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A Framework for learning Behavior Trees in Collaborative Robotic Applications 19

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A Framework for Learning Behavior Trees in Collaborative Rob...

19th IEEE International Conference on Automation Science and Engineering, CASE 2023

作者： Iovino, Matteo Styrud, Jonathan Falco, Pietro Smith, Christian Abb Corporate Research Västerås Sweden Kth - Royal Institute of Technology Division of Robotics Perception and Learning Stockholm Sweden Abb Robotics Västerås Sweden

ISBN: (纸本)9798350320695

In modern industrial collaborative robotic applications, it is desirable to create robot programs automatically, intuitively, and time-efficiently. Moreover, robots need to be controlled by reactive policies to face the unpredictability of the environment they operate in. In this paper we propose a framework that combines a method that learns Behavior Trees (BTs) from demonstration with a method that evolves them with Genetic Programming (GP) for collaborative robotic applications. The main contribution of this paper is to show that by combining the two learning methods we obtain a method that allows non-expert users to semi-automatically, time-efficiently, and interactively generate BTs. We validate the framework with a series of manipulation experiments. The BT is fully learnt in simulation and then transferred to a real collaborative robot. © 2023 IEEE.

关键词： Genetic programming

Network Parameter Control in Cellular Networks through Graph-Based Multi-Agent Constrained Reinforcement learning 19

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Network Parameter Control in Cellular Networks through Graph...

19th IEEE International Conference on Automation Science and Engineering, CASE 2023

作者： Forsberg, Albin Larsson Nikou, Alexandros Feljan, Aneta Vulgarakis Tumova, Jana Stockholm Sweden Kth Division of Robotics Perception and Learning Department of Electrical Engineering and Computer Science Stockholm Sweden Digital Futures United States

ISBN: (纸本)9798350320695

Cellular networks are growing in complexity at increasing speed and the geographical locations in which they are deployed in are getting denser. Traditional control methods fall short in providing a scalable and dynamic way of adapting the network to new conditions. Distributed multiagent reinforcement learning successfully addresses scalability problems seen in centralized approaches. The question of achieving learning with constraint satisfaction in distributed systems is still left unanswered in the state-of-the-art. In this work, we aim to perform distributed multi-agent constrained reinforcement learning in order to learn a policy online while satisfying imposed constraints. We use a coordination graph to model the interactions between agents and decompose the global value function. A conservative safety critic is trained in parallel to evaluate the safety of proposed actions. Our method allows for separate training of both the critic and the value network independently of each other, and hence offers flexibility in how and when to train the different models. The results are compared to a baseline using no safety critic. Simulations show that the agents succeed in learning a policy that can satisfy the constraints, while still maximizing the objective. © 2023 IEEE.

关键词： Graphic methods

One Map to Find Them All: Real-time Open-Vocabulary Mapping for Zero-shot Multi-Object Navigation

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arXiv 2024年

作者： Busch, Finn Lukas Homberger, Timon Ortega-Peimbert, Jesús Yang, Quantao Andersson, Olov Division of Robotics Perception and Learning KTH Royal Institute of Technology Sweden

The capability to efficiently search for objects in complex environments is fundamental for many real-world robot applications. Recent advances in open-vocabulary vision models have resulted in semantically-informed object navigation methods that allow a robot to search for an arbitrary object without prior training. However, these zero-shot methods have so far treated the environment as unknown for each consecutive query. In this paper we introduce a new benchmark for zero-shot multi-object navigation, allowing the robot to leverage information gathered from previous searches to more efficiently find new objects. To address this problem we build a reusable open-vocabulary feature map tailored for real-time object search. We further propose a probabilistic-semantic map update that mitigates common sources of errors in semantic feature extraction and leverage this semantic uncertainty for informed multi-object exploration. We evaluate our method on a set of object navigation tasks in both simulation as well as with a real robot, running in real-time on a Jetson Orin AGX. We demonstrate that it outperforms existing state-of-the-art approaches both on single and multi-object navigation tasks. Additional videos, code and the multi-object navigation benchmark will be available on https://***/OneMap. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

