A Novel Framework for Informed Manipulation Planning
In contrast to humans who use many types of manipulation to accomplish daily tasks and can easily sequence and execute pertinent actions, robots are confined to simple tasks that are often painstakingly broken down by the humans who operate those robots. Towards the goal of increasing robot autonomy, this project will extend the capabilities of manipulation planners. For the purposes of this research, manipulation planning is the domain in-between classical motion planning and what is often called task and motion planning, which includes temporal reasoning and high-order logics. This research adopts a constraint-centric view and defines a set of low-dimensional subspaces, or modes, amongst which the system must transition. The definition of transitions is also constraint-centric and is only possible because of the unified approach used when considering modes. The work depends on constructs from differential geometry and the use of powerful motion planners. It adopts a synergistic layered scheme where a discrete planner decides the sequence of modes while being constantly informed by a continuous planner that attempts the transitions between modes. The work will start with a specific but general type of constraints, manifold constraints, and later expand to other types. The proposed research will identify the limits of using constraints as a unifying construct in manipulation planning and in doing so, it will also allow for the incorporation of manipulation-specific primitives that can extend the framework.
This work has been supported by grant NSF RI 2008720.
Related Publications
- Z. Kingston and L. E. Kavraki, “Robowflex: Robot Motion Planning with MoveIt Made Easy,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022.
BibTeX
@inproceedings{kingston2022-robowflex, abstract = {Robowflex is a software library for robot motion planning in industrial and research applications, leveraging the popular MoveIt library and Robot Operating System (ROS) middleware. Robowflex takes advantage of the ease of motion planning with MoveIt while providing an augmented API to craft and manipulate motion planning queries within a single program. Robowflex's high-level API simplifies many common use-cases while still providing access to the underlying MoveIt library. Robowflex is particularly useful for 1) developing new motion planners, 2) evaluation of motion planners, and 3) complex problems that use motion planning (e.g., task and motion planning). Robowflex also provides visualization capabilities, integrations to other robotics libraries (e.g., DART and Tesseract), and is complimentary to many other robotics packages. With our library, the user does not need to be an expert at ROS or MoveIt in order to set up motion planning queries, extract information from results, and directly interface with a variety of software components. We provide a few example use-cases that demonstrate its efficacy.}, author = {Kingston, Zachary and Kavraki, Lydia E.}, booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems}, title = {Robowflex: Robot Motion Planning with MoveIt Made Easy}, year = {2022}, month = oct }DetailsAbstract
Robowflex is a software library for robot motion planning in industrial and research applications, leveraging the popular MoveIt library and Robot Operating System (ROS) middleware. Robowflex takes advantage of the ease of motion planning with MoveIt while providing an augmented API to craft and manipulate motion planning queries within a single program. Robowflex’s high-level API simplifies many common use-cases while still providing access to the underlying MoveIt library. Robowflex is particularly useful for 1) developing new motion planners, 2) evaluation of motion planners, and 3) complex problems that use motion planning (e.g., task and motion planning). Robowflex also provides visualization capabilities, integrations to other robotics libraries (e.g., DART and Tesseract), and is complimentary to many other robotics packages. With our library, the user does not need to be an expert at ROS or MoveIt in order to set up motion planning queries, extract information from results, and directly interface with a variety of software components. We provide a few example use-cases that demonstrate its efficacy. - K. Ren, L. E. Kavraki, and K. Hang, “Rearrangement-Based Manipulation via Kinodynamic Planning and Dynamic Planning
Horizons,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022.
