Our Video Archive

Some of the most exciting robot learning research results (mainly done by our alumni) are shown here. For the recent videos checkout this page here.

Guiding Trajectory Optimization by Demonstrated Distributions

Trajectory optimization is an essential tool for motion planning under multiple constraints of robotic manipulators. Optimization-based methods can explicitly optimize a trajectory by leveraging prior knowledge of the system and have been used in various applications such as collision avoidance. However, these methods often require a hand-coded cost function in order to achieve the desired behavior. Specifying such cost function for a complex desired behavior, e.g., disentangling a rope, is a nontrivial task that is often even infeasible. Learning from demonstration (LfD) methods offer an alternative way to program robot motion. LfD methods are less dependent on analytical models and instead learn the behavior of experts implicitly from the demonstrated trajectories. However, the problem of adapting the demonstrations to new situations, e.g., avoiding newly introduced obstacles, has not been fully investigated in the literature. In this letter, we present a motion planning framework that combines the advantages of optimization-based and demonstration-based methods. We learn a distribution of trajectories demonstrated by human experts and use it to guide the trajectory optimization process. The resulting trajectory maintains the demonstrated behaviors, which are essential to performing the task successfully while adapting the trajectory to avoid obstacles. In simulated experiments and with a real robotic system, we verify that our approach optimizes the trajectory to avoid obstacles and encodes the demonstrated behavior in the resulting trajectory.

Want to know more? Read:

  1. Osa, T.; Ghalamzan, E. A. M.; Stolkin, R.; Lioutikov, R.; Peters, J.; Neumann, G. (2017). Guiding Trajectory Optimization by Demonstrated Distributions, IEEE Robotics and Automation Letters (RA-L), 2, 2, pp.819-826, IEEE.   Download Article [PDF]   BibTeX Reference [BibTex]

Preemptive Human-Robot Collaboration

This paper introduces our initial investigation on the problem of providing a semi-autonomous robot collaborator with anticipative capabilities to predict human actions. Anticipative robot behavior is a desired characteristic of robot collaborators that lead to fluid, proactive interactions. We are particularly interested in improving reactive methods that rely on human action recognition to activate the corresponding robot action. Action recognition invariably causes delay in the robot’s response, and the goal of our method is to eliminate this delay by predicting the next human action. Prediction is achieved by using a lookup table containing variations of assembly sequences, previously demonstrated by different users. The method uses the nearest neighbor sequence in the table that matches the actual sequence of human actions. At the movement level, our method uses a probabilistic representation of interaction primitives to generate robot trajectories. The method is demonstrated using a 7 degree-offreedom lightweight arm equipped with a 5-finger hand on an assembly task consisting of 17 steps. In this video, the robot tries to predict the next human action. The robot preemptively moves its hand towards the closest object that it "thinks" the human will need. And then wait until the human moves to confirm his/her action. This basically requires a plan as a sequence of actions. This sequence is given by a lookup table containing previous demonstrations of the assembly. This is the result of joint work between IAS-TU Darmstadt and IIT Madras.

Want to know more? Read:

  1. Maeda, G.; Maloo, A.; Ewerton, M.; Lioutikov, R.; Peters, J. (2016). Anticipative Interaction Primitives for Human-Robot Collaboration, AAAI Fall Symposium Series. Shared Autonomy in Research and Practice, Arlington, VA, USA.   Download Article [PDF]   BibTeX Reference [BibTex]

Learning to play golf by observing the human

Robot imitation based on observations of the human movement is a challenging problem as the structure of the human demonstrator and the robot learner are usually different. A movement that can be demonstrated well by a human may not be kinematically feasible for robot reproduction. A common approach to solve this kinematic mapping is to retarget predefined corresponding parts of the human and the robot kinematic structure. When such a correspondence is not available, manual scaling of the movement amplitude and the positioning of the demonstration in relation to the reference frame of the robot may be required. This letter's contribution is a method that eliminates both the need of human-robot structural associations and therefore is less sensitive to the type of robot kinematics and searches for the optimal location and adaptation of the human demonstration, such that the robot can accurately execute the optimized solution. The method defines a cost that quantifies the quality of the kinematic mapping and decreases it in conjunction with task-specific costs such as via-points and obstacles. We demonstrate the method experimentally where a real golf swing recorded via marker tracking is generalized to different speeds on the embodiment of a 7 degree-of-freedom (DoF) arm. In simulation, we compare solutions of robots with different kinematic structures.