Pushing Everything Everywhere All At Once: Probabilistic Prehensile Pushing

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arXiv 2025年

作者： Perugini, Patrizio Lundell, Jens Friedl, Katharina Kragic, Danica Flanders Make Belgium The division of Robotics Perception and Learning at KTH Stockholm Sweden

We address prehensile pushing, the problem of manipulating a grasped object by pushing against the environment. Our solution is an efficient nonlinear trajectory optimization problem relaxed from an exact mixed integer non-linear trajectory optimization formulation. The critical insight is recasting the external pushers (environment) as a discrete probability distribution instead of binary variables and minimizing the entropy of the distribution. The probabilistic reformulation allows all pushers to be used simultaneously, but at the optimum, the probability mass concentrates onto one due to the entropy minimization. We numerically compare our method against a state-of-the-art sampling-based baseline on a prehensile pushing task. The results demonstrate that our method finds trajectories 8 times faster and at a 20 times lower cost than the baseline. Finally, we demonstrate that a simulated and real Frank Panda robot can successfully manipulate different objects following the trajectories proposed by our method. Supplementary materials are available at https: //***/. Copyright © 2025, The Authors. All rights reserved.

关键词： Trajectories

A Control- Theoretic Framework for Voronoi-like Space Partitioning in Multi-Agent Drone Systems with Second-Order Costs

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A Control- Theoretic Framework for Voronoi-like Space Partit...

International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Andre N. Costa Petter Ogren Division of Robotics Perception and Learning (RPL) Royal Institute of Technology (KTH) Stockholm Sweden

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

We present a framework for space partitioning, where the Regions of Influence (ROIs) of the agents are defined based on proximity metrics derived from the cost of optimal control problems. Efficient space partitioning in multi-agent systems, particularly in Unmanned Aerial Vehicle (UAV) operations, is critical for coverage, load balancing, and task allocation. However, traditional methods, such as the standard Voronoi Diagrams (VDs) based solely on distances, often fail to account for the dynamic behavior and capabilities of UAV s. We generalize the VD concept by replacing distance-based metrics with transition costs obtained from optimal control formulations. This allows the resulting partitions to incorporate UAV dynamics, including initial states and control effort, in defining regions where one agent is more suitable than another for a given task. We show that for a broad class of problems with second-order optimal costs, the boundaries between ROIs are given by either hyperplanes or quadratic surfaces. This includes, as special cases, classical VDs based on distance, minimum-time problems for single integrators, the fixed-final-state (FFS) optimal transfer problem, and Linear Quadratic Regulators (LQR). Overall, the proposed framework bridges geometric and control-theoretic space partitioning, enabling dynamic and context-aware task allocation in multi-agent systems.

关键词： Measurement Space vehicles Costs Optimal control Aerospace electronics Autonomous aerial vehicles Resource management Vehicle dynamics Standards Multi-agent systems

Streaming Network for Continual learning of Object Relocations under Household Context Drifts

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arXiv 2024年

作者： Bartoli, Ermanno Doğan, Fethiye Irmak Leite, Iolanda Faculty of Robotics Perception and Learning KTH Royal Institute of Technology Stockholm Sweden

In most applications, robots need to adapt to new environments and be multi-functional without forgetting previous information. This requirement gains further importance in real-world scenarios where robots operate in coexistence with humans. In these complex environments, human actions inevitably lead to changes, requiring robots to adapt accordingly. To effectively address these dynamics, the concept of continual learning proves essential. It not only enables learning models to integrate new knowledge while preserving existing information but also facilitates the acquisition of insights from diverse contexts. This aspect is particularly relevant to the issue of context-switching, where robots must navigate and adapt to changing situational dynamics. Our approach introduces a novel approach to effectively tackle the problem of context drifts by designing a Streaming Graph Neural Network that incorporates both regularization and rehearsal techniques. Our Continual GTM model enables us to retain previous knowledge from different contexts, and it is more effective than traditional fine-tuning approaches. We evaluated the efficacy of Continual GTM in predicting human routines within household environments, leveraging spatio-temporal object dynamics across diverse scenarios. © 2024, CC BY-NC-SA.

