Publications:
2014 |
Zhao, M Identifying Features of Legible Manipulation Paths Masters Thesis Rutgers, the State University of New Jersey, 2014. @mastersthesis{Zhao:2014ab, title = {Identifying Features of Legible Manipulation Paths}, author = {M Zhao}, url = {http://www.cs.rutgers.edu/~kb572/pubs/Zhao_Min_MSThesis_legibility.pdf}, year = {2014}, date = {2014-10-01}, volume = {MS}, address = {Piscataway, New Jersey}, school = {Rutgers, the State University of New Jersey}, abstract = {This work performs an experimental study on the legibility of paths executed by a manipulation arm available on a Baxter robot. In this context, legibility is defined as the ability of people to effectively predict the target of the armtextquoterights motion. Paths that are legible can improve the collaboration of robots with humans since they allow people to intuitively understand the robottextquoterights intentions. Each experimental trial in this study reproduces manipulator motions to one of many targets in front of the robot. An appropriate experimental setup was developed in order to collect the responses of people in terms of the perceived robottextquoterights target during the execution of a trajectory by Baxter. The objective of the experimental setup was to minimize the cognitive load of the human subjects during the collection of data. The extensive experimental data provide insights into the features of motion that make certain paths more legible for humans than other paths. For instance, motions where the end-effector is oriented towards the intended target appear to be better in terms of legibility than alternatives.}, keywords = {}, pubstate = {published}, tppubtype = {mastersthesis} } This work performs an experimental study on the legibility of paths executed by a manipulation arm available on a Baxter robot. In this context, legibility is defined as the ability of people to effectively predict the target of the armtextquoterights motion. Paths that are legible can improve the collaboration of robots with humans since they allow people to intuitively understand the robottextquoterights intentions. Each experimental trial in this study reproduces manipulator motions to one of many targets in front of the robot. An appropriate experimental setup was developed in order to collect the responses of people in terms of the perceived robottextquoterights target during the execution of a trajectory by Baxter. The objective of the experimental setup was to minimize the cognitive load of the human subjects during the collection of data. The extensive experimental data provide insights into the features of motion that make certain paths more legible for humans than other paths. For instance, motions where the end-effector is oriented towards the intended target appear to be better in terms of legibility than alternatives. |
Krontiris, A; Shome, R; Dobson, A; Kimmel, A; Bekris, K Rearranging Similar Objects with a Manipulator Using Pebble Graphs Conference IEEE-RAS International Conference on Humanoid Robots (HUMANOIDS), Madrid, Spain, 2014. @conference{Krontiris:2014aa, title = {Rearranging Similar Objects with a Manipulator Using Pebble Graphs}, author = {A Krontiris and R Shome and A Dobson and A Kimmel and K Bekris}, url = {http://www.cs.rutgers.edu/~kb572/pubs/Krontiris_Humanoids14_rearrangement.pdf}, year = {2014}, date = {2014-01-01}, booktitle = {IEEE-RAS International Conference on Humanoid Robots (HUMANOIDS)}, address = {Madrid, Spain}, abstract = {This work proposes a method for effectively computing manipulation paths to rearrange similar objects in a cluttered space. Rearrangement is a challenging problem as it involves combinatorially large, continuous configuration spaces due to the presence of multiple bodies and kinematically complex manipulators. This work leverages ideas from algorithmic theory, multi-robot motion planning and manipulation planning to propose appropriate graphical representations for this challenge. These representations allow to quickly reason whether manipulation paths allow the transition between entire sets of object arrangements without having to explicitly store these arrangements. The proposed method also allows to take advantage of precomputation given a manipulation roadmap for transferring a single object in the same cluttered space. The resulting approach is probabilistically complete for a wide set of problem instances. It is evaluated in simulation for a realistic model of a Baxter robot and executed on the real system, showing that the approach solves complex instances and is promising in terms of scalability and success ratio.