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Maxim Likhachev

Publications and source records attributed to Maxim Likhachev.

At least 73 records · Page 4Linked to original sources
arXiv2021

Search-based Path Planning for a High Dimensional Manipulator in Cluttered Environments Using Optimization-based Primitives

In this work we tackle the path planning problem for a 21-dimensional snake robot-like manipulator, navigating a cluttered gas turbine for the purposes of inspection. Heuristic search based approaches are effective planning strategies for common manipulation domains. However, their performance on high dimensional systems is heavily reliant on the effectiveness of the action space and the heuristics chosen. The complex nature of our system, reachability constraints, and highly cluttered turbine environment renders naive choices of action spaces and heuristics ineffective. To this extent we have developed i) a methodology for dynamically generating actions based on online optimization that help the robot navigate narrow spaces, ii) a technique for lazily generating these computationally expensive optimization actions to effectively utilize resources, and iii) heuristics that reason about the homotopy classes induced by the blades of the turbine in the robot workspace and a Multi-Heuristic framework which guides the search along the relevant classes. The impact of our contributions is presented through an experimental study in simulation, where the 21 DOF manipulator navigates towards regions of inspection within a turbine.

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arXiv2021

Waiting Tables as a Robot Planning Problem

We present how we formalize the waiting tables task in a restaurant as a robot planning problem. This formalization was used to test our recently developed algorithms that allow for optimal planning for achieving multiple independent tasks that are partially observable and evolve over time [1], [2].

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arXiv2021

Euclidean Distance-Optimal Post-Processing of Grid-Based Paths

Paths planned over grids can often be suboptimal in an Euclidean space and contain a large number of unnecessary turns. Consequently, researchers have looked into post-processing techniques to improve the paths after they are planned. In this paper, we propose a novel post-processing technique, called Homotopic Visibility Graph Planning (HVG) which differentiates itself from existing post-processing methods in that it is guaranteed to shorten the path such that it is at least as short as the provably shortest path that lies within the same topological class as the initially computed path. We propose the algorithm, provide proofs and compare it experimentally against other post-processing methods and any-angle planning algorithms.

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arXiv2021

Interleaving Graph Search and Trajectory Optimization for Aggressive Quadrotor Flight

Quadrotors can achieve aggressive flight by tracking complex maneuvers and rapidly changing directions. Planning for aggressive flight with trajectory optimization could be incredibly fast, even in higher dimensions, and can account for dynamics of the quadrotor, however, only provides a locally optimal solution. On the other hand, planning with discrete graph search can handle non-convex spaces to guarantee optimality but suffers from exponential complexity with the dimension of search. We introduce a framework for aggressive quadrotor trajectory generation with global reasoning capabilities that combines the best of trajectory optimization and discrete graph search. Specifically, we develop a novel algorithmic framework that interleaves these two methods to complement each other and generate trajectories with provable guarantees on completeness up to discretization. We demonstrate and quantitatively analyze the performance of our algorithm in challenging simulation environments with narrow gaps that create severe attitude constraints and push the dynamic capabilities of the quadrotor. Experiments show the benefits of the proposed algorithmic framework over standalone trajectory optimization and graph search-based planning techniques for aggressive quadrotor flight.

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arXiv2021

Alternative Paths Planner (APP) for Provably Fixed-time Manipulation Planning in Semi-structured Environments

In many applications, including logistics and manufacturing, robot manipulators operate in semi-structured environments alongside humans or other robots. These environments are largely static, but they may contain some movable obstacles that the robot must avoid. Manipulation tasks in these applications are often highly repetitive, but require fast and reliable motion planning capabilities, often under strict time constraints. Existing preprocessing-based approaches are beneficial when the environments are highly-structured, but their performance degrades in the presence of movable obstacles, since these are not modelled a priori. We propose a novel preprocessing-based method called Alternative Paths Planner (APP) that provides provably fixed-time planning guarantees in semi-structured environments. APP plans a set of alternative paths offline such that, for any configuration of the movable obstacles, at least one of the paths from this set is collision-free. During online execution, a collision-free path can be looked up efficiently within a few microseconds. We evaluate APP on a 7 DoF robot arm in semi-structured domains of varying complexity and demonstrate that APP is several orders of magnitude faster than state-of-the-art motion planners for each domain. We further validate this approach with real-time experiments on a robotic manipulator.

