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IEEE-RAS International Conference on Humanoid Robots

作者： Manuel Baum Matthew Bernstein Roberto Martin-Martin Sebastian Höfer Johannes Kulick Marc Toussaint Alex Kacelnik Oliver Brock Robotics and Biology Lab (RBO) Technische Universität Berlin Machine Learning and Robotics Lab (MLR) Universität Stuttgart Department of Zoology Oxford University

ISBN: (纸本)9781538646793

How can we close the gap between animals and robots when it comes to intelligently interacting with the environment? On our quest for answers, we have investigated the problem of physically exploring complex mechanical puzzles, called lockboxes. Biologists have discovered that cockatoos are intrinsically motivated to explore and solve such problems through physical explorative behavior. In this work, we study how different strategies shape the robots' exploration, given basic perception-action skills. Our evaluation highlights the influence of different statistical priors on the performance of the exploration strategies, showing that not only a range of computational methods, but also a range of priors could explain different exploration behaviors. We carry out our study of exploration strategies both in simulation and on two robot platforms. This first step towards a fully integrated real-world system allowed us to identify and remove limitations of our prior theoretical work on cross-entropy-based exploration when applied to complex realistic scenarios. In this paper we propose novel variants of this strategy and our experiments verify that the cross-entropy method performs well on a physical lockbox analogue of the cockatoo apparatus, and can generalize to lockboxes of different properties.

关键词： Robot sensing systems Entropy Kinematics Robot kinematics Planning

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Is Data All That Matters? the Role of Control Frequency for learning-Based Sampled-Data Control of Uncertain Systems

Is Data All That Matters? the Role of Control Frequency for ...

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American Control Conference (ACC)

作者： Ralf Römer Lukas Brunke Siqi Zhou Angela P. Schoellig Learning Systems and Robotics Lab (***) School of Computation Information and Technology and the Munich Institute for Robotics and Machine Intelligence (MIRMI) Technical University of Munich Germany

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

learning models or control policies from data has become a powerful tool to improve the performance of uncertain systems. While a strong focus has been placed on increasing the amount and quality of data to improve performance, data can never fully eliminate uncertainty, making feedback necessary to ensure stability and performance. We show that the control frequency at which the input is recalculated is a crucial design parameter, yet it has hardly been considered before. We address this gap by combining probabilistic model learning and sampled-data control. We use Gaussian processes (GPs) to learn a continuous-time model and compute a corresponding discrete-time controller. The result is an uncertain sampled-data control system, for which we derive robust stability conditions. We formulate semidefinite programs to compute the minimum control frequency required for stability and to optimize performance. As a result, our approach enables us to study the effect of both control frequency and data on stability and closed-loop performance. We show in numerical simulations of a quadrotor that performance can be improved by increasing either the amount of data or the control frequency, and that we can trade off one for the other. For example, by increasing the control frequency by 33%, we can reduce the number of data points by half while still achieving similar performance.

关键词： Uncertain systems Uncertainty Robust stability Computational modeling Control systems Stability analysis Data models

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Co-Optimizing Robot, Environment, and Tool Design via Joint Manipulation Planning

Co-Optimizing Robot, Environment, and Tool Design via Joint ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Marc Toussaint Jung-Su Ha Ozgur S. Oguz Learning & Intelligent Systems Lab TU Berlin Germany Max Planck Institute for Intelligent Systems Germany Machine Learning & Robotics Lab University of Stuttgart Germany

Existing work on sequential manipulation planning and trajectory optimization typically assumes the robot, environment and tools to be given. However, in particular in industrial applications, it is highly interesting to ask, what would be an optimal robot design, tool shape, or robot station geometry for a particular ensemble of manipulation tasks. To tackle this problem we propose a formulation to jointly optimize over static design parameters and the sequential manipulation trajectory. We can include optimization objectives such as penalizing velocities (path length) and joint torques. Our evaluations show that design optimization can significantly improve on such metrics. For instance, in a wrench tool demonstration scenario we show that the shape of the wrench tool as well as design of the robot can be optimized to allow for exerting a necessary external torque with minimal effort.

