13 research outputs found
Manipulation-based active search for occluded objects
Object search is an integral part of daily life, and in the quest for competent mobile manipulation robots it is an unavoidable problem. Previous approaches focus on cases where objects are in unknown rooms but lying out in the open, which transforms object search into active visual search. However, in real life, objects may be in the back of cupboards occluded by other objects, instead of conveniently on a table by themselves. Extending search to occluded objects requires a more precise model and tighter integration with manipulation. We present a novel generative model for representing container contents by using object co-occurrence information and spatial constraints. Given a target object, a planner uses the model to guide an agent to explore containers where the target is likely, potentially needing to move occluding objects to enable further perception. We demonstrate the model on simulated domains and a detailed simulation involving a PR2 robot.National Science Foundation (U.S.) (Grant 1117325)United States. Office of Naval Research. Multidisciplinary University Research Initiative (Grant N00014-09-1-1051)United States. Air Force Office of Scientific Research (Grant FA2386-10-1-4135
Persistent Homology Guided Monte-Carlo Tree Search for Effective Non-Prehensile Manipulation
Performing object retrieval tasks in messy real-world workspaces involves the
challenges of \emph{uncertainty} and \emph{clutter}. One option is to solve
retrieval problems via a sequence of prehensile pick-n-place operations, which
can be computationally expensive to compute in highly-cluttered scenarios and
also inefficient to execute. The proposed framework selects the option of
performing non-prehensile actions, such as pushing, to clean a cluttered
workspace to allow a robotic arm to retrieve a target object. Non-prehensile
actions, allow to interact simultaneously with multiple objects, which can
speed up execution. At the same time, they can significantly increase
uncertainty as it is not easy to accurately estimate the outcome of a pushing
operation in clutter. The proposed framework integrates topological tools and
Monte-Carlo tree search to achieve effective and robust pushing for object
retrieval problems. In particular, it proposes using persistent homology to
automatically identify manageable clustering of blocking objects in the
workspace without the need for manually adjusting hyper-parameters.
Furthermore, MCTS uses this information to explore feasible actions to push
groups of objects together, aiming to minimize the number of pushing actions
needed to clear the path to the target object. Real-world experiments using a
Baxter robot, which involves some noise in actuation, show that the proposed
framework achieves a higher success rate in solving retrieval tasks in dense
clutter compared to state-of-the-art alternatives. Moreover, it produces
high-quality solutions with a small number of pushing actions improving the
overall execution time. More critically, it is robust enough that it allows to
plan the sequence of actions offline and then execute them reliably online with
Baxter
3D VSG: Long-term Semantic Scene Change Prediction through 3D Variable Scene Graphs
Numerous applications require robots to operate in environments shared with
other agents such as humans or other robots. However, such shared scenes are
typically subject to different kinds of long-term semantic scene changes. The
ability to model and predict such changes is thus crucial for robot autonomy.
In this work, we formalize the task of semantic scene variability estimation
and identify three main varieties of semantic scene change: changes in the
position of an object, its semantic state, or the composition of a scene as a
whole. To represent this variability, we propose the Variable Scene Graph
(VSG), which augments existing 3D Scene Graph (SG) representations with the
variability attribute, representing the likelihood of discrete long-term change
events. We present a novel method, DeltaVSG, to estimate the variability of
VSGs in a supervised fashion. We evaluate our method on the 3RScan long-term
dataset, showing notable improvements in this novel task over existing
approaches. Our method DeltaVSG achieves a precision of 72.2% and recall of
66.8%, often mimicking human intuition about how indoor scenes change over
time. We further show the utility of VSG predictions in the task of active
robotic change detection, speeding up task completion by 62.4% compared to a
scene-change-unaware planner. We make our code available as open-source.Comment: 8 pages, 4 figures, code to be released at
https://github.com/ethz-asl/3d_vs