Want to know more? Read:

  1. Maeda, G.; Ewerton, M.; Koert, D; Peters, J. (2016). Acquiring and Generalizing the Embodiment Mapping from Human Observations to Robot Skills, IEEE Robotics and Automation Letters (RA-L), 1, 2, pp.784--791.   Download Article [PDF]   BibTeX Reference [BibTex]

Controlling the head of the iCub using the Oculus Rift

Remote control of robots is often necessary to complete complex unstructured tasks in environments that are inaccessible (e.g. dangerous) for humans. Tele-operation of humanoid robots is often performed through motion tracking to reduce the complexity deriving from manually controlling a high number of DOF. However, most commercial motion tracking apparatus are expensive and often uncomfortable. Moreover, a limitation of this approach is the need to maintain visual contact with the operated robot, or to employ a second human operator to independently maneuver a camera. As a result, even performing simple tasks heavily depends on the skill and synchronization of the two operators. To alleviate this problem we propose to use augmented reality to provide the operator with first-person vision and a natural interface to directly control the camera and at the same time the robot. By integrating recent off-the-shelf technologies, we provide an affordable and intuitive environment composed of Microsoft Kinect, Oculus Rift and haptic SensorGlove to tele-operate in first-person humanoid robots. We demonstrate on the humanoid robot iCub that this set-up allows to quickly and naturally accomplish complex tasks.

Want to know more? Read:

  1. Fritsche, L.; Unverzagt, F.; Peters, J.; Calandra, R. (2015). First-Person Tele-Operation of a Humanoid Robot, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).   Download Article [PDF]   BibTeX Reference [BibTex]

Low Cost Sensor Glove with Force Feedback

Sensor gloves are popular input devices for a large variety of applications including health monitoring, control of music instruments, learning sign language, dexterous computer interfaces, and teleoperation robot hands. Many commercial products, as well as low-cost open-source projects, have been developed. We discuss here how low-cost (approx. 250 EUROs) sensor gloves with force feedback can be built, provide an open-source software interface for Matlab, and present first results in learning object manipulation skills through imitation learning on the humanoid robot iCub. The movie shows how the humanoid robot ICub can be trained to stack cups. A probabilistic trajectory model of the behavior is learned from two human demonstrations (the 2nd demo runs at double speed). After learning, the robot can reproduce the cup stacking motions.

Want to know more? Read:

  1. Rueckert, E.; Lioutikov, R.; Calandra, R.; Schmidt, M.; Beckerle, P.; Peters, J. (2015). Low-cost Sensor Glove with Force Feedback for Learning from Demonstrations using Probabilistic Trajectory Representations, ICRA 2015 Workshop on Tactile and force sensing for autonomous compliant intelligent robots.   Download Article [PDF]   BibTeX Reference [BibTex]

Estimating the phase of the execution of the human movement for human-robot interaction (IROS 2015)

Learning motor skills from multiple demonstrations presents a number of challenges. One of those challenges is the occurrence of occlusions and lack of sensor coverage, which may corrupt part of the recorded data. Another issue is the variability in speed of execution of the demonstrations, which may require a way of finding the correspondence between the time steps of the different demonstrations. In this paper, an approach to learn motor skills is proposed that accounts both for spatial and temporal variability of movements. This approach, based on an Expectation-Maximization algorithm to learn Probabilistic Movement Primitives, also allows for learning motor skills from partially observed demonstrations, which may result from occlusion or lack of sensor coverage. An application of the algorithm proposed in this work lies in the field of Human-Robot Interaction when the robot has to react to human movements executed at different speeds. Experiments in which a robotic arm receives a cup handed over by a human illustrate this application. The capabilities of the algorithm in learning and predicting movements are also evaluated in experiments using a data set of letters and a data set of golf putting movements. In this video, the robot uses the estimated phase to react with a corresponding speed such that the interaction looks more natural. This work allows our framework on Interaction Probabilistic Movement Primitives to not only infer the position at which the human will bring the cup but also at which speed the human is approaching so that the robot should react faster or slower.

