Category Archives: Robot Task Planning

Integrating symbolic (common sense) reasoning and probabilistic planning (POMDPs) in robots

Shiqi Zhang, Piyush Khandelwal, Peter Stone, iCORPP: Interleaved commonsense reasoning and probabilistic planning on robots, Robotics and Autonomous Systems, Volume 174, 2024 DOI: 10.1016/j.robot.2023.104613.

Robot sequential decision-making in the real world is a challenge because it requires the robots to simultaneously reason about the current world state and dynamics, while planning actions to accomplish complex tasks. On the one hand, declarative languages and reasoning algorithms support representing and reasoning with commonsense knowledge. But these algorithms are not good at planning actions toward maximizing cumulative reward over a long, unspecified horizon. On the other hand, probabilistic planning frameworks, such as Markov decision processes (MDPs) and partially observable MDPs (POMDPs), support planning to achieve long-term goals under uncertainty. But they are ill-equipped to represent or reason about knowledge that is not directly related to actions. In this article, we present an algorithm, called iCORPP, to simultaneously estimate the current world state, reason about world dynamics, and construct task-oriented controllers. In this process, robot decision-making problems are decomposed into two interdependent (smaller) subproblems that focus on reasoning to “understand the world” and planning to “achieve the goal” respectively. The developed algorithm has been implemented and evaluated both in simulation and on real robots using everyday service tasks, such as indoor navigation, and dialog management. Results show significant improvements in scalability, efficiency, and adaptiveness, compared to competitive baselines including handcrafted action policies.

Monte Carlo Tree Search (MTCS) with hybrid discrete-continuous beliefs, applied to robotics

M. Barenboim, M. Shienman and V. Indelman, Monte Carlo Planning in Hybrid Belief POMDPs, IEEE Robotics and Automation Letters, vol. 8, no. 8, pp. 4410-4417, Aug. 2023 DOI: 10.1109/LRA.2023.3282773.

Real-world problems often require reasoning about hybrid beliefs, over both discrete and continuous random variables. Yet, such a setting has hardly been investigated in the context of planning. Moreover, existing online partially observable Markov decision processes (POMDPs) solvers do not support hybrid beliefs directly. In particular, these solvers do not address the added computational burden due to an increasing number of hypotheses with the planning horizon, which can grow exponentially. As part of this work, we present a novel algorithm, Hybrid Belief Monte Carlo Planning (HB-MCP) that utilizes the Monte Carlo Tree Search (MCTS) algorithm to solve a POMDP while maintaining a hybrid belief. We illustrate how the upper confidence bound (UCB) exploration bonus can be leveraged to guide the growth of hypotheses trees alongside the belief trees. We then evaluate our approach in highly aliased simulated environments where unresolved data association leads to multi-modal belief hypotheses.

A RRT-based method that addresses combined task and motion planning

Riccardo Caccavale, Alberto Finzi, A rapidly-exploring random trees approach to combined task and motion planning, Robotics and Autonomous Systems, Volume 157, 2022 DOI: 10.1016/j.robot.2022.104238.

Task and motion planning in robotics are typically addressed by separated intertwined methods. Task planners generate abstract high-level actions to be executed, while motion planners provide the associated discrete movements in the configuration space satisfying kinodynamic constraints. However, these two planning processes are strictly dependent, therefore the problem of combining task and motion planning with a uniform approach is very relevant. In this work, we tackle this issue by proposing a RRT-based method that addresses combined task and motion planning. Our approach relies on a combined metric space where both symbolic (task) and sub-symbolic (motion) spaces are represented. The associated notion of distance is then exploited by a RRT-based planner to generate a plan that includes both symbolic actions and feasible movements in the configuration space. The proposed method is assessed in several case studies provided by a real-world hospital logistic scenario, where an omni-directional mobile robot is involved in navigation and transportation tasks.

POMDP Planner that uses multiple levels of approximation to the system dynamics to reduce the number and complexity of forward simulations

Hoerger M, Kurniawati H, Elfes A. , Multilevel Monte Carlo for solving POMDPs on-line, The International Journal of Robotics Research. 2023;42(4-5):196-213 DOI: 10.1177/02783649221093658.

