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1. Compositional planning in Markov decision processes: Temporal abstraction meets generalized logic composition

   Liu, Xuan; Fu, Jie (July 2019, Annual American Control Conference)

   In hierarchical planning for Markov decision processes (MDPs), temporal abstraction allows planning with macro-actions that take place at different time scale in the form of sequential composition. In this paper, we propose a novel approach to compositional reasoning and hierarchical planning for MDPs under co-safe temporal logic constraints. In addition to sequential composition, we introduce a composition of policies based on generalized logic composition: Given sub-policies for sub-tasks and a new task expressed as logic compositions of subtasks, a semi-optimal policy, which is optimal in planning with only sub-policies, can be obtained by simply composing sub-polices. Thus, a synthesis algorithm is developed to compute optimal policies efficiently by planning with primitive actions, policies for sub-tasks, and the compositions of sub-policies, for maximizing the probability of satisfying constraints specified in the fragment of co-safe temporal logic. We demonstrate the correctness and efficiency of the proposed method in stochastic planning examples with a single agent and multiple task specifications. 

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2. Parametrized topological complexity of sphere bundles

   https://doi.org/10.12775/TMNA.2022.049  

   Farber, Michael; Weinberger, Shmuel Weinberger (January 2023, Topological Methods in Nonlinear Analysis)

   Parametrized motion planning algorithms \cite{CFW} have high degree of flexibility and universality, they can work under a variety of external conditions, which are viewed as parameters and form part of the input of the algorithm. In this paper we analyse the parameterized motion planning problem in the case of sphere bundles.Our main results provide upper and lower bounds for the parametrized topological complexity; the upper bounds typically involve sectional categories of the associated fibrations and the lower bounds are given in terms of characteristic classes and their properties. We explicitly compute the parametrized topological complexity in many examples and show that it may assume arbitrarily large values. 

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3. Learning Sampling Distributions Using Local 3D Workspace Decompositions for Motion Planning in High Dimensions

   Chamzas, Constantinos; Kingston, Zachary; Quintero-Pena, Carlos; Shrivastava, Anshumali; Kavraki, Lydia E. (April 2022, Proceedings of the International Conference on Robotics and Automation 2021)

   null (Ed.)

   Earlier work has shown that reusing experience from prior motion planning problems can improve the efficiency of similar, future motion planning queries. However, for robots with many degrees-of-freedom, these methods exhibit poor generalization across different environments and often require large datasets that are impractical to gather. We present SPARK and FLAME, two experience-based frameworks for sampling-based planning applicable to complex manipulators in 3D environments. Both combine samplers associated with features from a workspace decomposition into a global biased sampling distribution. SPARK decomposes the environment based on exact geometry while FLAME is more general, and uses an octree-based decomposition obtained from sensor data. We demonstrate the effectiveness of SPARK and FLAME on a real and simulated Fetch robot tasked with challenging pick-and-place manipulation problems. Our approaches can be trained incrementally and significantly improve performance with only a handful of examples, generalizing better over diverse tasks and environments as compared to prior approaches. 

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4. Mediating Between Contact Feasibility and Robustness of Trajectory Optimization Through Chance Complementarity Constraints

   https://doi.org/10.3389/frobt.2021.785925  

   Drnach, Luke; Zhang, John Z.; Zhao, Ye (January 2022, Frontiers in Robotics and AI)

   As robots move from the laboratory into the real world, motion planning will need to account for model uncertainty and risk. For robot motions involving intermittent contact, planning for uncertainty in contact is especially important, as failure to successfully make and maintain contact can be catastrophic. Here, we model uncertainty in terrain geometry and friction characteristics, and combine a risk-sensitive objective with chance constraints to provide a trade-off between robustness to uncertainty and constraint satisfaction with an arbitrarily high feasibility guarantee. We evaluate our approach in two simple examples: a push-block system for benchmarking and a single-legged hopper. We demonstrate that chance constraints alone produce trajectories similar to those produced using strict complementarity constraints; however, when equipped with a robust objective, we show the chance constraints can mediate a trade-off between robustness to uncertainty and strict constraint satisfaction. Thus, our study may represent an important step towards reasoning about contact uncertainty in motion planning. 

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5. FogROS 2: An Adaptive and Extensible Platform for Cloud and Fog Robotics Using ROS 2

   Ichnowski, Jeffrey; Chen, Kaiyuan; Dharmarajan, Karthik; Adebola, Simeon; Danielczuk, Michael; Mayoral-Vilches, V; Zhan, Hugo; Xu, Derek; Ghassemi, Ramtin; Kubiatowicz, John; et al (May 2023, Proceedings IEEE International Conference on Robotics and Automation)

   Mobility, power, and price points often dictate that robots do not have sufficient computing power on board to run contemporary robot algorithms at desired rates. Cloud computing providers such as AWS, GCP, and Azure offer immense computing power on demand, but tapping into that power from a robot is non-trivial. We present FogROS2, an open-source platform to facilitate cloud and fog robotics that is compatible with the emerging Robot Operating System 2 (ROS 2) standard. FogROS2 is completely redesigned and distinct from its predecessor FogROS1 in 9 ways, and has lower latency, overhead, and startup times; improved usability, and additional automa-tion, such as region and computer type selection. Additionally, FogROS2 was added to the official distribution of ROS 2, gaining performance, timing, and additional improvements associated with ROS 2. In examples, FogROS2 reduces SLAM latency by 50 %, reduces grasp planning time from 14 s to 1.2 s, and speeds up motion planning 28x. When compared to FogROS1, FogROS2 reduces network utilization by up to 3.8x, improves startup time by 63 %, and network round-trip latency by 97 %for images using video compression. The source code, examples, and documentation for FogROS2 are available at https://github.com/BerkeleyAutomation/FogROS2, and is available through the official ROS 2 repository at https://index.ros.org/p/fogros2/ 

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