关键词： Measurement Torque Service robots Shape Tools Programming Planning

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learning Efficient Constraint Graph Sampling for Robotic Sequential Manipulation

Learning Efficient Constraint Graph Sampling for Robotic Seq...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Joaquim Ortiz-Haro Valentin N. Hartmann Ozgur S. Oguz Marc Toussaint Machine Learning & Robotics Lab University of Stuttgart Germany Learning and Intelligent Systems Lab TU Berlin Germany Max Planck Institute for Intelligent Systems Germany

Efficient sampling from constraint manifolds, and thereby generating a diverse set of solutions for feasibility problems, is a fundamental challenge. We consider the case where a problem is factored, that is, the underlying nonlinear program is decomposed into differentiable equality and inequality constraints, each of which depends only on some variables. Such problems are at the core of efficient and robust sequential robot manipulation planning. Naive sequential conditional sampling of individual variables, as well as fully joint sampling of all variables at once (e.g., leveraging optimization methods), can be highly inefficient and non-robust. We propose a novel framework to learn how to break the overall problem into smaller sequential sampling problems. Specifically, we leverage Monte-Carlo Tree Search to learn assignment orders for the variable-subsets, in order to minimize the computation time to generate feasible full samples. This strategy allows us to efficiently compute a set of diverse valid robot configurations for mode-switches within sequential manipulation tasks, which are waypoints for subsequent trajectory optimization or sampling-based motion planning algorithms. We show that the learning method quickly converges to the best sampling strategy for a given problem, and outperforms user-defined orderings or fully joint optimization, while providing a higher sample diversity. Video: https://***/mCNdvjTbHNI

关键词： Manifolds learning systems Costs Monte Carlo methods Conferences Optimization methods Planning

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Constrained Bayesian optimization of combined interaction force/task space controllers for manipulations

Constrained Bayesian optimization of combined interaction fo...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Danny Drieß Peter Englert Marc Toussaint Universitat Stuttgart Stuttgart Baden-WÃ¼rttemberg DE Machine Learning and Robotics Lab University of Stuttgart Germany

In this paper, we address the problem of how a robot can optimize parameters of combined interaction force/task space controllers under a success constraint in an active way. To enable the robot to explore its environment robustly, safely and without the risk of damaging anything, suitable control concepts have to be developed that enable compliant and force control in situations that are afflicted with high uncertainties. Instances of such concepts are impedance, operational space or hybrid control. However, the parameters of these controllers have to be tuned precisely in order to achieve reasonable performance, which is inherently challenging, as often no sufficient model of the environment is available. To overcome this, we propose to use constrained Bayesian optimization to enable the robot to tune its controller parameters autonomously. Unlike other controller tuning methods, this method allows us to include a success constraint into the optimization. Further, we introduce novel performance measures for compliant, force controlled robots. In real world experiments we show that our approach is able to optimize the parameters for a task that consists of establishing and maintaining contact between the robot and the environment efficiently and successfully.

关键词： Aerospace electronics Robots Bayes methods Force Tuning Cost function

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Deep 6-DoF Tracking of Unknown Objects for Reactive Grasping

Deep 6-DoF Tracking of Unknown Objects for Reactive Grasping

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IEEE International Conference on robotics and Automation (ICRA)

作者： Marc Tuscher Julian Hörz Danny Driess Marc Toussaint sereact Machine Learning and Robotics Lab University of Stuttgart Max-Planck Institute for Intelligent Systems Stuttgart Learning and Intelligent Systems TU Berlin

Robotic manipulation of unknown objects is an important field of research. Practical applications occur in many real-world settings where robots need to interact with an unknown environment. We tackle the problem of reactive grasping by proposing a method for unknown object tracking, grasp point sampling and dynamic trajectory planning. Our object tracking method combines Siamese Networks with an Iterative Closest Point approach for pointcloud registration into a method for 6-DoF unknown object tracking. The method does not require further training and is robust to noise and occlusion. We propose a robotic manipulation system, which is able to grasp a wide variety of formerly unseen objects and is robust against object perturbations and inferior grasping points.

