169 research outputs found
Stable bin packing of non-convex 3D objects with a robot manipulator
Recent progress in the field of robotic manipulation has generated interest
in fully automatic object packing in warehouses. This paper proposes a
formulation of the packing problem that is tailored to the automated
warehousing domain. Besides minimizing waste space inside a container, the
problem requires stability of the object pile during packing and the
feasibility of the robot motion executing the placement plans. To address this
problem, a set of constraints are formulated, and a constructive packing
pipeline is proposed to solve for these constraints. The pipeline is able to
pack geometrically complex, non-convex objects with stability while satisfying
robot constraints. In particular, a new 3D positioning heuristic called
Heightmap-Minimization heuristic is proposed, and heightmaps are used to speed
up the search. Experimental evaluation of the method is conducted with a
realistic physical simulator on a dataset of scanned real-world items,
demonstrating stable and high-quality packing plans compared with other 3D
packing methods
Advances in flexible manipulation through the application of AI-based techniques
282 p.Objektuak hartu eta uztea oinarrizko bi eragiketa dira ia edozein aplikazio robotikotan. Gaur egun, "pick and place" aplikazioetarako erabiltzen diren robot industrialek zeregin sinpleak eta errepikakorrak egiteko duten eraginkortasuna dute ezaugarri. Hala ere, sistema horiek oso zurrunak dira, erabat kontrolatutako inguruneetan lan egiten dute, eta oso kostu handia dakarte beste zeregin batzuk egiteko birprogramatzeak. Gaur egun, industria-ingurune desberdinetako zereginak daude (adibidez, logistika-ingurune batean eskaerak prestatzea), zeinak objektuak malgutasunez manipulatzea eskatzen duten, eta oraindik ezin izan dira automatizatu beren izaera dela-eta. Automatizazioa zailtzen duten botila-lepo nagusiak manipulatu beharreko objektuen aniztasuna, roboten trebetasun falta eta kontrolatu gabeko ingurune dinamikoen ziurgabetasuna dira.Adimen artifizialak (AA) gero eta paper garrantzitsuagoa betetzen du robotikaren barruan, robotei zeregin konplexuak betetzeko beharrezko adimena ematen baitie. Gainera, AAk benetako esperientzia erabiliz portaera konplexuak ikasteko aukera ematen du, programazioaren kostua nabarmen murriztuz. Objektuak manipulatzeko egungo sistema robotikoen mugak ikusita, lan honen helburu nagusia manipulazio-sistemen malgutasuna handitzea da AAn oinarritutako algoritmoak erabiliz, birprogramatu beharrik gabe ingurune dinamikoetara egokitzeko beharrezko gaitasunak emanez
SDF-Pack: Towards Compact Bin Packing with Signed-Distance-Field Minimization
Robotic bin packing is very challenging, especially when considering
practical needs such as object variety and packing compactness. This paper
presents SDF-Pack, a new approach based on signed distance field (SDF) to model
the geometric condition of objects in a container and compute the object
placement locations and packing orders for achieving a more compact bin
packing. Our method adopts a truncated SDF representation to localize the
computation, and based on it, we formulate the SDF minimization heuristic to
find optimized placements to compactly pack objects with the existing ones. To
further improve space utilization, if the packing sequence is controllable, our
method can suggest which object to be packed next. Experimental results on a
large variety of everyday objects show that our method can consistently achieve
higher packing compactness over 1,000 packing cases, enabling us to pack more
objects into the container, compared with the existing heuristics under various
packing settings
Learning Physically Realizable Skills for Online Packing of General 3D Shapes
We study the problem of learning online packing skills for irregular 3D
shapes, which is arguably the most challenging setting of bin packing problems.
The goal is to consecutively move a sequence of 3D objects with arbitrary
shapes into a designated container with only partial observations of the object
sequence. Meanwhile, we take physical realizability into account, involving
physics dynamics and constraints of a placement. The packing policy should
understand the 3D geometry of the object to be packed and make effective
decisions to accommodate it in the container in a physically realizable way. We
propose a Reinforcement Learning (RL) pipeline to learn the policy. The complex
irregular geometry and imperfect object placement together lead to huge
solution space. Direct training in such space is prohibitively data intensive.
We instead propose a theoretically-provable method for candidate action
generation to reduce the action space of RL and the learning burden. A
parameterized policy is then learned to select the best placement from the
candidates. Equipped with an efficient method of asynchronous RL acceleration
and a data preparation process of simulation-ready training sequences, a mature
packing policy can be trained in a physics-based environment within 48 hours.
Through extensive evaluation on a variety of real-life shape datasets and
comparisons with state-of-the-art baselines, we demonstrate that our method
outperforms the best-performing baseline on all datasets by at least 12.8% in
terms of packing utility.Comment: ACM Transactions on Graphics (TOG
The sensor based manipulation of irregularly shaped objects with special application to the semiconductor industry
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1998.Includes bibliographical references (leaves 91-94).by Vivek Anand Sujan.S.M
Robotic Learning the Sequence of Packing Irregular Objects from Human Demonstrations
We address the unsolved task of robotic bin packing with irregular objects,
such as groceries, where the underlying constraints on object placement and
manipulation, and the diverse objects' physical properties make preprogrammed
strategies unfeasible. Our approach is to learn directly from expert
demonstrations in order to extract implicit task knowledge and strategies to
achieve an efficient space usage, safe object positioning and to generate
human-like behaviors that enhance human-robot trust. We collect and make
available a novel and diverse dataset, BoxED, of box packing demonstrations by
humans in virtual reality. In total, 263 boxes were packed with
supermarket-like objects by 43 participants, yielding 4644 object
manipulations. We use the BoxED dataset to learn a Markov chain to predict the
object packing sequence for a given set of objects and compare it with human
performance. Our experimental results show that the model surpasses human
performance by generating sequence predictions that humans classify as
human-like more frequently than human-generated sequences.Comment: 8 pages, 7 figure
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