关键词： Adversarial machine learning

A RIEMANNIAN FRAMEWORK FOR learning REDUCED-ORDER LAGRANGIAN DYNAMICS

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arXiv 2024年

作者： Friedl, Katharina Jaquier, Noémie Lundell, Jens Asfour, Tamim Kragic, Danica Division of Robotics Perception and Learning KTH Royal Institute of Technology Sweden Germany

By incorporating physical consistency as inductive bias, deep neural networks display increased generalization capabilities and data efficiency in learning nonlinear dynamic models. However, the complexity of these models generally increases with the system dimensionality, requiring larger datasets, more complex deep networks, and significant computational effort. We propose a novel geometric network architecture to learn physically-consistent reduced-order dynamic parameters that accurately describe the original high-dimensional system behavior. This is achieved by building on recent advances in model-order reduction and by adopting a Riemannian perspective to jointly learn a non-linear structure-preserving latent space and the associated low-dimensional dynamics. Our approach enables accurate long-term predictions of the high-dimensional dynamics of rigid and deformable systems with increased data efficiency by inferring interpretable and physically-plausible reduced Lagrangian models. Copyright © 2024, The Authors. All rights reserved.

关键词： Lagrange multipliers

Quasi-static Soft Fixture Analysis of Rigid and Deformable Objects

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Quasi-static Soft Fixture Analysis of Rigid and Deformable O...

IEEE International Conference on robotics and Automation (ICRA)

作者： Yifei Dong Florian T. Pokorny Division of Robotics Perception and Learning KTH Royal Institute of Technology Stockholm Sweden

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

We present a sampling-based approach to reasoning about the caging-based manipulation of rigid and a simplified class of deformable 3D objects subject to energy constraints. Towards this end, we propose the notion of soft fixtures extending earlier work on energy-bounded caging to include a broader set of energy function constraints, such as gravitational and elastic potential energy of 3D deformable objects. Previous methods focused on establishing provably correct algorithms to compute lower bounds or analytically exact estimates of escape energy for a very restricted class of known objects with low-dimensional configuration spaces, such as planar polygons. We instead propose a practical sampling-based approach that is applicable in higher-dimensional configuration spaces, but only produces a sequence of upper-bound estimates that, however, appear to converge rapidly to actual escape energy. We present 8 simulation experiments demonstrating the applicability of our approach to various complex quasi-static manipulation scenarios. Quantitative results indicate the effectiveness of our approach in providing upper-bound estimates for escape energy in quasi-static manipulation scenarios. Two real-world experiments also show that the computed normalized escape energy estimates appear to correlate strongly with the probability of escape of an object under randomized pose perturbation 1 .

关键词： Potential energy Three-dimensional displays Heuristic algorithms Fixtures Cognition robotics and automation

Robust MITL planning under uncertain navigation times

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Robust MITL planning under uncertain navigation times

IEEE International Conference on robotics and Automation (ICRA)

作者： Alexis Linard Anna Gautier Daniel Duberg Jana Tumova KTH Royal Institute of Technology Stockholm Sweden Division of Robotics Perception and Learning

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In environments like offices, the duration of a robot’s navigation between two locations may vary over time. For instance, reaching a kitchen may take more time during lunchtime since the corridors are crowded with people heading the same way. In this work, we address the problem of routing in such environments with tasks expressed in Metric Interval Temporal Logic (MITL) – a rich robot task specification language that allows us to capture explicit time requirements. Our objective is to find a strategy that maximizes the temporal robustness of the robot’s MITL task. As the first step towards a solution, we define a Mixed-integer linear programming approach to solving the task planning problem over a Varying Weighted Transition System, where navigation durations are deterministic but vary depending on the time of day. Then, we apply this planner to optimize for MITL temporal robustness in Markov Decision Processes, where the navigation durations between physical locations are uncertain, but the time-dependent distribution over possible delays is known. Finally, we develop a receding horizon planner for Markov Decision Processes that preserves guarantees over MITL temporal robustness. We show the scalability of our planning algorithms in simulations of robotic tasks.

关键词： Navigation Markov decision processes Scalability Routing Robustness Planning Specification languages