关键词： Training Target tracking Automation Trajectory planning Perturbation methods Conferences Grasping

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Deep Visual Heuristics: learning Feasibility of Mixed-Integer Programs for Manipulation Planning

Deep Visual Heuristics: Learning Feasibility of Mixed-Intege...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Danny Driess Ozgur Oguz Jung-Su Ha Marc Toussaint Machine Learning and Robotics Lab University of Stuttgart Germany Max Planck Institute for Intelligent Systems Stuttgart Germany

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

In this paper, we propose a deep neural network that predicts the feasibility of a mixed-integer program from visual input for robot manipulation planning. Integrating learning into task and motion planning is challenging, since it is unclear how the scene and goals can be encoded as input to the learning algorithm in a way that enables to generalize over a variety of tasks in environments with changing numbers of objects and goals. To achieve this, we propose to encode the scene and the target object directly in the image *** experiments show that our proposed network generalizes to scenes with multiple objects, although during training only two objects are present at the same time. By using the learned network as a heuristic to guide the search over the discrete variables of the mixed-integer program, the number of optimization problems that have to be solved to find a feasible solution or to detect infeasibility can greatly be reduced.

关键词： Planning Task analysis Robot sensing systems Neural networks Grasping Search problems

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Asymptotically Optimal Belief Space Planning in Discrete Partially-Observable Domains

arXiv

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arXiv 2023年

作者： Freund, Janis Eric Phiquepal, Camille Orthey, Andreas Toussaint, Marc Technical University of Berlin Germany Realtime Robotics Inc. BostonMA United States Machine Learning & Robotics Lab University of Stuttgart Germany

Robots often have to operate in discrete partially observable worlds, where the states of world are only observable at runtime. To react to different world states, robots need contingencies. However, computing contingencies is costly and often non-optimal. To address this problem, we develop the improved path tree optimization (PTO) method. PTO computes motion contingencies by constructing a tree of motion paths in belief space. This is achieved by constructing a graph of configurations, then adding observation edges to extend the graph to belief space. Afterwards, we use a dynamic programming step to extract the path tree. PTO extends prior work by adding a camera-based state sampler to improve the search for observation points. We also add support to non-euclidean state spaces, provide an implementation in the open motion planning library (OMPL), and evaluate PTO on four realistic scenarios with a virtual camera in up to 10-dimensional state spaces. We compare PTO with a default and with the new camera-based state sampler. The results indicate that the camera-based state sampler improves success rates in 3 out of 4 scenarios while having a significant lower memory footprint. This makes PTO an important contribution to advance the state-of-the-art for discrete belief space planning. Copyright © 2023, The Authors. All rights reserved.

关键词： Dynamic programming

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Planning Coordinated Human-Robot Motions with Neural Network Full-Body Prediction Models

arXiv

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arXiv 2022年

作者： Kratzer, Philipp Toussaint, Marc Mainprice, Jim The Machine Learning and Robotics Lab University of Stuttgart Germany The Humans to Robots Motions Research Group University of Stuttgart Germany The Learning and Intelligent Systems Lab TU Berlin Germany

Numerical optimization has become a popular approach to plan smooth motion trajectories for robots. However, when sharing space with humans, balancing properly safety, comfort and efficiency still remains challenging. This is notably the case because humans adapt their behavior to that of the robot, raising the need for intricate planning and prediction. In this paper, we propose a novel optimization-based motion planning algorithm, which generates robot motions, while simultaneously maximizing the human trajectory likelihood under a data-driven predictive model. Considering planning and prediction together allows us to formulate objective and constraint functions in the joint human-robot state space. Key to the approach are added latent space modifiers to a differentiable human predictive model based on a dedicated recurrent neural network. These modifiers allow to change the human prediction within motion optimization. We empirically evaluate our method using the publicly available MoGaze [1] dataset. Our results indicate that the proposed framework outperforms current baselines for planning handover trajectories and avoiding collisions between a robot and a human. Our experiments demonstrate collaborative motion trajectories, where both, the human prediction and the robot plan, adapt to each other. © 2022, CC BY.

关键词： Forecasting

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Reinforcement learning with Non-uniform State Representations for Adaptive Search

Reinforcement Learning with Non-uniform State Representation...

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IEEE International Workshop on Safety, Security, and Rescue robotics (SSRR)

作者： Sandeep Manjanna Herke van Hoof Gregory Dudek Mobile Robotics Lab (MRL) McGill University Montreal Canada Amsterdam Machine Learning Lab (AMLAB) University of Amsterdam Amsterdam Netherlands

The following topics are dealt with: mobile robots; path planning; robot vision; autonomous aerial vehicles; remotely operated vehicles; rescue robots; cameras; multi-robot systems; emergency services; learning (artif... 详细信息

ISBN: (纸本)9781538655733

关键词： Robots Probability distribution Search problems learning (artificial intelligence) Trajectory Task analysis

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