Kinodynamic randomized rearrangement planning via dynamic transitions between statically stable states

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

doi:10.1109/icra.2015.7139621

Cited by 63 publications

(66 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several recent work considering object manipulation in clutter [3], [8], [9] also do not directly optimize energy or time used for accomplishing grasping tasks but mainly concern about validity of their plans. For example, [8] presents a randomized motion planner to grasp an object in clutter where no collision-free path exists in the initial configuration.…”

Section: Related Workmentioning

confidence: 99%

Efficient Obstacle Rearrangement for Object Manipulation Tasks in Cluttered Environments

Lee

Cho

Nam

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

We present an algorithm that produces a plan for relocating obstacles in order to grasp a target in clutter by a robotic manipulator without collisions. We consider configurations where objects are densely populated in a constrained and confined space. Thus, there exists no collision-free path for the manipulator without relocating obstacles. Since the problem of planning for object rearrangement has shown to be NP-hard, it is difficult to perform manipulation tasks efficiently which could frequently happen in service domains (e.g., taking out a target from a shelf or a fridge).Our proposed planner employs a collision avoidance scheme which has been widely used in mobile robot navigation. The planner determines an obstacle to be removed quickly in real time. It also can deal with dynamic changes in the configuration (e.g., changes in object poses). Our method is shown to be complete and runs in polynomial time. Experimental results in a realistic simulated environment show that our method improves up to 31% of the execution time compared to other competitors.

show abstract

Section: Related Workmentioning

confidence: 99%

Efficient Obstacle Rearrangement for Object Manipulation Tasks in Cluttered Environments

Lee

Cho

Nam

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

show abstract

“…Instead, we accelerate the learning by jump starting the value function from demonstrations [9], [18], [19]. The demonstrations we collect are generated in the simulator using a sampling based planner, the Kinodynamic RRT [20], and the rewards along the trajectory are fed to the neural network, to get an approximate value function for the policy produced by the planner.…”

Section: Learning the Value Functionmentioning

confidence: 99%

Learning Physics-Based Manipulation in Clutter: Combining Image-Based Generalization and Look-Ahead Planning

Bejjani

Dogar

Leonetti

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

View full text Add to dashboard Cite

Physics-based manipulation in clutter involves complex interaction between multiple objects. In this paper, we consider the problem of learning, from interaction in a physics simulator, manipulation skills to solve this multi-step sequential decision making problem in the real world. Our approach has two key properties: (i) the ability to generalize and transfer manipulation skills (over the type, shape, and number of objects in the scene) using an abstract imagebased representation that enables a neural network to learn useful features; and (ii) the ability to perform look-ahead planning in the image space using a physics simulator, which is essential for such multi-step problems. We show, in sets of simulated and real-world experiments (video available on https://youtu.be/EmkUQfyvwkY), that by learning to evaluate actions in an abstract image-based representation of the real world, the robot can generalize and adapt to the object shapes in challenging real-world environments.

show abstract

“…In particular, Kino-dynamic planners are one family of the sampling-based Rapidly exploring Random Trees planners, specific for solving planning problems that involve dynamic interactions. We implement a state-of-the-art kino-dynamic planner [4] used for solving physics-based manipulation in clutter planning problems. We generate P random problem instances s p init , G p P p=1 , as described in Sec.…”

Section: A Generating Example Plansmentioning

confidence: 99%

Planning with a Receding Horizon for Manipulation in Clutter Using a Learned Value Function

Bejjani

Papallas

Leonetti

et al. 2018

2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids)

View full text Add to dashboard Cite

Manipulation in clutter requires solving complex sequential decision making problems in an environment rich with physical interactions. The transfer of motion planning solutions from simulation to the real world, in open-loop, suffers from the inherent uncertainty in modelling real world physics. We propose interleaving planning and execution in real-time, in a closed-loop setting, using a Receding Horizon Planner (RHP) for pushing manipulation in clutter. In this context, we address the problem of finding a suitable value function based heuristic for efficient planning, and for estimating the cost-to-go from the horizon to the goal. We estimate such a value function first by using plans generated by an existing sampling-based planner. Then, we further optimize the value function through reinforcement learning. We evaluate our approach and compare it to state-of-the-art planning techniques for manipulation in clutter. We conduct experiments in simulation with artificially injected uncertainty on the physics parameters, as well as in real world tasks of manipulation in clutter. We show that this approach enables the robot to react to the uncertain dynamics of the real world effectively. arXiv:1803.08100v2 [cs.RO]

show abstract

Kinodynamic randomized rearrangement planning via dynamic transitions between statically stable states

Cited by 63 publications

References 24 publications

Efficient Obstacle Rearrangement for Object Manipulation Tasks in Cluttered Environments

Efficient Obstacle Rearrangement for Object Manipulation Tasks in Cluttered Environments

Learning Physics-Based Manipulation in Clutter: Combining Image-Based Generalization and Look-Ahead Planning

Planning with a Receding Horizon for Manipulation in Clutter Using a Learned Value Function

Contact Info

Product

Resources

About