Want to know more? Read:

  1. Ewerton, M.; Maeda, G.J.; Peters, J.; Neumann, G. (2015). Learning Motor Skills from Partially Observed Movements Executed at Different Speeds, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS), pp.456--463.   Download Article [PDF]   BibTeX Reference [BibTex]

Collaborative assembly with phase estimation (ISRR 2015)

This paper proposes an interaction learning method suited for semi-autonomous robots that work with or assist a human partner. The method aims at generating a collaborative trajectory of the robot as a function of the current action of the human. The trajectory generation is based on action recognition and prediction of the human movement given intermittent observations of his/her positions under unknown speeds of execution; a problem typically found when using motion capture systems in occluded scenarios. Of particular interest, the ability to predict the human movement while observing the initial part of the trajectory, allows for faster robot reactions. The method is based on probabilistically modelling the coupling between human-robot movement primitives and eliminates the need of time-alignment of the training data while being scalable in relation to the number of tasks. We evaluated the method using a 7-DoF lightweight robot arm equipped with a 5-finger hand in a multi-task collaborative assembly experiment, also comparing results with our previous method based on time-aligned trajectories.

Want to know more? Read:

  1. Maeda, G.; Neumann, G.; Ewerton, M.; Lioutikov, R.; Peters, J. (2015). A Probabilistic Framework for Semi-Autonomous Robots Based on Interaction Primitives with Phase Estimation, Proceedings of the International Symposium of Robotics Research (ISRR).   Download Article [PDF]   BibTeX Reference [BibTex]

Extracting Low-Dimensional Control Variables for Movement Primitives

Movement primitives (MPs) provide a powerful framework for data driven movement generation that has been successfully applied for learning from demonstrations and robot reinforcement learning. In robotics we often want to solve a multitude of different, but related tasks. As the parameters of the primitives are typically high dimensional, a common practice for the generalization of movement primitives to new tasks is to adapt only a small set of control variables, also called meta parameters, of the primitive. Yet, for most MP representations, the encoding of these control variables is pre-coded in the representation and can not be adapted to the considered tasks. In this paper, we want to learn the encoding of task-specific control variables also from data instead of relying on fixed meta-parameter representations. We use hierarchical Bayesian models (HBMs) to estimate a low dimensional latent variable model for probabilistic movement primitives (ProMPs), which is a recent movement primitive representation. We show on two real robot datasets that ProMPs based on HBMs outperform standard ProMPs in terms of generalization and learning from a small amount of data and also allows for an intuitive analysis of the movement. We also extend our HBM by a mixture model, such that we can model different movement types in the same dataset. The movie shows an example of target-reaching movements on a KUKA robot arm generated with a probabilistic movement representation. In the representation, latent variables are extracted from human demonstrations that can be used to generate new movements to unseen tasks, as well as to model different types of movements.

Want to know more? Read:

  1. Rueckert, E.; Mundo, J.; Paraschos, A.; Peters, J.; Neumann, G. (2015). Extracting Low-Dimensional Control Variables for Movement Primitives, Proceedings of the International Conference on Robotics and Automation (ICRA).   Download Article [PDF]   BibTeX Reference [BibTex]

Semi-Autonomous Robots at TU Darmstadt: Interaction Primitives for Assistive Robotics

This paper proposes an interaction learning method for collaborative and assistive robots based on movement primitives. The method allows for both action recognition and human-robot movement coordination. It uses imitation learning to construct a mixture model of human-robot interaction primitives. This probabilistic model allows the assistive trajectory of the robot to be inferred from human observations. The method is scalable in relation to the number of tasks and can learn nonlinear correlations between the trajectories that describe the human-robot interaction. We evaluated the method experimentally with a lightweight. This video shows a 7DoF compliant arm being used as an assistive robot. The algorithm is based on Interaction Probabilistic Movement Primitives.