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arXiv2021

Learning Optimal Decision Making for an Industrial Truck Unloading Robot using Minimal Simulator Runs

Consider a truck filled with boxes of varying size and unknown mass and an industrial robot with end-effectors that can unload multiple boxes from any reachable location. In this work, we investigate how would the robot with the help of a simulator, learn to maximize the number of boxes unloaded by each action. Most high-fidelity robotic simulators like ours are time-consuming. Therefore, we investigate the above learning problem with a focus on minimizing the number of simulation runs required. The optimal decision-making problem under this setting can be formulated as a multi-class classification problem. However, to obtain the outcome of any action requires us to run the time-consuming simulator, thereby restricting the amount of training data that can be collected. Thus, we need a data-efficient approach to learn the classifier and generalize it with a minimal amount of data. A high-fidelity physics-based simulator is common in general for complex manipulation tasks involving multi-body interactions. To this end, we train an optimal decision tree as the classifier, and for each branch of the decision tree, we reason about the confidence in the decision using a Probably Approximately Correct (PAC) framework to determine whether more simulator data will help reach a certain confidence level. This provides us with a mechanism to evaluate when simulation can be avoided for certain decisions, and when simulation will improve the decision making. For the truck unloading problem, our experiments show that a significant reduction in simulator runs can be achieved using the proposed method as compared to naively running the simulator to collect data to train equally performing decision trees.

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arXiv2021

Provably Constant-time Planning and Replanning for Real-time Grasping Objects off a Conveyor Belt

In warehouse and manufacturing environments, manipulation platforms are frequently deployed at conveyor belts to perform pick and place tasks. Because objects on the conveyor belts are moving, robots have limited time to pick them up. This brings the requirement for fast and reliable motion planners that could provide provable real-time planning guarantees, which the existing algorithms do not provide. Besides the planning efficiency, the success of manipulation tasks relies heavily on the accuracy of the perception system which is often noisy, especially if the target objects are perceived from a distance. For fast moving conveyor belts, the robot cannot wait for a perfect estimate before it starts executing its motion. In order to be able to reach the object in time, it must start moving early on (relying on the initial noisy estimates) and adjust its motion on-the-fly in response to the pose updates from perception. We propose a planning framework that meets these requirements by providing provable constant-time planning and replanning guarantees. To this end, we first introduce and formalize a new class of algorithms called Constant-Time Motion Planning algorithms (CTMP) that guarantee to plan in constant time and within a user-defined time bound. We then present our planning framework for grasping objects off a conveyor belt as an instance of the CTMP class of algorithms.

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arXiv2020

Search-based Planning for Active Sensing in Goal-Directed Coverage Tasks

Path planning for robotic coverage is the task of determining a collision-free robot trajectory that observes all points of interest in an environment. Robots employed for such tasks are often capable of exercising active control over onboard observational sensors during navigation. In this paper, we tackle the problem of planning robot and sensor trajectories that maximize information gain in such tasks where the robot needs to cover points of interest with its sensor footprint. Search-based planners in general guarantee completeness and provable bounds on suboptimality with respect to an underlying graph discretization. However, searching for kinodynamically feasible paths in the joint space of robot and sensor state variables with standard search is computationally expensive. We propose two alternative search-based approaches to this problem. The first solves for robot and sensor trajectories independently in decoupled state spaces while maintaining a history of sensor headings during the search. The second is a two-step approach that first quickly computes a solution in decoupled state spaces and then refines it by searching its local neighborhood in the joint space for a better solution. We evaluate our approaches in simulation with a kinodynamically constrained unmanned aerial vehicle performing coverage over a 2D environment and show their benefits.

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arXiv2020

Planning and Execution using Inaccurate Models with Provable Guarantees

Models used in modern planning problems to simulate outcomes of real world action executions are becoming increasingly complex, ranging from simulators that do physics-based reasoning to precomputed analytical motion primitives. However, robots operating in the real world often face situations not modeled by these models before execution. This imperfect modeling can lead to highly suboptimal or even incomplete behavior during execution. In this paper, we propose CMAX an approach for interleaving planning and execution. CMAX adapts its planning strategy online during real-world execution to account for any discrepancies in dynamics during planning, without requiring updates to the dynamics of the model. This is achieved by biasing the planner away from transitions whose dynamics are discovered to be inaccurately modeled, thereby leading to robot behavior that tries to complete the task despite having an inaccurate model. We provide provable guarantees on the completeness and efficiency of the proposed planning and execution framework under specific assumptions on the model, for both small and large state spaces. Our approach CMAX is shown to be efficient empirically in simulated robotic tasks including 4D planar pushing, and in real robotic experiments using PR2 involving a 3D pick-and-place task where the mass of the object is incorrectly modeled, and a 7D arm planning task where one of the joints is not operational leading to discrepancy in dynamics. The video of our physical robot experiments can be found at https://youtu.be/eQmAeWIhjO8

