Author name cluster

Philipp Schillinger

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

11 papers

2 author rows

ICRA Conference 2024 Conference Paper

Efficient End-to-End Detection of 6-DoF Grasps for Robotic Bin Picking

Yushi Liu
Alexander Qualmann
Zehao Yu 0002
Miroslav Gabriel
Philipp Schillinger
Markus Spies
Ngo Anh Vien
Andreas Geiger 0001

Bin picking is an important building block for many robotic systems, in logistics, production or in household use-cases. In recent years, machine learning methods for the prediction of 6-DoF grasps on diverse and unknown objects have shown promising progress. However, existing approaches only consider a single ground truth grasp orientation at a grasp location during training and therefore can only predict limited grasp orientations which leads to a reduced number of feasible grasps in bin picking with restricted reachability. In this paper, we propose a novel approach for learning dense and diverse 6-DoF grasps for parallel-jaw grippers in robotic bin picking. We introduce a parameterized grasp distribution model based on Power-Spherical distributions that enables a training based on all possible ground truth samples. Thereby, we also consider the grasp uncertainty enhancing the model’s robustness to noisy inputs. As a result, given a single top-down view depth image, our model can generate diverse grasps with multiple collision-free grasp orientations. Experimental evaluations in simulation and on a real robotic bin picking setup demonstrate the model’s ability to generalize across various object categories achieving an object clearing rate of around 90% in simulation and real-world experiments. We also outperform state of the art approaches. Moreover, the proposed approach exhibits its usability in real robot experiments without any refinement steps, even when only trained on a synthetic dataset, due to the probabilistic grasp distribution modeling.

Details

ICRA Conference 2024 Conference Paper

Pseudo Labeling and Contextual Curriculum Learning for Online Grasp Learning in Robotic Bin Picking

Huy Le
Philipp Schillinger
Miroslav Gabriel
Alexander Qualmann
Ngo Anh Vien

The prevailing grasp prediction methods predominantly rely on offline learning, overlooking the dynamic grasp learning that occurs during real-time adaptation to novel picking scenarios. These scenarios may involve previously unseen objects, variations in camera perspectives, and bin configurations, among other factors. In this paper, we introduce a novel approach, SSL-ConvSAC, that combines semi-supervised learning and reinforcement learning for online grasp learning. By treating pixels with reward feedback as labeled data and others as unlabeled, it efficiently exploits unlabeled data to enhance learning. In addition, we address the imbalance between labeled and unlabeled data by proposing a contextual curriculum-based method. We ablate the proposed approach on real-world evaluation data and demonstrate promise for improving online grasp learning on bin picking tasks using a physical 7-DoF Franka Emika robot arm with a suction gripper. Video: https://youtu.be/OAro5pg8I9U

Details

ICRA Conference 2024 Conference Paper

Uncertainty-driven Exploration Strategies for Online Grasp Learning

Yitian Shi
Philipp Schillinger
Miroslav Gabriel
Alexander Qualmann
Zohar Feldman
Hanna Ziesche
Ngo Anh Vien

Existing grasp prediction approaches are mostly based on offline learning, while, ignoring the exploratory grasp learning during online adaptation to new picking scenarios, i. e. , objects that are unseen or out-of-domain (OOD), camera and bin settings, etc. In this paper, we present an uncertainty-based approach for online learning of grasp predictions for robotic bin picking. Specifically, the online learning algorithm with an effective exploration strategy can significantly improve its adaptation performance to unseen environment settings. To this end, we first propose to formulate online grasp learning as an RL problem that will allow us to adapt both grasp reward prediction and grasp poses. We propose various uncertainty estimation schemes based on Bayesian uncertainty quantification and distributional ensembles. We carry out evaluations on real-world bin picking scenes of varying difficulty. The objects in the bin have various challenging physical and perceptual characteristics that can be characterized by semi- or total transparency, and irregular or curved surfaces. The results of our experiments demonstrate a notable improvement of grasp performance in comparison to conventional online learning methods which incorporate only naive exploration strategies. Video: https://youtu.be/fPKOrjC2QrU

