8 research outputs found
6-DoF Stability Field via Diffusion Models
A core capability for robot manipulation is reasoning over where and how to
stably place objects in cluttered environments. Traditionally, robots have
relied on object-specific, hand-crafted heuristics in order to perform such
reasoning, with limited generalizability beyond a small number of object
instances and object interaction patterns. Recent approaches instead learn
notions of physical interaction, namely motion prediction, but require
supervision in the form of labeled object information or come at the cost of
high sample complexity, and do not directly reason over stability or object
placement. We present 6-DoFusion, a generative model capable of generating 3D
poses of an object that produces a stable configuration of a given scene.
Underlying 6-DoFusion is a diffusion model that incrementally refines a
randomly initialized SE(3) pose to generate a sample from a learned,
context-dependent distribution over stable poses. We evaluate our model on
different object placement and stacking tasks, demonstrating its ability to
construct stable scenes that involve novel object classes as well as to improve
the accuracy of state-of-the-art 3D pose estimation methods.Comment: In submissio
Quasi-static Soft Fixture Analysis of Rigid and Deformable Objects
We present a sampling-based approach to reasoning about the caging-based
manipulation of rigid and a simplified class of deformable 3D objects subject
to energy constraints. Towards this end, we propose the notion of soft fixtures
extending earlier work on energy-bounded caging to include a broader set of
energy function constraints and settings, such as gravitational and elastic
potential energy of 3D deformable objects. Previous methods focused on
establishing provably correct algorithms to compute lower bounds or
analytically exact estimates of escape energy for a very restricted class of
known objects with low-dimensional C-spaces, such as planar polygons. We
instead propose a practical sampling-based approach that is applicable in
higher-dimensional C-spaces but only produces a sequence of upper-bound
estimates that, however, appear to converge rapidly to actual escape energy. We
present 8 simulation experiments demonstrating the applicability of our
approach to various complex quasi-static manipulation scenarios. Quantitative
results indicate the effectiveness of our approach in providing upper-bound
estimates for escape energy in quasi-static manipulation scenarios. Two
real-world experiments also show that the computed normalized escape energy
estimates appear to correlate strongly with the probability of escape of an
object under randomized pose perturbation.Comment: Paper submitted to ICRA 202
Asymptotically Optimal Sampling-Based Motion Planning Methods
Motion planning is a fundamental problem in autonomous robotics that requires
finding a path to a specified goal that avoids obstacles and takes into account
a robot's limitations and constraints. It is often desirable for this path to
also optimize a cost function, such as path length.
Formal path-quality guarantees for continuously valued search spaces are an
active area of research interest. Recent results have proven that some
sampling-based planning methods probabilistically converge toward the optimal
solution as computational effort approaches infinity. This survey summarizes
the assumptions behind these popular asymptotically optimal techniques and
provides an introduction to the significant ongoing research on this topic.Comment: Posted with permission from the Annual Review of Control, Robotics,
and Autonomous Systems, Volume 4. Copyright 2021 by Annual Reviews,
https://www.annualreviews.org/. 25 pages. 2 figure