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } This work proposes a method for effectively computing manipulation paths to rearrange similar objects in a cluttered space. Rearrangement is a challenging problem as it involves combinatorially large, continuous configuration spaces due to the presence of multiple bodies and kinematically complex manipulators. This work leverages ideas from algorithmic theory, multi-robot motion planning and manipulation planning to propose appropriate graphical representations for this challenge. These representations allow to quickly reason whether manipulation paths allow the transition between entire sets of object arrangements without having to explicitly store these arrangements. The proposed method also allows to take advantage of precomputation given a manipulation roadmap for transferring a single object in the same cluttered space. The resulting approach is probabilistically complete for a wide set of problem instances. It is evaluated in simulation for a realistic model of a Baxter robot and executed on the real system, showing that the approach solves complex instances and is promising in terms of scalability and success ratio. |
Krontiris, A; Shome, R; Dobson, A; Kimmel, A; Yochelson, I; Bekris, K Similar Part Rearrangement with Pebble Graphs Journal Article CoRR, abs/1404.6573 , 2014. @article{Krontiris:2014ab, title = {Similar Part Rearrangement with Pebble Graphs}, author = {A Krontiris and R Shome and A Dobson and A Kimmel and I Yochelson and K Bekris}, url = {https://arxiv.org/abs/1404.6573}, year = {2014}, date = {2014-01-01}, journal = {CoRR}, volume = {abs/1404.6573}, abstract = {This work proposes a method for effectively computing manipulation paths to rearrange similar objects in a cluttered space. The solution can be used to place similar products in a factory floor in a desirable arrangement or for retrieving a particular object from a shelf blocked by similarly sized objects. These are challenging problems as they involve combinatorially large, continuous configuration spaces due to the presence of multiple moving bodies and kinematically complex manipulators. This work leverages ideas from algorithmic theory, multi-robot motion planning and manipulation planning to propose appropriate graphical representations for this challenge. These representations allow to quickly reason whether manipulation paths allow the transition between entire sets of objects arrangements without having to explicitly enumerate the path for each pair of arrangements. The proposed method also allows to take advantage of precomputation given a manipulation roadmap for transferring a single object in the same cluttered space. The resulting approach is evaluated in simulation for a realistic model of a Baxter robot and executed in open-loop on the real system, showing that the approach solves complex instances and is promising in terms of scalability and success ratio.}, keywords = {}, pubstate = {published}, tppubtype = {article} } This work proposes a method for effectively computing manipulation paths to rearrange similar objects in a cluttered space. The solution can be used to place similar products in a factory floor in a desirable arrangement or for retrieving a particular object from a shelf blocked by similarly sized objects. These are challenging problems as they involve combinatorially large, continuous configuration spaces due to the presence of multiple moving bodies and kinematically complex manipulators. This work leverages ideas from algorithmic theory, multi-robot motion planning and manipulation planning to propose appropriate graphical representations for this challenge. These representations allow to quickly reason whether manipulation paths allow the transition between entire sets of objects arrangements without having to explicitly enumerate the path for each pair of arrangements. The proposed method also allows to take advantage of precomputation given a manipulation roadmap for transferring a single object in the same cluttered space. The resulting approach is evaluated in simulation for a realistic model of a Baxter robot and executed in open-loop on the real system, showing that the approach solves complex instances and is promising in terms of scalability and success ratio. |
2005 |
Plaku, E; Bekris, K; Chen, B; Ladd, A; Kavraki, L Sampling-Based Roadmap of Trees for Parallel Motion Planning Journal Article IEEE Transactions on Robotics, 21 (4), 2005. @article{Plaku:2005aa, title = {Sampling-Based Roadmap of Trees for Parallel Motion Planning}, author = {E Plaku and K Bekris and B Chen and A Ladd and L Kavraki}, url = {https://ieeexplore.ieee.org/document/1492476}, year = {2005}, date = {2005-08-01}, journal = {IEEE Transactions on Robotics}, volume = {21}, number = {4}, chapter = {597-608}, abstract = {This paper shows how to effectively combine a sampling-based method primarily designed for multiple-query motion planning [probabilistic roadmap method (PRM)] with sampling-based tree methods primarily designed for single-query