BibTeX
@inproceedings{ren2022-rearrangement, abstract = {Robot manipulation in cluttered environments often requires complex and sequential rearrangement of multiple objects in order to achieve the desired reconfiguration of the target objects. Due to the sophisticated physical interactions involved in such scenarios, rearrangement-based manipulation is still limited to a small range of tasks and is especially vulnerable to physical uncertainties and perception noise. This paper presents a planning framework that leverages the efficiency of sampling-based planning approaches, and closes the manipulation loop by dynamically controlling the planning horizon. Our approach interleaves planning and execution to progressively approach the manipulation goal while correcting any errors or path deviations along the process. Meanwhile, our framework allows the definition of manipulation goals without requiring explicit goal configurations, enabling the robot to flexibly interact with all objects to facilitate the manipulation of the target ones. With extensive experiments both in simulation and on a real robot, we evaluate our framework on three manipulation tasks in cluttered environments: grasping, relocating, and sorting. In comparison with two baseline approaches, we show that our framework can significantly improve planning efficiency, robustness against physical uncertainties, and task success rate under limited time budgets.}, author = {Ren, Kejia and Kavraki, Lydia E. and Hang, Kaiyu}, booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems}, title = {Rearrangement-Based Manipulation via Kinodynamic Planning and Dynamic Planning Horizons}, year = {2022}, month = oct }DetailsAbstract
Robot manipulation in cluttered environments often requires complex and sequential rearrangement of multiple objects in order to achieve the desired reconfiguration of the target objects. Due to the sophisticated physical interactions involved in such scenarios, rearrangement-based manipulation is still limited to a small range of tasks and is especially vulnerable to physical uncertainties and perception noise. This paper presents a planning framework that leverages the efficiency of sampling-based planning approaches, and closes the manipulation loop by dynamically controlling the planning horizon. Our approach interleaves planning and execution to progressively approach the manipulation goal while correcting any errors or path deviations along the process. Meanwhile, our framework allows the definition of manipulation goals without requiring explicit goal configurations, enabling the robot to flexibly interact with all objects to facilitate the manipulation of the target ones. With extensive experiments both in simulation and on a real robot, we evaluate our framework on three manipulation tasks in cluttered environments: grasping, relocating, and sorting. In comparison with two baseline approaches, we show that our framework can significantly improve planning efficiency, robustness against physical uncertainties, and task success rate under limited time budgets. - C. Chamzas, M. Lippi, M. C. Welle, A. Varava, L. E. Kavraki, and D. Kragic, “Comparing Reconstruction-and Contrastive-based Models for Visual Task Planning,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022.
BibTeX
@inproceedings{chamzas2022-contrastive-visual-task-planning, title = {Comparing Reconstruction-and Contrastive-based Models for Visual Task Planning}, author = {Chamzas, Constantinos and Lippi, Martina and C. Welle, Michael and Varava, Anastasia and E. Kavraki, Lydia and Kragic, Danica}, booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems}, month = oct, year = {2022}, abstract = {Learning state representations enables robotic planning directly from raw observations such as images. Most methods learn state representations by utilizing losses based on the reconstruction of the raw observations from a lower-dimensional latent space. The similarity between observations in the space of images is often assumed and used as a proxy for estimating similarity between the underlying states of the system. However, observations commonly contain task-irrelevant factors of variation which are nonetheless important for reconstruction, such as varying lighting and different camera viewpoints. In this work, we define relevant evaluation metrics and perform a thorough study of different loss functions for state representation learning. We show that models exploiting task priors, such as Siamese networks with a simple contrastive loss, outperform reconstruction-based representations in visual task planning.}, keyword = {fundamentals of sampling-based motion planning} }DetailsAbstract
Learning state representations enables robotic planning directly from raw observations such as images. Most methods learn state representations by utilizing losses based on the reconstruction of the raw observations from a lower-dimensional latent space. The similarity between observations in the space of images is often assumed and used as a proxy for estimating similarity between the underlying states of the system. However, observations commonly contain task-irrelevant factors of variation which are nonetheless important for reconstruction, such as varying lighting and different camera viewpoints. In this work, we define relevant evaluation metrics and perform a thorough study of different loss functions for state representation learning. We show that models exploiting task priors, such as Siamese networks with a simple contrastive loss, outperform reconstruction-based representations in visual task planning. - Z. Kingston and L. E. Kavraki, “Scaling Multimodal Planning: Using Experience and Informing Discrete Search,” IEEE Transactions on Robotics, pp. 1–19, Aug. 2022.