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arXiv2020

CMAX++ : Leveraging Experience in Planning and Execution using Inaccurate Models

Given access to accurate dynamical models, modern planning approaches are effective in computing feasible and optimal plans for repetitive robotic tasks. However, it is difficult to model the true dynamics of the real world before execution, especially for tasks requiring interactions with objects whose parameters are unknown. A recent planning approach, CMAX, tackles this problem by adapting the planner online during execution to bias the resulting plans away from inaccurately modeled regions. CMAX, while being provably guaranteed to reach the goal, requires strong assumptions on the accuracy of the model used for planning and fails to improve the quality of the solution over repetitions of the same task. In this paper we propose CMAX++, an approach that leverages real-world experience to improve the quality of resulting plans over successive repetitions of a robotic task. CMAX++ achieves this by integrating model-free learning using acquired experience with model-based planning using the potentially inaccurate model. We provide provable guarantees on the completeness and asymptotic convergence of CMAX++ to the optimal path cost as the number of repetitions increases. CMAX++ is also shown to outperform baselines in simulated robotic tasks including 3D mobile robot navigation where the track friction is incorrectly modeled, and a 7D pick-and-place task where the mass of the object is unknown leading to discrepancy between true and modeled dynamics.

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arXiv2020

PERCH 2.0 : Fast and Accurate GPU-based Perception via Search for Object Pose Estimation

Pose estimation of known objects is fundamental to tasks such as robotic grasping and manipulation. The need for reliable grasping imposes stringent accuracy requirements on pose estimation in cluttered, occluded scenes in dynamic environments. Modern methods employ large sets of training data to learn features in order to find correspondence between 3D models and observed data. However these methods require extensive annotation of ground truth poses. An alternative is to use algorithms that search for the best explanation of the observed scene in a space of possible rendered scenes. A recently developed algorithm, PERCH (PErception Via SeaRCH) does so by using depth data to converge to a globally optimum solution using a search over a specially constructed tree. While PERCH offers strong guarantees on accuracy, the current formulation suffers from low scalability owing to its high runtime. In addition, the sole reliance on depth data for pose estimation restricts the algorithm to scenes where no two objects have the same shape. In this work, we propose PERCH 2.0, a novel perception via search strategy that takes advantage of GPU acceleration and RGB data. We show that our approach can achieve a speedup of 100x over PERCH, as well as better accuracy than the state-of-the-art data-driven approaches on 6-DoF pose estimation without the need for annotating ground truth poses in the training data. Our code and video are available at https://sbpl-cruz.github.io/perception/.

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arXiv2020

Planning, Learning and Reasoning Framework for Robot Truck Unloading

We consider the task of autonomously unloading boxes from trucks using an industrial manipulator robot. There are multiple challenges that arise: (1) real-time motion planning for a complex robotic system carrying two articulated mechanisms, an arm and a scooper, (2) decision-making in terms of what action to execute next given imperfect information about boxes such as their masses, (3) accounting for the sequential nature of the problem where current actions affect future state of the boxes, and (4) real-time execution that interleaves high-level decision-making with lower level motion planning. In this work, we propose a planning, learning, and reasoning framework to tackle these challenges, and describe its components including motion planning, belief space planning for offline learning, online decision-making based on offline learning, and an execution module to combine decision-making with motion planning. We analyze the performance of the framework on real-world scenarios. In particular, motion planning and execution modules are evaluated in simulation and on a real robot, while offline learning and online decision-making are evaluated in simulated real-world scenarios.

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arXiv2020

Provably Constant-time Planning and Replanning for Real-time Grasping Objects off a Conveyor Belt

In warehouse and manufacturing environments, manipulation platforms are frequently deployed at conveyor belts to perform pick and place tasks. Because objects on the conveyor belts are moving, robots have limited time to pick them up. This brings the requirement for fast and reliable motion planners that could provide provable real-time planning guarantees, which the existing algorithms do not provide. Besides the planning efficiency, the success of manipulation tasks relies heavily on the accuracy of the perception system which is often noisy, especially if the target objects are perceived from a distance. For fast moving conveyor belts, the robot cannot wait for a perfect estimate before it starts executing its motion. In order to be able to reach the object in time it must start moving early on (relying on the initial noisy estimates) and adjust its motion on-the-fly in response to the pose updates from perception. We propose an approach that meets these requirements by providing provable constant-time planning and replanning guarantees. We present it, give its analytical properties and show experimental analysis in simulation and on a real robot.