Want to know more? Read:

  1. Maeda, G.; Neumann, G.; Ewerton, M.; Lioutikov, R.; Kroemer, O.; Peters, J. (2017). Probabilistic Movement Primitives for Coordination of Multiple Human-Robot Collaborative Tasks, Autonomous Robots (AURO), 41, 3, pp.593-612.   Download Article [PDF]   BibTeX Reference [BibTex]

Semi-Autonomous Robots at TU Darmstadt: Learning Multiple Collaborative Tasks

Robots that interact with humans must learn to not only adapt to different human partners but also to new interactions. Such a form of learning can be achieved by demonstrations and imitation. A recently introduced method to learn interactions from demonstrations is the framework of Interaction Primitives. While this framework is limited to represent and generalize a single interaction pattern, in practice, interactions between a human and a robot can consist of many different patterns. To overcome this limitation this paper proposes a Mixture of Interaction Primitives to learn multiple interaction patterns from unlabeled demonstrations. Specifically the proposed method uses Gaussian Mixture Models of Interaction Primitives to model nonlinear correlations between the movements of the different agents. We validate our algorithm with two experiments involving interactive tasks between a human and a lightweight robotic arm. In the first, we compare our proposed method with conventional Interaction Primitives in a toy problem scenario where the robot and the human are not linearly correlated. In the second, we present a proof-of-concept experiment where the robot assists a human in assembling a box

Want to know more? Read:

  1. Ewerton, M.; Neumann, G.; Lioutikov, R.; Ben Amor, H.; Peters, J.; Maeda, G. (2015). Learning Multiple Collaborative Tasks with a Mixture of Interaction Primitives, Proceedings of the International Conference on Robotics and Automation (ICRA), pp.1535--1542.   Download Article [PDF]   BibTeX Reference [BibTex]

Semi-Autonomous Robots at TU Darmstadt: Learning Multiple Collaborative Tasks

This paper proposes a probabilistic framework based on movement primitives for robots that work in collaboration with a human coworker. Since the human coworker can execute a variety of unforeseen tasks a requirement of our system is that the robot assistant must be able to adapt and learn new skills on-demand, without the need of an expert programmer. Thus, this paper leverages on the framework of imitation learning and its application to human-robot interaction using the concept of Interaction Primitives (IPs). We introduce the use of Probabilistic Movement Primitives (ProMPs) to devise an interaction method that both recognizes the action of a human and generates the appropriate movement primitive of the robot assistant. We evaluate our method on experiments using a lightweight arm interacting with a human partner and also using motion capture trajectories of two humans assembling a box. The advantages of ProMPs in relation to the original formulation for interaction are exposed and compared.

Want to know more? Read:

  1. Maeda, G.J.; Ewerton, M.; Lioutikov, R.; Amor, H.B.; Peters, J.; Neumann, G. (2014). Learning Interaction for Collaborative Tasks with Probabilistic Movement Primitives, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), pp.527--534.   Download Article [PDF]   BibTeX Reference [BibTex]

Predicting Object Interactions from Contact Distributions

Contacts between objects play an important role in manipulation tasks. Depending on the locations of contacts, different manipulations or interactions can be performed with the object. By observing the contacts between two objects, a robot can learn to detect potential interactions between them. Rather than defining a set of features for modeling the contact distributions, we propose a kernel-based approach. The contact points are first modeled using a Gaussian distribution. The similarity between these distributions is computed using a kernel function. The contact distributions are then classified using kernel logistic regression. The proposed approach was used to predict stable grasps of an elongated object, as well as to construct towers out of assorted toy blocks.