BibTeX
@article{kingston2022-scaling-mmp, author = {Kingston, Zachary and Kavraki, Lydia E.}, journal = {IEEE Transactions on Robotics}, title = {Scaling Multimodal Planning: Using Experience and Informing Discrete Search}, month = aug, year = {2022}, volume = {}, number = {}, pages = {1-19}, doi = {10.1109/TRO.2022.3197080}, abstract = {Robotic manipulation is inherently continuous, but typically has an underlying discrete structure, such as if an object is grasped. Many problems like these are multi-modal, such as pick-and-place tasks where every object grasp and placement is a mode. Multi-modal problems require finding a sequence of transitions between modes - for example, a particular sequence of object picks and placements. However, many multi-modal planners fail to scale when motion planning is difficult (e.g., in clutter) or the task has a long horizon (e.g., rearrangement). This work presents solutions for multi-modal scalability in both these areas. For motion planning, we present an experience-based planning framework ALEF which reuses experience from similar modes both online and from training data. For task satisfaction, we present a layered planning approach that uses a discrete lead to bias search towards useful mode transitions, informed by weights over mode transitions. Together, these contributions enable multi-modal planners to tackle complex manipulation tasks that were previously infeasible or inefficient, and provide significant improvements in scenes with high-dimensional robots.}, keyword = {fundamentals of sampling-based motion planning} }DetailsAbstract
Robotic manipulation is inherently continuous, but typically has an underlying discrete structure, such as if an object is grasped. Many problems like these are multi-modal, such as pick-and-place tasks where every object grasp and placement is a mode. Multi-modal problems require finding a sequence of transitions between modes - for example, a particular sequence of object picks and placements. However, many multi-modal planners fail to scale when motion planning is difficult (e.g., in clutter) or the task has a long horizon (e.g., rearrangement). This work presents solutions for multi-modal scalability in both these areas. For motion planning, we present an experience-based planning framework ALEF which reuses experience from similar modes both online and from training data. For task satisfaction, we present a layered planning approach that uses a discrete lead to bias search towards useful mode transitions, informed by weights over mode transitions. Together, these contributions enable multi-modal planners to tackle complex manipulation tasks that were previously infeasible or inefficient, and provide significant improvements in scenes with high-dimensional robots. - Y. Lee, C. Chamzas, and L. E. Kavraki, “Adaptive Experience Sampling for Motion Planning using the Generator-Critic Framework,” IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 9437–9444, Jul. 2022.
BibTeX
@article{lee2022-apes, title = {Adaptive Experience Sampling for Motion Planning using the Generator-Critic Framework}, author = {Lee, Yiyuan and Chamzas, Constantinos and E. Kavraki, Lydia}, journal = {IEEE Robotics and Automation Letters}, volume = {7}, number = {4}, month = jul, year = {2022}, pages = {9437--9444}, doi = {10.1109/LRA.2022.3191803}, abstract = {Sampling-based motion planners are widely used for motion planning with high-dof robots. These planners generally rely on a uniform distribution to explore the search space. Recent work has explored learning biased sampling distributions to improve the time efficiency of these planners. However, learning such distributions is challenging, since there is no direct connection between the choice of distributions and the performance of the downstream planner. To alleviate this challenge, this paper proposes APES, a framework that learns sampling distributions optimized directly for the planner's performance. This is done using a critic, which serves as a differentiable surrogate objective modeling the planner's performance - thus allowing gradients to circumvent the non-differentiable planner. Leveraging the differentiability of the critic, we train a generator, which outputs sampling distributions optimized for the given problem instance. We evaluate APES on a series of realistic and challenging high-dof manipulation problems in simulation. Our experimental results demonstrate that APES can learn high-quality distributions that improve planning performance more than other biased sampling baselines.}, keyword = {fundamentals of sampling-based motion planning}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)} }DetailsAbstract
Sampling-based motion planners are widely used for motion planning with high-dof robots. These planners generally rely on a uniform distribution to explore the search space. Recent work has explored learning biased sampling distributions to improve the time efficiency of these planners. However, learning such distributions is challenging, since there is no direct connection between the choice of distributions and the performance of the downstream planner. To alleviate this challenge, this paper proposes APES, a framework that learns sampling distributions optimized directly for the planner’s performance. This is done using a critic, which serves as a differentiable surrogate objective modeling the planner’s performance - thus allowing gradients to circumvent the non-differentiable planner. Leveraging the differentiability of the critic, we train a generator, which outputs sampling distributions optimized for the given problem instance. We evaluate APES on a series of realistic and challenging high-dof manipulation problems in simulation. Our experimental results demonstrate that APES can learn high-quality distributions that improve planning performance more than other biased sampling baselines. - C. Quintero-Peña, C. Chamzas, Z. Sun, V. Unhelkar, and L. E. Kavraki, “Human-Guided Motion Planning in Partially Observable Environments,” in 2022 International Conference on Robotics and Automation (ICRA), 2022, pp. 7226–7232.
