529 research outputs found
Estimating Properties of Solid Particles Inside Container Using Touch Sensing
Solid particles, such as rice and coffee beans, are commonly stored in
containers and are ubiquitous in our daily lives. Understanding those
particles' properties could help us make later decisions or perform later
manipulation tasks such as pouring. Humans typically interact with the
containers to get an understanding of the particles inside them, but it is
still a challenge for robots to achieve that. This work utilizes tactile
sensing to estimate multiple properties of solid particles enclosed in the
container, specifically, content mass, content volume, particle size, and
particle shape. We design a sequence of robot actions to interact with the
container. Based on physical understanding, we extract static force/torque
value from the F/T sensor, vibration-related features and topple-related
features from the newly designed high-speed GelSight tactile sensor to estimate
those four particle properties. We test our method on very different daily
particles, including powder, rice, beans, tablets, etc. Experiments show that
our approach is able to estimate content mass with an error of g, content
volume with an error of ml, particle size with an error of mm, and
achieves an accuracy of % for particle shape estimation. In addition, our
method can generalize to unseen particles with unknown volumes. By estimating
these particle properties, our method can help robots to better perceive the
granular media and help with different manipulation tasks in daily life and
industry.Comment: 8 pages, 14 figure
Dynamic-Resolution Model Learning for Object Pile Manipulation
Dynamics models learned from visual observations have shown to be effective
in various robotic manipulation tasks. One of the key questions for learning
such dynamics models is what scene representation to use. Prior works typically
assume representation at a fixed dimension or resolution, which may be
inefficient for simple tasks and ineffective for more complicated tasks. In
this work, we investigate how to learn dynamic and adaptive representations at
different levels of abstraction to achieve the optimal trade-off between
efficiency and effectiveness. Specifically, we construct dynamic-resolution
particle representations of the environment and learn a unified dynamics model
using graph neural networks (GNNs) that allows continuous selection of the
abstraction level. During test time, the agent can adaptively determine the
optimal resolution at each model-predictive control (MPC) step. We evaluate our
method in object pile manipulation, a task we commonly encounter in cooking,
agriculture, manufacturing, and pharmaceutical applications. Through
comprehensive evaluations both in the simulation and the real world, we show
that our method achieves significantly better performance than state-of-the-art
fixed-resolution baselines at the gathering, sorting, and redistribution of
granular object piles made with various instances like coffee beans, almonds,
corn, etc.Comment: Accepted to Robotics: Science and Systems (RSS 2023). The first two
authors contributed equally. Project Page:
https://https://robopil.github.io/dyn-res-pile-mani
On Neuromechanical Approaches for the Study of Biological Grasp and Manipulation
Biological and robotic grasp and manipulation are undeniably similar at the
level of mechanical task performance. However, their underlying fundamental
biological vs. engineering mechanisms are, by definition, dramatically
different and can even be antithetical. Even our approach to each is
diametrically opposite: inductive science for the study of biological systems
vs. engineering synthesis for the design and construction of robotic systems.
The past 20 years have seen several conceptual advances in both fields and the
quest to unify them. Chief among them is the reluctant recognition that their
underlying fundamental mechanisms may actually share limited common ground,
while exhibiting many fundamental differences. This recognition is particularly
liberating because it allows us to resolve and move beyond multiple paradoxes
and contradictions that arose from the initial reasonable assumption of a large
common ground. Here, we begin by introducing the perspective of neuromechanics,
which emphasizes that real-world behavior emerges from the intimate
interactions among the physical structure of the system, the mechanical
requirements of a task, the feasible neural control actions to produce it, and
the ability of the neuromuscular system to adapt through interactions with the
environment. This allows us to articulate a succinct overview of a few salient
conceptual paradoxes and contradictions regarding under-determined vs.
over-determined mechanics, under- vs. over-actuated control, prescribed vs.
emergent function, learning vs. implementation vs. adaptation, prescriptive vs.
descriptive synergies, and optimal vs. habitual performance. We conclude by
presenting open questions and suggesting directions for future research. We
hope this frank assessment of the state-of-the-art will encourage and guide
these communities to continue to interact and make progress in these important
areas
Robotic Picking of Tangle-prone Materials (with Applications to Agriculture).
