Combining Coarse and Fine Physics for Manipulation Using Parallel-in-Time Integration

Agboh, Wisdom C.; Ruprecht, Daniel; Dogar, Mehmet R.

doi:10.1007/978-3-030-95459-8_44

Cited by 10 publications

(6 citation statements)

References 27 publications

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“…This approach requires modeling contact interactions which can use simplified quasi-static pushing models [95], or general dynamic simulations such as offered by Mujoco [96], PyBullet [97], or DART [98]. The computational expense of these simulations is challenging, and motivated recent work on coarse physics predictions during manipulation planning [99]. Toussaint et al [100] use different abstractions of physics for manipulation planning with tool use.…”

Section: World Models Including Contact Interactionsmentioning

confidence: 99%

Towards Living Machines: current and future trends of tactile sensing, grasping, and social robotics

et al. 2023

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The development of future technologies can be highly influenced by our deeper understanding of the principles that underlie living organisms. The Living Machines conference aims at presenting (among others) the interdisciplinary work of behaving systems based on such principles. Celebrating the 10 years of the conference, we present the progress and future challenges of some of the key themes presented in the robotics workshop of the Living Machines conference. More specifically, in this perspective paper, we focus on the advances in the field of biomimetics and robotics for the creation of artificial systems that can robustly interact with their environment, ranging from tactile sensing, grasping, and manipulation to the creation of psychologically plausible agents.

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Section: World Models Including Contact Interactionsmentioning

confidence: 99%

Towards Living Machines: current and future trends of tactile sensing, grasping, and social robotics

et al. 2023

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“…[33] improved the longhorizon planning efficiency of a Monte-Carlo Tree Searchbased planner by integrating parallel simulation. [34,35] use simulators with different precisions and interleaved simulation and execution to improve manipulation planning performance and robustness. These approaches complement SAST.…”

Section: A Simulation In Roboticsmentioning

confidence: 99%

Object Reconfiguration with Simulation-Derived Feasible Actions

Lee¹,

Thomason²,

Kingston³

et al. 2023

Preprint

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3D object reconfiguration encompasses common robot manipulation tasks in which a set of objects must be moved through a series of physically feasible state changes into a desired final configuration. Object reconfiguration is challenging to solve in general, as it requires efficient reasoning about environment physics that determine action validity. This information is typically manually encoded in an explicit transition system. Constructing these explicit encodings is tedious and error-prone, and is often a bottleneck for planner use. In this work, we explore embedding a physics simulator within a motion planner to implicitly discover and specify the valid actions from any state, removing the need for manual specification of action semantics. Our experiments demonstrate that the resulting simulation-based planner can effectively produce physically valid rearrangement trajectories for a range of 3D object reconfiguration problems without requiring more than an environment description and start and goal arrangements.

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“…In this paper, we take a single-step push-grasp action and derive conditions for multi-object grasping under the frictional point contact model. Given the uncertainty in grasping systems [30,31], we plan robust multi-object grasps to improve PPH.…”

Section: A Multi-object Graspsmentioning

confidence: 99%

Learning to Efficiently Plan Robust Frictional Multi-Object Grasps

Agboh,

Sharma,

Srinivas

et al. 2023

2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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We consider a decluttering problem where multiple rigid convex polygonal objects rest in randomly placed positions and orientations on a planar surface and must be efficiently transported to a packing box using both single and multi-object grasps. Prior work considered frictionless multi-object grasping. In this paper, we introduce friction to increase the number of potential grasps for a given group of objects, and thus increase picks per hour. We train a neural network using real examples to plan robust multi-object grasps. In physical experiments, we find a 13.7% increase in success rate, a 1.6x increase in picks per hour, and a 6.3x decrease in grasp planning time compared to prior work on multi-object grasping. Compared to single-object grasping, we find a 3.1x increase in picks per hour.

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Combining Coarse and Fine Physics for Manipulation Using Parallel-in-Time Integration

Cited by 10 publications

References 27 publications

Towards Living Machines: current and future trends of tactile sensing, grasping, and social robotics

Towards Living Machines: current and future trends of tactile sensing, grasping, and social robotics

Object Reconfiguration with Simulation-Derived Feasible Actions

Learning to Efficiently Plan Robust Frictional Multi-Object Grasps

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