Planning under partial observability is essential for autonomous robots. A principled way to address such planning problems is the Partially Observable Markov Decision Process (POMDP). Although solving POMDPs is computationally intractable, substantial advancements have been achieved in developing approximate POMDP solvers in the past two decades. However, computing robust solutions for systems with complex dynamics remains challenging. Most on-line solvers rely on a large number of forward simulations and standard Monte Carlo methods to compute the expected outcomes of actions the robot can perform. For systems with complex dynamics, for example, those with non-linear dynamics that admit no closed-form solution, even a single forward simulation can be prohibitively expensive. Of course, this issue exacerbates for problems with long planning horizons. This paper aims to alleviate the above difficulty. To this end, we propose a new on-line POMDP solver, called Multilevel POMDP Planner\u2009(MLPP), that combines the commonly known Monte-Carlo-Tree-Search with the concept of Multilevel Monte Carlo to speed up our capability in generating approximately optimal solutions for POMDPs with complex dynamics. Experiments on four different problems involving torque control, navigation and grasping indicate that MLPP\u2009substantially outperforms state-of-the-art POMDP solvers.

How plans influence sensors

McFassel G, Shell DA. Reactivity and statefulness: Action-based sensors, plans, and necessary state. The International Journal of Robotics Research. 2023;42(6):385-411 DOI: 10.1177/02783649221078874.

Typically to a roboticist, a plan is the outcome of other work, a synthesized object that realizes ends defined by some problem; plans qua plans are seldom treated as first-class objects of study. Plans designate functionality: a plan can be viewed as defining a robot\u2019s behavior throughout its execution. This informs and reveals many other aspects of the robot\u2019s design, including: necessary sensors and action choices, history, state, task structure, and how to define progress. Interrogating sets of plans helps in comprehending the ways in which differing executions influence the interrelationships between these various aspects. Revisiting Erdmann\u2019s theory of action-based sensors, a classical approach for characterizing fundamental information requirements, we show how plans (in their role of designating behavior) influence sensing requirements. Using an algorithm for enumerating plans, we examine how some plans for which no action-based sensor exists can be transformed into sets of sensors through the identification and handling of features that preclude the existence of action-based sensors. We are not aware of those obstructing features having been previously identified. Action-based sensors may be treated as standalone reactive plans; we relate them to the set of all possible plans through a lattice structure. This lattice reveals a boundary between plans with action-based sensors and those without. Some plans, specifically those that are not reactive plans and require some notion of internal state, can never have associated action-based sensors. Even so, action-based sensors can serve as a framework to explore and interpret how such plans make use of state.

Analysis of the under-optimality of path lengths when path planning is carried out on a grid instead of the continuous world

James P. Bailey, Alex Nash, Craig A. Tovey, Sven Koenig, Path-length analysis for grid-based path planning, Artificial Intelligence, Volume 301, 2021, DOI: 10.1016/j.artint.2021.103560.

In video games and robotics, one often discretizes a continuous 2D environment into a regular grid with blocked and unblocked cells and then finds shortest paths for the agents on the resulting grid graph. Shortest grid paths, of course, are not necessarily true shortest paths in the continuous 2D environment. In this article, we therefore study how much longer a shortest grid path can be than a corresponding true shortest path on all regular grids with blocked and unblocked cells that tessellate continuous 2D environments. We study 5 different vertex connectivities that result from both different tessellations and different definitions of the neighbors of a vertex. Our path-length analysis yields either tight or asymptotically tight worst-case bounds in a unified framework. Our results show that the percentage by which a shortest grid path can be longer than a corresponding true shortest path decreases as the vertex connectivity increases. Our path-length analysis is topical because it determines the largest path-length reduction possible for any-angle path-planning algorithms (and thus their benefit), a class of path-planning algorithms in artificial intelligence and robotics that has become popular.

Classical task planning at an abstract level for achieving good low level motion planning under uncertainty

Antony Thomas, Fulvio Mastrogiovanni, Marco Baglietto, MPTP: Motion-planning-aware task planning for navigation in belief space, . Robotics and Autonomous Systems, Volume 141, 2021 DOI: 10.1016/j.robot.2021.103786.

We present an integrated Task-Motion Planning (TMP) framework for navigation in large-scale environments. Of late, TMP for manipulation has attracted significant interest resulting in a proliferation of different approaches. In contrast, TMP for navigation has received considerably less attention. Autonomous robots operating in real-world complex scenarios require planning in the discrete (task) space and the continuous (motion) space. In knowledge-intensive domains, on the one hand, a robot has to reason at the highest-level, for example, the objects to procure, the regions to navigate to in order to acquire them; on the other hand, the feasibility of the respective navigation tasks have to be checked at the execution level. This presents a need for motion-planning-aware task planners. In this paper, we discuss a probabilistically complete approach that leverages this task-motion interaction for navigating in large knowledge-intensive domains, returning a plan that is optimal at the task-level. The framework is intended for motion planning under motion and sensing uncertainty, which is formally known as belief space planning. The underlying methodology is validated in simulation, in an office environment and its scalability is tested in the larger Willow Garage world. A reasonable comparison with a work that is closest to our approach is also provided. We also demonstrate the adaptability of our approach by considering a building floor navigation domain. Finally, we also discuss the limitations of our approach and put forward suggestions for improvements and future work.