BibTeX
@inproceedings{quintero-chamzas2022-blind, title = {Human-Guided Motion Planning in Partially Observable Environments}, author = {Quintero-Pe{\~n}a, Carlos and Chamzas, Constantinos and Sun, Zhanyi and Unhelkar, Vaibhav and Kavraki, Lydia E}, booktitle = {2022 International Conference on Robotics and Automation (ICRA)}, month = may, year = {2022}, pages = {7226--7232}, doi = {10.1109/ICRA46639.2022.9811893}, abstract = {Motion planning is a core problem in robotics, with a range of existing methods aimed to address its diverse set of challenges. However, most existing methods rely on complete knowledge of the robot environment; an assumption that seldom holds true due to inherent limitations of robot perception. To enable tractable motion planning for high-DOF robots under partial observability, we introduce BLIND, an algorithm that leverages human guidance. BLIND utilizes inverse reinforcement learning to derive motion-level guidance from human critiques. The algorithm overcomes the computational challenge of reward learning for high-DOF robots by projecting the robot’s continuous configuration space to a motion-planner-guided discrete task model. The learned reward is in turn used as guidance to generate robot motion using a novel motion planner. We demonstrate BLIND using the Fetch robot an dperform two simulation experiments with partial observability. Our experiments demonstrate that, despite the challenge of partial observability and high dimensionality, BLIND is capable of generating safe robot motion and outperforms baselines on metrics of teaching efficiency, success rate, and path quality.}, keyword = {uncertainty}, publisher = {IEEE} }DetailsAbstract
Motion planning is a core problem in robotics, with a range of existing methods aimed to address its diverse set of challenges. However, most existing methods rely on complete knowledge of the robot environment; an assumption that seldom holds true due to inherent limitations of robot perception. To enable tractable motion planning for high-DOF robots under partial observability, we introduce BLIND, an algorithm that leverages human guidance. BLIND utilizes inverse reinforcement learning to derive motion-level guidance from human critiques. The algorithm overcomes the computational challenge of reward learning for high-DOF robots by projecting the robot’s continuous configuration space to a motion-planner-guided discrete task model. The learned reward is in turn used as guidance to generate robot motion using a novel motion planner. We demonstrate BLIND using the Fetch robot an dperform two simulation experiments with partial observability. Our experiments demonstrate that, despite the challenge of partial observability and high dimensionality, BLIND is capable of generating safe robot motion and outperforms baselines on metrics of teaching efficiency, success rate, and path quality. - T. Pan, A. M. Wells, R. Shome, and L. E. Kavraki, “Failure is an option: Task and Motion Planning with Failing Executions,” in 2022 International Conference on Robotics and Automation (ICRA), 2022, pp. 1947–1953.
