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Simon Manschitz

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4 papers

1 author row

ICRA Conference 2025 Conference Paper

Sampling-Based Grasp and Collision Prediction for Assisted Teleoperation

Simon Manschitz
Berk Gueler
Wei Ma
Dirk Ruiken

Shared autonomy allows for combining the global planning capabilities of a human operator with the strengths of a robot such as repeatability and accurate control. In a real-time teleoperation setting, one possibility for shared autonomy is to let the human operator decide for the rough movement and to let the robot do fine adjustments, e. g. , when the view of the operator is occluded. We present a learning-based concept for shared autonomy that aims at supporting the human operator in a real-time teleoperation setting. At every step, our system tracks the target pose set by the human operator as accurately as possible while at the same time satisfying a set of constraints which influence the robot's behavior. An important characteristic is that the constraints can be dynamically activated and deactivated which allows the system to provide task-specific assistance. Since the system must generate robot commands in real-time, solving an optimization problem in every iteration is not feasible. Instead, we sample potential target configurations and use Neural Networks for predicting the constraint costs for each configuration. By evaluating each configuration in parallel, our system is able to select the target configuration which satisfies the constraints and has the minimum distance to the operator's target pose with minimal delay. We evaluate the framework with a pick and place task on a bi-manual setup with two Franka Emika Panda robot arms with Robotiq grippers.

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IROS Conference 2016 Conference Paper

Probabilistic decomposition of sequential force interaction tasks into Movement Primitives

Simon Manschitz
Michael Gienger
Jens Kober
Jan Peters 0001

Learning sequential force interaction tasks from kinesthetic demonstrations is a promising approach to transfer human manipulation abilities to a robot. In this paper we propose a novel concept to decompose such demonstrations into a set of Movement Primitives (MPs). The decomposition is based on a probability distribution we call Directional Normal Distribution (DND). To capture the sequential properties of the manipulation task, we model the demonstrations with a Hidden Markov Model (HMM). Here, we employ mixtures of DNDs as the HMM's output emissions. The combination of HMMs and mixtures of DNDs allows to infer the MP's composition, i. e. , its coordinate frames, control variables and target coordinates from the demonstration data. In addition, it permits to determine an appropriate number of MPs that explains the demonstrations best. We evaluate the approach on kinesthetic demonstrations of a light bulb unscrewing task. Decomposing the task leads to intuitive and meaningful MPs that reflect the natural structure of the task.

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IROS Conference 2015 Conference Paper

Probabilistic progress prediction and sequencing of concurrent movement primitives

Simon Manschitz
Jens Kober
Michael Gienger
Jan Peters 0001

Classical approaches towards learning coordinated movement tasks often represent a movement in a sequential and exclusive fashion. Introducing concurrency allows to decompose such tasks into a number of separate sequences, for instance for two different end-effectors. While this results in a compact and generic representation of the individual movement primitives (MPs), it is a hard problem to learn their temporal and causal organization. This paper presents a concept for learning movement tasks that require the coordination of several controlled effectors of a robot. We firstly introduce a concept to learn and estimate the progress of individual MPs from a low number of demonstrations. Secondly, we propose a representation of the task that incorporates several concurrent sequences of MPs. Combining these two elements allows to learn and reproduce coordinated bi-manual movement tasks robustly. The synchronization of the concurrent MPs is achieved implicitly using the progress prediction. The approach is evaluated in two simulation studies with a 25 degrees of freedom two-arm robot performing a pick-and-place task.

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IROS Conference 2014 Conference Paper

Learning to sequence movement primitives from demonstrations

Simon Manschitz
Jens Kober
Michael Gienger
Jan Peters 0001

We present an approach for learning sequential robot skills through kinesthetic teaching. The demonstrations are represented by a sequence graph. Finding the transitions between consecutive basic movements is treated as classification problem where both Support Vector Machines and Gaussian Mixture Models are evaluated as classifiers. We show how the observed primitive order of all demonstrations can help to improve the movement reproduction by restricting the classification outcome to the currently executed primitive and its possible successors in the graph. The approach is validated with an experiment in which a 7-DOF Barrett WAM robot learns to unscrew a light bulb.

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