A new contribution along the DESPOT line focused on hybrid CPU+GPU platforms

Cai P, Luo Y, Hsu D, Lee WS., HyP-DESPOT: A hybrid parallel algorithm for online planning under uncertainty, The International Journal of Robotics Research. 2021;40(2-3):558-573, DOI: 10.1177/0278364920937074.

Robust planning under uncertainty is critical for robots in uncertain, dynamic environments, but incurs high computational cost. State-of-the-art online search algorithms, such as DESPOT, have vastly improved the computational efficiency of planning under uncertainty and made it a valuable tool for robotics in practice. This work takes one step further by leveraging both CPU and GPU parallelization in order to achieve real-time online planning performance for complex tasks with large state, action, and observation spaces. Specifically, Hybrid Parallel DESPOT (HyP-DESPOT) is a massively parallel online planning algorithm that integrates CPU and GPU parallelism in a multi-level scheme. It performs parallel DESPOT tree search by simultaneously traversing multiple independent paths using multi-core CPUs; it performs parallel Monte Carlo simulations at the leaf nodes of the search tree using GPUs. HyP-DESPOT provably converges in finite time under moderate conditions and guarantees near-optimality of the solution. Experimental results show that HyP-DESPOT speeds up online planning by up to a factor of several hundred in several challenging robotic tasks in simulation, compared with the original DESPOT algorithm. It also exhibits real-time performance on a robot vehicle navigating among many pedestrians.

Including the models into the state of a POMDP for learning them (using POMCPs in a robotic application)

Akinobu Hayashi, Dirk Ruiken, Tadaaki Hasegawa, Christian Goerick, Reasoning about uncertain parameters and agent behaviors through encoded experiences and belief planning, Artificial Intelligence, Volume 280, 2020 DOI: 10.1016/j.artint.2019.103228.

Robots are expected to handle increasingly complex tasks. Such tasks often include interaction with objects or collaboration with other agents. One of the key challenges for reasoning in such situations is the lack of accurate models that hinders the effectiveness of planners. We present a system for online model adaptation that continuously validates and improves models while solving tasks with a belief space planner. We employ the well known online belief planner POMCP. Particles are used to represent hypotheses about the current state and about models of the world. They are sufficient to configure a simulator to provide transition and observation models. We propose an enhanced particle reinvigoration process that leverages prior experiences encoded in a recurrent neural network (RNN). The network is trained through interaction with a large variety of object and agent parametrizations. The RNN is combined with a mixture density network (MDN) to process the current history of observations in order to propose suitable particles and models parametrizations. The proposed method also ensures that newly generated particles are consistent with the current history. These enhancements to the particle reinvigoration process help alleviate problems arising from poor sampling quality in large state spaces and enable handling of dynamics with discontinuities. The proposed approach can be applied to a variety of domains depending on what uncertainty the decision maker needs to reason about. We evaluate the approach with experiments in several domains and compare against other state-of-the-art methods. Experiments are done in a collaborative multi-agent and a single agent object manipulation domain. The experiments are performed both in simulation and on a real robot. The framework handles reasoning with uncertain agent behaviors and with unknown object and environment parametrizations well. The results show good performance and indicate that the proposed approach can improve existing state-of-the-art methods.

Use of Markov Decision Processes to select tasks in a service mobile robots

Lacerda, B., Faruq, F., Parker, D., & Hawes, N., Probabilistic planning with formal performance guarantees for mobile service robots, The International Journal of Robotics Research, DOI: 10.1177/0278364919856695.

We present a framework for mobile service robot task planning and execution, based on the use of probabilistic verification techniques for the generation of optimal policies with attached formal performance guarantees. Our approach is based on a Markov decision process model of the robot in its environment, encompassing a topological map where nodes represent relevant locations in the environment, and a range of tasks that can be executed in different locations. The navigation in the topological map is modeled stochastically for a specific time of day. This is done by using spatio-temporal models that provide, for a given time of day, the probability of successfully navigating between two topological nodes, and the expected time to do so. We then present a methodology to generate cost optimal policies for tasks specified in co-safe linear temporal logic. Our key contribution is to address scenarios in which the task may not be achievable with probability one. We introduce a task progression function and present an approach to generate policies that are formally guaranteed to, in decreasing order of priority: maximize the probability of finishing the task; maximize progress towards completion, if this is not possible; and minimize the expected time or cost required. We illustrate and evaluate our approach with a scalability evaluation in a simulated scenario, and report on its implementation in a robot performing service tasks in an office environment for long periods of time.