BibTeX
@inproceedings{pan2022failing-execution, title = {Failure is an option: Task and Motion Planning with Failing Executions}, author = {Pan, Tianyang and Wells, Andrew M. and Shome, Rahul and Kavraki, Lydia E.}, booktitle = {2022 International Conference on Robotics and Automation (ICRA)}, month = may, year = {2022}, pages = {1947--1953}, doi = {10.1109/ICRA46639.2022.9812273}, abstract = {Future robotic deployments will require robots to be able to repeatedly solve a variety of tasks in application domains. Task and motion planning addresses complex robotic problems that combine discrete reasoning over states and actions and geometric interactions during action executions. Moving beyond deterministic settings, stochastic actions can be handled by modeling the problem as a Markov Decision Process. The underlying probabilities however are typically hard to model since failures might be caused by hardware imperfections, sensing noise, or physical interactions. We propose a framework to address a task and motion planning setting where actions can fail during execution. To achieve a task goal actions need to be computed and executed despite failures. The robot has to infer which actions are robust and for each new problem effectively choose a solution that reduces expected execution failures. The key idea is to continually recover and refine the underlying beliefs associated with actions across multiple different problems in the domain. Our proposed method can find solutions that reduce the expected number of discrete, executed actions. Results in physics-based simulation indicate that our method outperforms baseline replanning strategies to deal with failing executions}, keyword = {task and motion planning}, publisher = {IEEE} }DetailsAbstract
Future robotic deployments will require robots to be able to repeatedly solve a variety of tasks in application domains. Task and motion planning addresses complex robotic problems that combine discrete reasoning over states and actions and geometric interactions during action executions. Moving beyond deterministic settings, stochastic actions can be handled by modeling the problem as a Markov Decision Process. The underlying probabilities however are typically hard to model since failures might be caused by hardware imperfections, sensing noise, or physical interactions. We propose a framework to address a task and motion planning setting where actions can fail during execution. To achieve a task goal actions need to be computed and executed despite failures. The robot has to infer which actions are robust and for each new problem effectively choose a solution that reduces expected execution failures. The key idea is to continually recover and refine the underlying beliefs associated with actions across multiple different problems in the domain. Our proposed method can find solutions that reduce the expected number of discrete, executed actions. Results in physics-based simulation indicate that our method outperforms baseline replanning strategies to deal with failing executions - C. Chamzas, C. Quintero-Peña, Z. Kingston, A. Orthey, D. Rakita, M. Gleicher, M. Toussaint, and L. E. Kavraki, “MotionBenchMaker: A Tool to Generate and Benchmark Motion Planning Datasets,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 882–889, Apr. 2022.
BibTeX
@article{chamzas2022-motion-bench-maker, title = {MotionBenchMaker: A Tool to Generate and Benchmark Motion Planning Datasets}, author = {Chamzas, Constantinos and Quintero-Pe{\~n}a, Carlos and Kingston, Zachary and Orthey, Andreas and Rakita, Daniel and Gleicher, Michael and Toussaint, Marc and E. Kavraki, Lydia}, journal = {IEEE Robotics and Automation Letters}, month = apr, year = {2022}, volume = {7}, number = {2}, pages = {882--889}, doi = {10.1109/LRA.2021.3133603}, abstract = {Recently, there has been a wealth of development in motion planning for robotic manipulationnew motion planners are continuously proposed, each with its own unique set of strengths and weaknesses. However, evaluating these new planners is challenging, and researchers often create their own ad-hoc problems for benchmarking, which is time-consuming, prone to bias, and does not directly compare against other state-of-the-art planners. We present MotionBenchMaker, an open-source tool to generate benchmarking datasets for realistic robot manipulation problems. MotionBenchMaker is designed to be an extensible, easy-to-use tool that allows users to both generate datasets and benchmark them by comparing motion planning algorithms. Empirically, we show the benefit of using MotionBenchMaker as a tool to procedurally generate datasets which helps in the fair evaluation of planners. We also present a suite of over 40 prefabricated datasets, with 5 different commonly used robots in 8 environments, to serve as a common ground for future motion planning research.}, issn = {2377-3766}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, url = {https://dx.doi.org/10.1109/LRA.2021.3133603} }DetailsAbstract
Recently, there has been a wealth of development in motion planning for robotic manipulationnew motion planners are continuously proposed, each with its own unique set of strengths and weaknesses. However, evaluating these new planners is challenging, and researchers often create their own ad-hoc problems for benchmarking, which is time-consuming, prone to bias, and does not directly compare against other state-of-the-art planners. We present MotionBenchMaker, an open-source tool to generate benchmarking datasets for realistic robot manipulation problems. MotionBenchMaker is designed to be an extensible, easy-to-use tool that allows users to both generate datasets and benchmark them by comparing motion planning algorithms. Empirically, we show the benefit of using MotionBenchMaker as a tool to procedurally generate datasets which helps in the fair evaluation of planners. We also present a suite of over 40 prefabricated datasets, with 5 different commonly used robots in 8 environments, to serve as a common ground for future motion planning research. - S. Sobti, R. Shome, S. Chaudhuri, and L. E. Kavraki, “A Sampling-based Motion Planning Framework for Complex Motor Actions,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and
Systems, 2021, pp. 6928–6934.