Details

IROS Conference 2023 Conference Paper

Model-Free Grasping with Multi-Suction Cup Grippers for Robotic Bin Picking

Philipp Schillinger
Miroslav Gabriel
Alexander Kuss
Hanna Ziesche
Ngo Anh Vien

This paper presents a novel method for model-free prediction of grasp poses for suction grippers with multiple suction cups. Our approach is agnostic to the design of the gripper and does not require gripper-specific training data. In particular, we propose a two-step approach, where first, a neural network predicts pixel-wise grasp quality for an input image to indicate areas that are generally graspable. Second, an optimization step determines the optimal gripper selection and corresponding grasp poses based on configured gripper layouts and activation schemes. In addition, we introduce a method for automated labeling for supervised training of the grasp quality network. Experimental evaluations on a real-world industrial application with bin picking scenes of varying difficulty demonstrate the effectiveness of our method.

Details

IROS Conference 2022 Conference Paper

Optimizing Demonstrated Robot Manipulation Skills for Temporal Logic Constraints

Akshay Dhonthi
Philipp Schillinger
Leonel Rozo
Daniele Nardi

For performing robotic manipulation tasks, the core problem is determining suitable trajectories that fulfill the task requirements. Various approaches to compute such trajectories exist, being learning and optimization the main driving techniques. Our work builds on the learning-from-demonstration (LfD) paradigm, where an expert demonstrates motions, and the robot learns to imitate them. However, expert demonstrations are not sufficient to capture all sorts of task specifications, such as the timing to grasp an object. In this paper, we propose a new method that considers formal task specifications within LfD skills. Precisely, we leverage Signal Temporal Logic (STL), an expressive form of temporal properties of systems, to formulate task specifications and use black-box optimization (BBO) to adapt an LfD skill accordingly. We demonstrate our approach in simulation and on a real industrial setting using several tasks that showcase how our approach addresses the LfD limitations using STL and BBO.

Details

AAAI Conference 2021 Conference Paper

Supervised Training of Dense Object Nets using Optimal Descriptors for Industrial Robotic Applications

Andras Gabor Kupcsik
Markus Spies
Alexander Klein
Marco Todescato
Nicolai Waniek
Philipp Schillinger
Mathias Bürger

Dense Object Nets (DONs) by Florence, Manuelli and Tedrake (2018) introduced dense object descriptors as a novel visual object representation for the robotics community. It is suitable for many applications including object grasping, policy learning, etc. DONs map an RGB image depicting an object into a descriptor space image, which implicitly encodes key features of an object invariant to the relative camera pose. Impressively, the self-supervised training of DONs can be applied to arbitrary objects and can be evaluated and deployed within hours. However, the training approach relies on accurate depth images and faces challenges with small, reflective objects, typical for industrial settings, when using consumer grade depth cameras. In this paper we show that given a 3D model of an object, we can generate its descriptor space image, which allows for supervised training of DONs. We rely on Laplacian Eigenmaps (LE) to embed the 3D model of an object into an optimally generated space. While our approach uses more domain knowledge, it can be efficiently applied even for smaller and reflective objects, as it does not rely on depth information. We compare the training methods on generating 6D grasps for industrial objects and show that our novel supervised training approach improves the pick-andplace performance in industry-relevant tasks.

PDF Details

IROS Conference 2020 Conference Paper

Learning and Sequencing of Object-Centric Manipulation Skills for Industrial Tasks

Leonel Rozo
Meng Guo 0002
Andras G. Kupcsik
Marco Todescato
Philipp Schillinger
Markus Giftthaler
Matthias Ochs
Markus Spies

Enabling robots to quickly learn manipulation skills is an important, yet challenging problem. Such manipulation skills should be flexible, e. g. , be able adapt to the current workspace configuration. Furthermore, to accomplish complex manipulation tasks, robots should be able to sequence several skills and adapt them to changing situations. In this work, we propose a rapid robot skill-sequencing algorithm, where the skills are encoded by object-centric hidden semi-Markov models. The learned skill models can encode multimodal (temporal and spatial) trajectory distributions. This approach significantly reduces manual modeling efforts, while ensuring a high degree of flexibility and re-usability of learned skills. Given a task goal and a set of generic skills, our framework computes smooth transitions between skill instances. To compute the corresponding optimal end-effector trajectory in task space we rely on Riemannian optimal controller. We demonstrate this approach on a 7 DoF robot arm for industrial assembly tasks.

