrent neural networks or physical models based on differential
equations. Finally, we test our proposed approach in a robot table tennis
scenario to evaluate the performance of the proposed method in a robotic task
with hard real time constraints
Probabilistic movement modeling for intention inference in human-robot interaction.
Intention inference can be an essential step toward efficient humanrobot interaction. For this purpose, we propose the Intention-Driven Dynamics Model (IDDM) to probabilistically model the generative process of movements that are directed by the intention. The IDDM allows to infer the intention from observed movements using Bayes ’ theorem. The IDDM simultaneously finds a latent state representation of noisy and highdimensional observations, and models the intention-driven dynamics in the latent states. As most robotics applications are subject to real-time constraints, we develop an efficient online algorithm that allows for real-time intention inference. Two human-robot interaction scenarios, i.e., target prediction for robot table tennis and action recognition for interactive humanoid robots, are used to evaluate the performance of our inference algorithm. In both intention inference tasks, the proposed algorithm achieves substantial improvements over support vector machines and Gaussian processes.
Integration of sensorimotor mappings by making use of redundancies
Hemion N, Joublin F, Rohlfing K. Integration of sensorimotor mappings by making use of redundancies. In: IEEE Computational Intelligence Society, Institute of Electrical and Electronics Engineers, eds. The 2012 International Joint Conference on Neural Networks (IJCNN). Brisbane, Australia: IEEE; 2012