BibTeX
@inproceedings{sobti2021-complex-motor-actions, title = {{A Sampling-based Motion Planning Framework for Complex Motor Actions}}, author = {Sobti, Shlok and Shome, Rahul and Chaudhuri, Swarat and Kavraki, Lydia E.}, booktitle = {Proceedings of the {IEEE/RSJ} International Conference on Intelligent Robots and Systems}, month = sep, year = {2021}, pages = {6928--6934}, doi = {10.1109/IROS51168.2021.9636395}, abstract = {We present a framework for planning complex motor actions such as pouring or scooping from arbitrary start states in cluttered real-world scenes. Traditional approaches to such tasks use dynamic motion primitives (DMPs) learned from human demonstrations. We enhance a recently proposed state-of-the-art DMP technique capable of obstacle avoidance by including them within a novel hybrid framework. This complements DMPs with sampling-based motion planning algorithms, using the latter to explore the scene and reach promising regions from which a DMP can successfully complete the task. Experiments indicate that even obstacle-aware DMPs suffer in task success when used in scenarios which largely differ from the trained demonstration in terms of the start, goal, and obstacles. Our hybrid approach significantly outperforms obstacle-aware DMPs by successfully completing tasks in cluttered scenes for a pouring task in simulation. We further demonstrate our method on a real robot for pouring and scooping tasks.}, keyword = {Motion and Path Planning, Manipulation Planning, Learning from Demonstration} }DetailsAbstract
We present a framework for planning complex motor actions such as pouring or scooping from arbitrary start states in cluttered real-world scenes. Traditional approaches to such tasks use dynamic motion primitives (DMPs) learned from human demonstrations. We enhance a recently proposed state-of-the-art DMP technique capable of obstacle avoidance by including them within a novel hybrid framework. This complements DMPs with sampling-based motion planning algorithms, using the latter to explore the scene and reach promising regions from which a DMP can successfully complete the task. Experiments indicate that even obstacle-aware DMPs suffer in task success when used in scenarios which largely differ from the trained demonstration in terms of the start, goal, and obstacles. Our hybrid approach significantly outperforms obstacle-aware DMPs by successfully completing tasks in cluttered scenes for a pouring task in simulation. We further demonstrate our method on a real robot for pouring and scooping tasks. - R. Shome and L. E. Kavraki, “Asymptotically Optimal Kinodynamic Planning Using Bundles of Edges,” in 2021 IEEE International Conference on Robotics and Automation (ICRA), 2021, pp. 9988–9994.
BibTeX
@inproceedings{shome2021-bundle-of-edges, title = {{Asymptotically Optimal Kinodynamic Planning Using Bundles of Edges}}, author = {Shome, Rahul and Kavraki, Lydia E.}, booktitle = {2021 IEEE International Conference on Robotics and Automation (ICRA)}, month = jun, year = {2021}, pages = {9988--9994}, doi = {10.1109/ICRA48506.2021.9560836}, abstract = {Using sampling to estimate the connectivity of high-dimensional configuration spaces has been the theoretical underpinning for effective sampling-based motion planners. Typical strategies either build a roadmap, or a tree as the underlying search structure that connects sampled configurations, with a focus on guaranteeing completeness and optimality as the number of samples tends to infinity. Roadmap-based planners allow preprocessing the space, and can solve multiple kinematic motion planning problems, but need a steering function to connect pairwise-states. Such steering functions are difficult to define for kinodynamic systems, and limit the applicability of roadmaps to motion planning problems with dynamical systems. Recent advances in the analysis of single-query tree-based planners has shown that forward search trees based on random propagations are asymptotically optimal. The current work leverages these recent results and proposes a multi-query framework for kinodynamic planning. Bundles of kinodynamic edges can be sampled to cover the state space before the query arrives. Then, given a motion planning query, the connectivity of the state space reachable from the start can be recovered from a forward search tree reasoning about a local neighborhood of the edge bundle from each tree node. The work demonstrates theoretically that considering any constant radial neighborhood during this process is sufficient to guarantee asymptotic optimality. Experimental validation in five and twelve dimensional simulated systems also highlights the ability of the proposed edge bundles to express high-quality kinodynamic solutions. Our approach consistently finds higher quality solutions compared to SST, and RRT, often with faster initial solution times. The strategy of sampling kinodynamic edges is demonstrated to be a promising new paradigm.}, keyword = {Motion Planning, Asymptotic Optimality, Kinodynamic Planning, Bundle Of Edges} }DetailsAbstract