Details

ICRA Conference 2018 Conference Paper

Auctioning over Probabilistic Options for Temporal Logic-Based Multi-Robot Cooperation Under Uncertainty

Philipp Schillinger
Mathias Bürger
Dimos V. Dimarogonas

Coordinating a team of robots to fulfill a common task is still a demanding problem. This is even more the case when considering uncertainty in the environment, as well as temporal dependencies within the task specification. A multi-robot cooperation from a single goal specification requires mechanisms for decomposing the goal as well as an efficient planning for the team. However, planning action sequences offline is insufficient in real world applications. Rather, due to uncertainties, the robots also need to closely coordinate during execution and adjust their policies when additional observations are made. The framework presented in this paper enables the robot team to cooperatively fulfill tasks given as temporal logic specifications while explicitly considering uncertainty and incorporating observations during execution. We present the effectiveness of our ROS implementation of this approach in a case study scenario.

Details

ICRA Conference 2017 Conference Paper

Multi-objective search for optimal multi-robot planning with finite LTL specifications and resource constraints

Philipp Schillinger
Mathias Bürger
Dimos V. Dimarogonas

We present an efficient approach to plan action sequences for a team of robots from a single finite LTL mission specification. The resulting execution strategy is proven to solve the given mission with minimal team costs, e. g. , with shortest execution time. For planning, an established graph-based search method based on the multi-objective shortest path problem is adapted to multi-robot planning and extended to support resource constraints. We further improve planning efficiency significantly for missions which consist of independent parts by using previous results regarding LTL decomposition. The efficiency and practicality of the ROS implementation of our approach is demonstrated in example scenarios.

Details

ICRA Conference 2016 Conference Paper

Human-robot collaborative high-level control with application to rescue robotics

Philipp Schillinger
Stefan Kohlbrecher
Oskar von Stryk

Motivated by the DARPA Robotics Challenge (DRC), the application of operator assisted (semi-)autonomous robots with highly complex locomotion and manipulation abilities is considered for solving complex tasks in potentially unknown and unstructured environments. Because of the limited a priori knowledge about the state of the environment and tasks needed to achieve a complex mission, a sufficiently complete a priori design of high level robot behaviors is not possible. Most of the situational knowledge required for such behavior design is gathered only during runtime and needs to be interpreted by a human operator. However, current behavior control approaches only allow for very limited adaptation at runtime and no flexible operator interaction. In this paper an approach for definition and execution of complex robot behaviors based on hierarchical state machines is presented, allowing to flexibly change the structure of behaviors on the fly during runtime through assistance of a remote operator. The efficiency of the proposed approach is demonstrated and evaluated not only in an example scenario, but also by application in two robot competitions.

Details

ICRA Conference 2016 Conference Paper

Reactive high-level behavior synthesis for an Atlas humanoid robot

Spyros Maniatopoulos
Philipp Schillinger
Vitchyr H. Pong
David C. Conner
Hadas Kress-Gazit

In this work, we take a step towards bridging the gap between the theory of formal synthesis and its application to real-world, complex, robotic systems. In particular, we present an end-to-end approach for the automatic generation of code that implements high-level robot behaviors in a verifiably correct manner, including reaction to the possible failures of low-level actions. We start with a description of the system defined a priori. Thus, a non-expert user need only specify a high-level task. We automatically construct a formal specification, in a fragment of Linear Temporal Logic (LTL), that encodes the system's capabilities and constraints, the task, and the desired reaction to low-level failures. We then synthesize a reactive mission plan that is guaranteed to satisfy the formal specification, i. e. , achieve the task's goals or correctly react to failures. Lastly, we automatically generate a state machine that instantiates the synthesized symbolic plan in software. We showcase our approach using Team ViGIR's software and Atlas humanoid robot and present lab experiments, thus demonstrating the application of formal synthesis techniques to complex robotic systems. The proposed approach has been implemented and open-sourced as a collection of Robot Operating System (ROS) packages, which are adaptable to other systems.

Details