The picking of one or more objects from an unsorted pile continues to be non-trivial for robotic systems. This is especially so when the pile consists of individual items that tangle with one another, causing more to be picked out than desired. One of the key features of such tangling-prone materials (e.g., herbs, salads) is the presence of protrusions (e.g., leaves) extending out from the main body of items in the pile.This thesis explores the issue of picking excess mass due to entanglement such as occurs in bins composed of tangling-prone materials (TPs), especially in the context of a one-shot mass-constrained robotic bin-picking task. Specifically, it proposes a human-inspired entanglement reduction method for making the picking of TPs more predictable. The primary approach is to directly counter entanglement through pile interaction with an aim of reducing it to a level where the picked mass is predictable, instead of avoiding entanglement by picking from collision or entanglement-free points or regions. Taking this perspective, several contributions are presented that (i) improve the understanding of the phenomenon of entanglement and (ii) reduce the picking error (PE) by effectively countering entanglement in a TP pile.First, it studies the mechanics of a variety of TPs improving the understanding of the phenomenon of entanglement as observed in TP bins. It reports experiments with a real robot in which picking TPs with different protrusion lengths (PLs) results in up to a 76% increase in picked mass variance, suggesting PL be an informative feature in the design of picking strategies. Moreover, to counter the inherent entanglement in a TP pile, it proposes a new Spread-and-Pick (SnP) approach that significantly reduces entanglement, making picking more consistent. Compared to prior approaches that seek to pick from a tangle-free point in the pile, the proposed method results in a decrease in PE of up to 51% and shows good generalisation to previously unseen TPs
Learning to Simulate Tree-Branch Dynamics for Manipulation
We propose to use a simulation driven inverse inference approach to model the
joint dynamics of tree branches under manipulation. Learning branch dynamics
and gaining the ability to manipulate deformable vegetation can help with
occlusion-prone tasks, such as fruit picking in dense foliage, as well as
moving overhanging vines and branches for navigation in dense vegetation. The
underlying deformable tree geometry is encapsulated as coarse spring
abstractions executed on parallel, non-differentiable simulators. The implicit
statistical model defined by the simulator, reference trajectories obtained by
actively probing the ground truth, and the Bayesian formalism, together guide
the spring parameter posterior density estimation. Our non-parametric inference
algorithm, based on Stein Variational Gradient Descent, incorporates
biologically motivated assumptions into the inference process as neural network
driven learnt joint priors; moreover, it leverages the finite difference scheme
for gradient approximations. Real and simulated experiments confirm that our
model can predict deformation trajectories, quantify the estimation
uncertainty, and it can perform better when base-lined against other inference
algorithms, particularly from the Monte Carlo family. The model displays strong
robustness properties in the presence of heteroscedastic sensor noise;
furthermore, it can generalise to unseen grasp locations.Comment: 8 pages, 9 figure
Granular Jamming: Stiffness vs Pressure and Organ Palpation Devices
The intent of this thesis it to find a correlation between the stiffness of granular jammed particles and the pressure of the vacuum initiating the jamming force. Currently, granular jamming is being used to create palpation simulators for physicians to practice feeling the variety of stiffnesses of organs when healthy or ill. Because granular jamming allows for variable stiffness of any shape, it is an apt phenomenon to simulate the change of rigidity organs like the liver undergoes when diseased. For physicians to correctly identify how stiff the organ must be when using these palpation simulators, there needs to be a way to know how much pressure must be applied to correctly simulate the stiffness of the organ for each specific scenario. This thesis will discuss how pressure affects stiffness by using the three-point bending test. To perform this test, a tubular balloon filled with coffee granules was used to represent the beam. An impact force as well as a hanging force was used to displace the beam. The displacement of the beam is adequate to find the Young\u27s Modulus or stiffness of the beam of granules at different pressures provided by the vacuum. It was found that there is a correlation between stiffness and pressure of a granular jammed system. This will allow for future physicians to accurately and consistently use model organs to practice palpation techniques
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