Belief space planning assuming maximum likelihood observations

Platt, Robert; Tedrake, Russell Louis; Kaelbling, Leslie Pack; Lozano-Pérez, Tomás

doi:10.15607/rss.2010.vi.037

Cited by 276 publications

(291 citation statements)

References 11 publications

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“…Some approaches operate in generality and compute complete policies [8] while operate online, computing a plausible openloop plan and updating it as more information is acquired [14].…”

Section: Related Workmentioning

confidence: 99%

Provably Safe Robot Navigation with Obstacle Uncertainty

Axelrod

Kaelbling

Lozano-Pérez

2017

Robotics: Science and Systems XIII

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Abstract-As drones and autonomous cars become more widespread it is becoming increasingly important that robots can operate safely under realistic conditions. The noisy information fed into real systems means that robots must use estimates of the environment to plan navigation. Efficiently guaranteeing that the resulting motion plans are safe under these circumstances has proved difficult. We examine how to guarantee that a trajectory or policy is safe with only imperfect observations of the environment. We examine the implications of various mathematical formalisms of safety and arrive at a mathematical notion of safety of a long-term execution, even when conditioned on observational information. We present efficient algorithms that can prove that trajectories or policies are safe with much tighter bounds than in previous work. Notably, the complexity of the environment does not affect our method's ability to evaluate if a trajectory or policy is safe. We then use these safety checking methods to design a safe variant of the RRT planning algorithm.

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“…Some approaches operate in generality and compute complete policies [8] while operate online, computing a plausible openloop plan and updating it as more information is acquired [14].…”

Section: Related Workmentioning

confidence: 99%

Provably Safe Robot Navigation with Obstacle Uncertainty

Axelrod

Kaelbling

Lozano-Pérez

2017

Robotics: Science and Systems XIII

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“…Our focus in this paper is on robots with uncertainty in their motion and state estimation; we do not consider uncertainty in sensing of obstacle locations (e.g., [11]) or grasping (e.g., [20]). Extensive prior work has investigated motion planning under uncertainty for mobile robots that can be approximated as points or spheres in the workspace, e.g., [26,17,18,27,2,21,19,4,8]. For point or spherical robots, computing an estimate of the probability of collision with obstacles can be done in the workspace since the geometry of the C-obstacles is low dimensional and can be directly computed.…”

Section: Related Workmentioning

confidence: 99%

“…Extensive prior work has investigated integrated motion planning and control under uncertainty for robots that can be approximated as points in the workspace (e.g., [26,25,19,4]), but robotic manipulators raise new challenges. Whereas for point robots analytically estimating probability of collisions is facilitated by the fact that the robot's configuration space and workspace share parameters, for manipulators the configuration space and workspace are disjoint.…”

Section: Introductionmentioning

confidence: 99%

Safe Motion Planning for Imprecise Robotic Manipulators by Minimizing Probability of Collision

Sun

Torres

Berg

et al. 2016

Springer Tracts in Advanced Robotics

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Robotic manipulators designed for home assistance and new surgical procedures often have significant uncertainty in their actuation due to compliance requirements, cost constraints, and size limits. We introduce a new integrated motion planning and control algorithm for robotic manipulators that makes safety a priority by explicitly considering the probability of unwanted collisions. We first present a fast method for estimating the probability of collision of a motion plan for a robotic manipulator under the assumptions of Gaussian motion and sensing uncertainty. Our approach quickly computes distances to obstacles in the workspace and appropriately transforms this information into the configuration space using a Newton method to estimate the most relevant collision points in configuration space. We then present a sampling-based motion planner based on executing multiple independent rapidly exploring random trees that returns a plan that, under reasonable assumptions, asymptotically converges to a plan that minimizes the estimated collision probability. We demonstrate the speed and safety of our plans in simulation for (1) a 3-D manipulator with 6 DOF, and (2) a concentric tube robot, a tentacle-like robot designed for surgical applications.

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“…Interestingly, Hsiao et al [14] also explicitly consider the execution problem-but given a complete optimized belief plan. More recently, efficient approaches for belief planning in (locally approximated) Gaussian belief space using iLQG have been proposed [15], [16]. However, the running costs is still O(n 6 ) in the configuration space dimension.…”

Section: B Belief Planningmentioning

confidence: 99%

Dual execution of optimized contact interaction trajectories

Toussaint

Ratliff

Bohg

et al. 2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Efficient manipulation requires contact to reduce uncertainty. The manipulation literature refers to this as funneling: a methodology for increasing reliability and robustness by leveraging haptic feedback and control of environmental interaction. However, there is a fundamental gap between traditional approaches to trajectory optimization and this concept of robustness by funneling: traditional trajectory optimizers do not discover force feedback strategies. From a POMDP perspective, these behaviors could be regarded as explicit observation actions planned to sufficiently reduce uncertainty thereby enabling a task. While we are sympathetic to the full POMDP view, solving full continuous-space POMDPs in high-dimensions is hard. In this paper, we propose an alternative approach in which trajectory optimization objectives are augmented with new terms that reward uncertainty reduction through contacts, explicitly promoting funneling. This augmentation shifts the responsibility of robustness toward the actual execution of the optimized trajectories. Directly tracing trajectories through configuration space would lose all robustness-dual execution achieves robustness by devising force controllers to reproduce the temporal interaction profile encoded in the dual solution of the optimization problem. This work introduces dual execution in depth and analyze its performance through robustness experiments in both simulation and on a real-world robotic platform.

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Belief space planning assuming maximum likelihood observations

Cited by 276 publications

References 11 publications

Provably Safe Robot Navigation with Obstacle Uncertainty

Provably Safe Robot Navigation with Obstacle Uncertainty

Safe Motion Planning for Imprecise Robotic Manipulators by Minimizing Probability of Collision

Dual execution of optimized contact interaction trajectories

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