motion planning (expansive space trees, rapidly exploring random trees, and others) in a novel planning framework that can be efficiently parallelized. Our planner not only achieves a smooth spectrum between multiple-query and single-query planning, but it combines advantages of both. We present experiments which show that our planner is capable of solving problems that cannot be addressed efficiently with PRM or single-query planners. A key advantage of our planner is that it is significantly more decoupled than PRM and sampling-based tree planners. Exploiting this property, we designed and implemented a parallel version of our planner. Our experiments show that our planner distributes well and can easily solve high-dimensional problems that exhaust resources available to single machines and cannot be addressed with existing planners.}, keywords = {}, pubstate = {published}, tppubtype = {article} } This paper shows how to effectively combine a sampling-based method primarily designed for multiple-query motion planning [probabilistic roadmap method (PRM)] with sampling-based tree methods primarily designed for single-query motion planning (expansive space trees, rapidly exploring random trees, and others) in a novel planning framework that can be efficiently parallelized. Our planner not only achieves a smooth spectrum between multiple-query and single-query planning, but it combines advantages of both. We present experiments which show that our planner is capable of solving problems that cannot be addressed efficiently with PRM or single-query planners. A key advantage of our planner is that it is significantly more decoupled than PRM and sampling-based tree planners. Exploiting this property, we designed and implemented a parallel version of our planner. Our experiments show that our planner distributes well and can easily solve high-dimensional problems that exhaust resources available to single machines and cannot be addressed with existing planners. |
0000 |
Dobson, A; Moustakides, G; Bekris, K Sampling-based Roadmap Planners are Probably Near-Optimal after Finite Computation Journal Article CoRR, abs/1404.2166 , 0000. @article{Dobson:2014ac, title = {Sampling-based Roadmap Planners are Probably Near-Optimal after Finite Computation}, author = {A Dobson and G Moustakides and K Bekris}, url = {http://arxiv.org/abs/1404.2166}, journal = {CoRR}, volume = {abs/1404.2166}, abstract = {Sampling-based motion planners have proven to be efficient solutions to a variety of high-dimensional, geometrically complex motion planning problems with applications in several domains. The traditional view of these approaches is that they solve challenges efficiently by giving up formal guarantees and instead attain asymptotic properties in terms of completeness and optimality. Recent work has argued based on Monte Carlo experiments that these approaches also exhibit desirable probabilistic properties in terms of completeness and optimality after finite computation. The current paper formalizes these guarantees. It proves a formal bound on the probability that solutions returned by asymptotically optimal roadmap-based methods (e.g., PRM*) are within a bound of the optimal path length I* with clearance epsilon after a finite iteration n. This bound has the form P(|In - I* | łeq deltaI*) łeq Psuccess, where delta is an error term for the length a path in the PRM* graph, In. This bound is proven for general dimension Euclidean spaces and evaluated in simulation. A discussion on how this bound can be used in practice, as well as bounds for sparse roadmaps are also provided.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Sampling-based motion planners have proven to be efficient solutions to a variety of high-dimensional, geometrically complex motion planning problems with applications in several domains. The traditional view of these approaches is that they solve challenges efficiently by giving up formal guarantees and instead attain asymptotic properties in terms of completeness and optimality. Recent work has argued based on Monte Carlo experiments that these approaches also exhibit desirable probabilistic properties in terms of completeness and optimality after finite computation. The current paper formalizes these guarantees. It proves a formal bound on the probability that solutions returned by asymptotically optimal roadmap-based methods (e.g., PRM*) are within a bound of the optimal path length I* with clearance epsilon after a finite iteration n. This bound has the form P(|In - I* | łeq deltaI*) łeq Psuccess, where delta is an error term for the length a path in the PRM* graph, In. This bound is proven for general dimension Euclidean spaces and evaluated in simulation. A discussion on how this bound can be used in practice, as well as bounds for sparse roadmaps are also provided. |