Author name cluster

Marco Todescato

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3 papers

2 author rows

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.

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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.

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AAAI Conference 2019 Conference Paper

Bounded Suboptimal Search with Learned Heuristics for Multi-Agent Systems

Markus Spies
Marco Todescato
Hannes Becker
Patrick Kesper
Nicolai Waniek
Meng Guo

A wide range of discrete planning problems can be solved optimally using graph search algorithms. However, optimal search quickly becomes infeasible with increased complexity of a problem. In such a case, heuristics that guide the planning process towards the goal state can increase performance considerably. Unfortunately, heuristics are often unavailable or need manual and time-consuming engineering. Building upon recent results on applying deep learning to learn generalized reactive policies, we propose to learn heuristics by imitation learning. After learning heuristics based on optimal examples, they are used to guide a classical search algorithm to solve unseen tasks. However, directly applying learned heuristics in search algorithms such as A∗ breaks optimality guarantees, since learned heuristics are not necessarily admissible. Therefore, we (i) propose a novel method that utilizes learned heuristics to guide Focal Search A∗, a variant of A∗ with guarantees on bounded suboptimality; (ii) compare the complexity and performance of jointly learning individual policies for multiple robots with an approach that learns one policy for all robots; (iii) thoroughly examine how learned policies generalize to previously unseen environments and demonstrate considerably improved performance in a simulated complex dynamic coverage problem.

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