Nonprehensile whole arm rearrangement planning on physics manifolds

King, Jennifer E.; Haustein, Joshua A.; Srinivasa, Siddhartha S.; Asfour, Tamim

doi:10.1109/icra.2015.7139535

Cited by 58 publications

(81 citation statements)

References 33 publications

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“…Recent work has formulated rearrangement planning [39] as a kinodynamic motion planning problem [13] in the joint configuration space of the robot and movable objects in the environment [15,22]. This formulation naturally copes with the continuous state and action spaces.…”

Section: Related Workmentioning

confidence: 99%

“…We populate Π can by drawing samples from a distribution over Π. In the rearrangement planning problem, we generate candidates by repeatedly calling a kinodynamic RRT [22] in the state space X with start x s and goal X g . .…”

Section: B Trajectory Selectionmentioning

confidence: 99%

“…Recent work has used simple physics models, like quasistatic pushing [8,9,22,32,39,43], to quickly produce efficient and intricate plans. However, the intricacy of these plans makes them particularly sensitive to uncertainty in object pose, physics parameters, and trajectory execution.…”

Section: Introductionmentioning

confidence: 99%

“…Exploiting the fact that we can quickly generate feasible state space trajectories [22], we formulate rearrangement planning under uncertainty (sections III and IV)…”

Section: Introductionmentioning

confidence: 99%

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Robust trajectory selection for rearrangement planning as a multi-armed bandit problem

Koval

King

Pollard

et al. 2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-We present an algorithm for generating openloop trajectories that solve the problem of rearrangement planning under uncertainty. We frame this as a selection problem where the goal is to choose the most robust trajectory from a finite set of candidates. We generate each candidate using a kinodynamic state space planner and evaluate it using noisy rollouts.Our key insight is we can formalize the selection problem as the "best arm" variant of the multi-armed bandit problem. We use the successive rejects algorithm to efficiently allocate rollouts between candidate trajectories given a rollout budget. We show that the successive rejects algorithm identifies the best candidate using fewer rollouts than a baseline algorithm in simulation. We also show that selecting a good candidate increases the likelihood of successful execution on a real robot. I. IntroductionWe explore the rearrangement planning problem [11] where a robot must rearrange several objects in a cluttered environment to achieve a goal ( Fig.1-Top). Recent work has used simple physics models, like quasistatic pushing [8,9,22,32,39,43], to quickly produce efficient and intricate plans. However, the intricacy of these plans makes them particularly sensitive to uncertainty in object pose, physics parameters, and trajectory execution.We address this problem by generating open-loop trajectories that are robust to uncertainty. However, this is particularly hard for rearrangement planning.First, the problem is set in a high-dimensional space with continuous actions. Second, contact causes physics to evolve in complex, non-linear ways and quickly leads to multi-modal and non-smooth distributions [25,26,34]. Third, finding good trajectories is inherently hard: most trajectories achieve success with zero probability.As a consequence, this problem lies outside the domains of standard conformant planning [19,37], POMDP [20] and policy search [7] algorithms.In response to these challenges, we propose a domain-agnostic algorithm that only requires: (1) a stochastic method of generating trajectories, (2) the ability to forward-simulate the system's dynamics, and (3) the capability of testing whether an execution is successful.Exploiting the fact that we can quickly generate feasible state space trajectories [22], we formulate rearrangement planning under uncertainty (sections III and IV)

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Section: Related Workmentioning

confidence: 99%

Section: B Trajectory Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Exploiting the fact that we can quickly generate feasible state space trajectories [22], we formulate rearrangement planning under uncertainty (sections III and IV)…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust trajectory selection for rearrangement planning as a multi-armed bandit problem

Koval

King

Pollard

et al. 2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Self Cite

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“…While manipulation is closely related to grasp planning and dynamics with its questions of stability, friction, and physics [13], [8], [14], we constrain ourselves to pre-defined manifolds of manipulation poses, which in the following will be introduced as transfer primitives.…”

Section: Related Workmentioning

confidence: 99%

MOPL: A multi-modal path planner for generic manipulation tasks

Jentzsch

Gaschler

Khatib

et al. 2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-For intelligent robots to solve real-world tasks, they need to manipulate multiple objects, and perform diverse manipulation actions apart from rigid transfers, such as pushing and sliding. Planning these tasks requires discrete changes between actions, and continuous, collision-free paths that fulfill action-specific constraints. In this work, we propose a multi-modal path planner, named MOPL, which accepts generic definitions of primitive actions with different types of contact manifolds, and randomly spans its search trees through these subspaces. Our evaluation shows that this generic search technique allows MOPL to solve several challenging scenarios over different types of kinematics and tools with reasonable performance. Furthermore, we demonstrate MOPL by solving and executing plans in two real-world experimental setups.

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Towards autonomous selective harvesting: A review of robot perception, robot design, motion planning and control

Rajendran

Debnath

Mghames

et al. 2023

Journal of Field Robotics

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This paper provides an overview of the current state‐of‐the‐art in selective harvesting robots (SHRs) and their potential for addressing the challenges of global food production. SHRs have the potential to increase productivity, reduce labor costs, and minimize wastage by selectively harvesting only ripe fruits and vegetables. The paper discusses the main components of SHRs, including perception, grasping, cutting, motion planning, and control. It also highlights the challenges in developing SHR technologies, particularly in the areas of robot design, motion planning, and control. The paper also discusses the potential benefits of integrating artificial intelligence and soft robots and data‐driven methods to enhance the performance and robustness of SHR systems. Finally, the paper identifies several open research questions in the field and highlights the need for further research and development efforts to advance SHR technologies to meet the challenges of global food production. Overall, this paper provides a starting point for researchers and practitioners interested in developing SHRs and highlights the need for more research in this field.

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Nonprehensile whole arm rearrangement planning on physics manifolds

Cited by 58 publications

References 33 publications

Robust trajectory selection for rearrangement planning as a multi-armed bandit problem

Robust trajectory selection for rearrangement planning as a multi-armed bandit problem

MOPL: A multi-modal path planner for generic manipulation tasks

Towards autonomous selective harvesting: A review of robot perception, robot design, motion planning and control

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