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arXiv2020

Multi-Resolution A*

Heuristic search-based planning techniques are commonly used for motion planning on discretized spaces. The performance of these algorithms is heavily affected by the resolution at which the search space is discretized. Typically a fixed resolution is chosen for a given domain. While a finer resolution allows for better maneuverability, it significantly increases the size of the state space, and hence demands more search efforts. On the contrary, a coarser resolution gives a fast exploratory behavior but compromises on maneuverability and the completeness of the search. To effectively leverage the advantages of both high and low resolution discretizations, we propose Multi-Resolution A* (MRA*) algorithm, that runs multiple weighted-A*(WA*) searches having different resolution levels simultaneously and combines the strengths of all of them. In addition to these searches, MRA* uses one anchor search to control expansions from these searches. We show that MRA* is bounded suboptimal with respect to the anchor resolution search space and resolution complete. We performed experiments on several motion planning domains including 2D, 3D grid planning and 7 DOF manipulation planning and compared our approach with several search-based and sampling-based baselines.

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arXiv2020

Planning with Selective Physics-based Simulation for Manipulation Among Movable Objects

Use of physics-based simulation as a planning model enables a planner to reason and generate plans that involve non-trivial interactions with the world. For example, grasping a milk container out of a cluttered refrigerator may involve moving a robot manipulator in between other objects, pushing away the ones that are movable and avoiding interactions with certain fragile containers. A physics-based simulator allows a planner to reason about the effects of interactions with these objects and to generate a plan that grasps the milk container successfully. The use of physics-based simulation for planning however is underutilized. One of the reasons for it being that physics-based simulations are typically way too slow for being used within a planning loop that typically requires tens of thousands of actions to be evaluated within a matter of a second or two. In this work, we develop a planning algorithm that tries to address this challenge. In particular, it builds on the observation that only a small number of actions actually need to be simulated using physics, and the remaining set of actions, such as moving an arm around obstacles, can be evaluated using a much simpler internal planning model, e.g., a simple collision-checking model. Motivated by this, we develop an algorithm called Planning with Selective Physics-based Simulation that automatically discovers what should be simulated with physics and what can utilize an internal planning model for pick-and-place tasks.

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arXiv2019

A Planning Framework for Persistent, Multi-UAV Coverage with Global Deconfliction

Planning for multi-robot coverage seeks to determine collision-free paths for a fleet of robots, enabling them to collectively observe points of interest in an environment. Persistent coverage is a variant of traditional coverage where coverage-levels in the environment decay over time. Thus, robots have to continuously revisit parts of the environment to maintain a desired coverage-level. Facilitating this in the real world demands we tackle numerous subproblems. While there exist standard solutions to these subproblems, there is no complete framework that addresses all of their individual challenges as a whole in a practical setting. We adapt and combine these solutions to present a planning framework for persistent coverage with multiple unmanned aerial vehicles (UAVs). Specifically, we run a continuous loop of goal assignment and globally deconflicting, kinodynamic path planning for multiple UAVs. We evaluate our framework in simulation as well as the real world. In particular, we demonstrate that (i) our framework exhibits graceful coverage given sufficient resources, we maintain persistent coverage; if resources are insufficient (e.g., having too few UAVs for a given size of the enviornment), coverage-levels decay slowly and (ii) planning with global deconfliction in our framework incurs a negligibly higher price compared to other weaker, more local collision-checking schemes. (Video: https://youtu.be/aqDs6Wymp5Q)

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arXiv2019

Provable Indefinite-Horizon Real-Time Planning for Repetitive Tasks

In many robotic manipulation scenarios, robots often have to perform highly-repetitive tasks in structured environments e.g. sorting mail in a mailroom or pick and place objects on a conveyor belt. In this work we are interested in settings where the tasks are similar, yet not identical (e.g., due to uncertain orientation of objects) and motion planning needs to be extremely fast. Preprocessing-based approaches prove to be very beneficial in these settings. They analyze the configuration-space offline to generate some auxiliary information which can then be used in the query phase to speedup planning times. Typically, the tighter the requirement is on query times the larger the memory footprint will be. In particular, for high-dimensional spaces, providing real-time planning capabilities is extremely challenging. While there are planners that guarantee real-time performance by limiting the planning horizon, we are not aware of general-purpose planners capable of doing it for indefinite horizon (i.e., planning to the goal). To this end, we propose a preprocessing-based method that provides provable bounds on the query time while incurring only a small amount of memory overhead in the query phase. We evaluate our method on a 7-DOF robot arm and show a speedup of over tenfold in query time when compared to the PRM algorithm.

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