Using sampling to estimate the connectivity of high-dimensional configuration spaces has been the theoretical underpinning for effective sampling-based motion planners. Typical strategies either build a roadmap, or a tree as the underlying search structure that connects sampled configurations, with a focus on guaranteeing completeness and optimality as the number of samples tends to infinity. Roadmap-based planners allow preprocessing the space, and can solve multiple kinematic motion planning problems, but need a steering function to connect pairwise-states. Such steering functions are difficult to define for kinodynamic systems, and limit the applicability of roadmaps to motion planning problems with dynamical systems. Recent advances in the analysis of single-query tree-based planners has shown that forward search trees based on random propagations are asymptotically optimal. The current work leverages these recent results and proposes a multi-query framework for kinodynamic planning. Bundles of kinodynamic edges can be sampled to cover the state space before the query arrives. Then, given a motion planning query, the connectivity of the state space reachable from the start can be recovered from a forward search tree reasoning about a local neighborhood of the edge bundle from each tree node. The work demonstrates theoretically that considering any constant radial neighborhood during this process is sufficient to guarantee asymptotic optimality. Experimental validation in five and twelve dimensional simulated systems also highlights the ability of the proposed edge bundles to express high-quality kinodynamic solutions. Our approach consistently finds higher quality solutions compared to SST, and RRT, often with faster initial solution times. The strategy of sampling kinodynamic edges is demonstrated to be a promising new paradigm. - C. Quintero-Peña, A. Kyrillidis, and L. E. Kavraki, “Robust Optimization-based Motion Planning for high-DOF Robots under Sensing
Uncertainty,” in 2021 IEEE International Conference on Robotics and Automation (ICRA), 2021, pp. 9724–9730.
BibTeX
@inproceedings{quintero2021-robust-motion-planning, title = {{Robust Optimization-based Motion Planning for high-DOF Robots under Sensing Uncertainty}}, author = {Quintero-Pe{\~n}a, Carlos and Kyrillidis, Anastasios and Kavraki, Lydia E.}, booktitle = {2021 IEEE International Conference on Robotics and Automation (ICRA)}, month = jun, year = {2021}, pages = {9724--9730}, doi = {10.1109/ICRA48506.2021.9560917}, abstract = {Motion planning for high degree-of-freedom (DOF) robots is challenging, especially when acting in complex environments under sensing uncertainty. While there is significant work on how to plan under state uncertainty for low-DOF robots, existing methods cannot be easily translated into the high-DOF case, due to the complex geometry of the robot's body and its environment. In this paper, we present a method that enhances optimization-based motion planners to produce robust trajectories for high-DOF robots for convex obstacles. Our approach introduces robustness into planners that are based on sequential convex programming: We reformulate each convex subproblem as a robust optimization problem that ``protects'' the solution against deviations due to sensing uncertainty. The parameters of the robust problem are estimated by sampling from the distribution of noisy obstacles, and performing a first-order approximation of the signed distance function. The original merit function is updated to account for the new costs of the robust formulation at every step. The effectiveness of our approach is demonstrated on two simulated experiments that involve a full body square robot, that moves in randomly generated scenes, and a 7-DOF Fetch robot, performing tabletop operations. The results show nearly zero probability of collision for a reasonable range of the noise parameters for Gaussian and Uniform uncertainty.}, keyword = {uncertainty} }DetailsAbstract
Motion planning for high degree-of-freedom (DOF) robots is challenging, especially when acting in complex environments under sensing uncertainty. While there is significant work on how to plan under state uncertainty for low-DOF robots, existing methods cannot be easily translated into the high-DOF case, due to the complex geometry of the robot’s body and its environment. In this paper, we present a method that enhances optimization-based motion planners to produce robust trajectories for high-DOF robots for convex obstacles. Our approach introduces robustness into planners that are based on sequential convex programming: We reformulate each convex subproblem as a robust optimization problem that “protects” the solution against deviations due to sensing uncertainty. The parameters of the robust problem are estimated by sampling from the distribution of noisy obstacles, and performing a first-order approximation of the signed distance function. The original merit function is updated to account for the new costs of the robust formulation at every step. The effectiveness of our approach is demonstrated on two simulated experiments that involve a full body square robot, that moves in randomly generated scenes, and a 7-DOF Fetch robot, performing tabletop operations. The results show nearly zero probability of collision for a reasonable range of the noise parameters for Gaussian and Uniform uncertainty. - E. Pairet, C. Chamzas, Y. R. Petillot, and L. E. Kavraki, “Path Planning for Manipulation Using Experience-Driven Random Trees,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 3295–3302, Apr. 2021.