Want to know more? Read:

  1. Kroemer, O.; Peters, J. (2014). Predicting Object Interactions from Contact Distributions, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).   Download Article [PDF]   BibTeX Reference [BibTex]

Probabilistic Segmentation and Targeted Exploration of Objects in Cluttered Environments

Creating robots that can act autonomously in dynamic unstructured environments requires dealing with novel objects. Thus, an offline learning phase is not sufficient for recognizing and manipulating such objects. Rather, an autonomous robot needs to acquire knowledge through its own interaction with its environment, without using heuristics encoding human insights about the domain. Interaction also allows information that is not present in static images of a scene to be elicited. Out of a potentially large set of possible interactions, a robot must select actions that are expected to have the most informative outcomes to learn efficiently. In the proposed bottom-up probabilistic approach, the robot achieves this goal by quantifying the expected informativeness of its own actions in information-theoretic terms. We use this approach to segment a scene into its constituent objects. We retain a probability distribution over segmentations. We show that this approach is robust in the presence of noise and uncertainty in real-world experiments. Evaluations show that the proposed information-theoretic approach allows a robot to efficiently determine the composite structure of its environment. We also show that our probabilistic model allows straightforward integration of multiple modalities, such as movement data and static scene features. Learned static scene features allow for experience from similar environments to speed up learning for new scenes.

Want to know more? Read:

  1. van Hoof, H.; Kroemer, O; Peters, J. (2014). Probabilistic Segmentation and Targeted Exploration of Objects in Cluttered Environments, IEEE Transactions on Robotics (TRo), 30, 5, pp.1198-1209.   Download Article [PDF]   BibTeX Reference [BibTex]

Bayesian Gait Optimization for Bipedal Locomotion

One of the key challenges in robotic bipedal locomotion is finding gait parameters that optimize a desired performance criterion, such as speed, robustness or energy efficiency. Typically, gait optimization requires extensive robot experiments and specific expert knowledge. We propose to apply data-driven machine learning to automate and speed up the process of gait optimization. In particular, we use Bayesian optimization to efficiently find gait parameters that optimize the desired performance metric. As a proof of concept we demonstrate that Bayesian optimization is near-optimal in a classical stochastic optimal control framework. Moreover, we validate our approach to Bayesian gait optimization on a low-cost and fragile real bipedal walker and show that good walking gaits can be efficiently found by Bayesian optimization.

Want to know more? Read:

  1. Calandra, R.; Gopalan, N.; Seyfarth, A.; Peters, J.; Deisenroth, M.P. (2014). Bayesian Gait Optimization for Bipedal Locomotion, Proceedings of the 2014 Learning and Intelligent Optimization Conference (LION8).   Download Article [PDF]   BibTeX Reference [BibTex]

Extracting Low-Dimensional Control Variables for Movement Primitives

Movement primitives (MPs) provide a powerful framework for data-driven movement generation that has been successfully applied for learning from demonstrations and robot reinforcement learning. In robotics, we often want to solve a multitude of different, but related tasks. As the parameters of the primitives are typically high dimensional, a common practice for the generalization of movement primitives to new tasks is to adopt only a small set of control variables, also called meta parameters, of the primitive. Yet, for most MP representations, the encoding of these control variables is pre-coded in the representation and can not be adapted to the considered tasks. In this paper, we want to learn the encoding of task-specific control variables also from data instead of relying on fixed meta-parameter representations. We use hierarchical Bayesian models (HBMs) to estimate a low dimensional latent variable model for probabilistic movement primitives (ProMPs), which is a recent movement primitive representation. We show on two real robot datasets that ProMPs based on HBMs outperform standard ProMPs in terms of generalization and learning from a small amount of data and also allows for intuitive analysis of the movement. We also extend our HBM by a mixture model, such that we can model different movement types in the same dataset. The movie shows an example of target-reaching movements on a KUKA robot arm generated with a probabilistic movement representation. In the representation, latent variables are extracted from human demonstrations that can be used to generate new movements to unseen tasks, as well as to model different types of movements.

Want to know more? Read:

  1. Rueckert, E.; Mundo, J.; Paraschos, A.; Peters, J.; Neumann, G. (2015). Extracting Low-Dimensional Control Variables for Movement Primitives, Proceedings of the International Conference on Robotics and Automation (ICRA).   Download Article [PDF]   BibTeX Reference [BibTex]

  

zum Seitenanfang