BibTeX
@article{pairet2021-path-planning-for-manipulation, title = {Path Planning for Manipulation Using Experience-Driven Random Trees}, author = {Pairet, Eric and Chamzas, Constantinos and Petillot, Yvan R. and Kavraki, Lydia E.}, journal = {IEEE Robotics and Automation Letters}, month = apr, year = {2021}, volume = {6}, number = {2}, pages = {3295--3302}, doi = {10.1109/lra.2021.3063063}, abstract = {Robotic systems may frequently come across similar manipulation planning problems that result in similar motion plans. Instead of planning each problem from scratch, it is preferable to leverage previously computed motion plans, i.e., experiences, to ease the planning. Different approaches have been proposed to exploit prior information on novel task instances. These methods, however, rely on a vast repertoire of experiences and fail when none relates closely to the current problem. Thus, an open challenge is the ability to generalise prior experiences to task instances that do not necessarily resemble the prior. This work tackles the above challenge with the proposition that experiences are "decomposable" and "malleable", i.e., parts of an experience are suitable to relevantly explore the connectivity of the robot-task space even in non-experienced regions. Two new planners result from this insight: experience-driven random trees (ERT) and its bi-directional version ERTConnect. These planners adopt a tree sampling-based strategy that incrementally extracts and modulates parts of a single path experience to compose a valid motion plan. We demonstrate our method on task instances that significantly differ from the prior experiences, and compare with related state-of-the-art experience-based planners. While their repairing strategies fail to generalise priors of tens of experiences, our planner, with a single experience, significantly outperforms them in both success rate and planning time. Our planners are implemented and freely available in the Open Motion Planning Library.}, issn = {2377-3774}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, url = {http://dx.doi.org/10.1109/LRA.2021.3063063} }DetailsAbstract
Robotic systems may frequently come across similar manipulation planning problems that result in similar motion plans. Instead of planning each problem from scratch, it is preferable to leverage previously computed motion plans, i.e., experiences, to ease the planning. Different approaches have been proposed to exploit prior information on novel task instances. These methods, however, rely on a vast repertoire of experiences and fail when none relates closely to the current problem. Thus, an open challenge is the ability to generalise prior experiences to task instances that do not necessarily resemble the prior. This work tackles the above challenge with the proposition that experiences are "decomposable" and "malleable", i.e., parts of an experience are suitable to relevantly explore the connectivity of the robot-task space even in non-experienced regions. Two new planners result from this insight: experience-driven random trees (ERT) and its bi-directional version ERTConnect. These planners adopt a tree sampling-based strategy that incrementally extracts and modulates parts of a single path experience to compose a valid motion plan. We demonstrate our method on task instances that significantly differ from the prior experiences, and compare with related state-of-the-art experience-based planners. While their repairing strategies fail to generalise priors of tens of experiences, our planner, with a single experience, significantly outperforms them in both success rate and planning time. Our planners are implemented and freely available in the Open Motion Planning Library.