504 research outputs found
Data-Driven Approach to Simulating Realistic Human Joint Constraints
Modeling realistic human joint limits is important for applications involving
physical human-robot interaction. However, setting appropriate human joint
limits is challenging because it is pose-dependent: the range of joint motion
varies depending on the positions of other bones. The paper introduces a new
technique to accurately simulate human joint limits in physics simulation. We
propose to learn an implicit equation to represent the boundary of valid human
joint configurations from real human data. The function in the implicit
equation is represented by a fully connected neural network whose gradients can
be efficiently computed via back-propagation. Using gradients, we can
efficiently enforce realistic human joint limits through constraint forces in a
physics engine or as constraints in an optimization problem.Comment: To appear at ICRA 2018; 6 pages, 9 figures; for associated video, see
https://youtu.be/wzkoE7wCbu
Data-Augmented Contact Model for Rigid Body Simulation
Accurately modeling contact behaviors for real-world, near-rigid materials
remains a grand challenge for existing rigid-body physics simulators. This
paper introduces a data-augmented contact model that incorporates analytical
solutions with observed data to predict the 3D contact impulse which could
result in rigid bodies bouncing, sliding or spinning in all directions. Our
method enhances the expressiveness of the standard Coulomb contact model by
learning the contact behaviors from the observed data, while preserving the
fundamental contact constraints whenever possible. For example, a classifier is
trained to approximate the transitions between static and dynamic frictions,
while non-penetration constraint during collision is enforced analytically. Our
method computes the aggregated effect of contact for the entire rigid body,
instead of predicting the contact force for each contact point individually,
removing the exponential decline in accuracy as the number of contact points
increases.Comment: 7 pages, 7 figures. Submitted to ICRA 2019. Added video attachment
with full 3D experiments: https://youtu.be/AKSD8TabDV
Thermal Control of Electronics for Nuclear Robots via Phase Change Materials
AbstractAn effective thermal control is highly desired due to the increased heat generated from tight integration of electrical components. It is more difficult when the electronics are operating in high temperature, narrow space and strong nuclear radiation. In this paper, motor drivers of nuclear robots were taken as a case to study the thermal control methods and their effects on keeping the safe operation of electronics. Phase change materials (PCM) was found could lower the temperature by 20 oC and stabilize below 70 oC for more than 78min, which was 14 times longer than non-protective mode. Besides, the effect of heat sink on thermal conductivity enhancement was discussed
Learning Generalizable Manipulation Policies with Object-Centric 3D Representations
We introduce GROOT, an imitation learning method for learning robust policies
with object-centric and 3D priors. GROOT builds policies that generalize beyond
their initial training conditions for vision-based manipulation. It constructs
object-centric 3D representations that are robust toward background changes and
camera views and reason over these representations using a transformer-based
policy. Furthermore, we introduce a segmentation correspondence model that
allows policies to generalize to new objects at test time. Through
comprehensive experiments, we validate the robustness of GROOT policies against
perceptual variations in simulated and real-world environments. GROOT's
performance excels in generalization over background changes, camera viewpoint
shifts, and the presence of new object instances, whereas both state-of-the-art
end-to-end learning methods and object proposal-based approaches fall short. We
also extensively evaluate GROOT policies on real robots, where we demonstrate
the efficacy under very wild changes in setup. More videos and model details
can be found in the appendix and the project website:
https://ut-austin-rpl.github.io/GROOT .Comment: Accepted at the 7th Annual Conference on Robot Learning (CoRL), 2023
in Atlanta, U
An Integrative Paradigm for Enhanced Stroke Prediction: Synergizing XGBoost and xDeepFM Algorithms
Stroke prediction plays a crucial role in preventing and managing this
debilitating condition. In this study, we address the challenge of stroke
prediction using a comprehensive dataset, and propose an ensemble model that
combines the power of XGBoost and xDeepFM algorithms. Our work aims to improve
upon existing stroke prediction models by achieving higher accuracy and
robustness. Through rigorous experimentation, we validate the effectiveness of
our ensemble model using the AUC metric. Through comparing our findings with
those of other models in the field, we gain valuable insights into the merits
and drawbacks of various approaches. This, in turn, contributes significantly
to the progress of machine learning and deep learning techniques specifically
in the domain of stroke prediction
DROP: Dynamics Responses from Human Motion Prior and Projective Dynamics
Synthesizing realistic human movements, dynamically responsive to the
environment, is a long-standing objective in character animation, with
applications in computer vision, sports, and healthcare, for motion prediction
and data augmentation. Recent kinematics-based generative motion models offer
impressive scalability in modeling extensive motion data, albeit without an
interface to reason about and interact with physics. While
simulator-in-the-loop learning approaches enable highly physically realistic
behaviors, the challenges in training often affect scalability and adoption. We
introduce DROP, a novel framework for modeling Dynamics Responses of humans
using generative mOtion prior and Projective dynamics. DROP can be viewed as a
highly stable, minimalist physics-based human simulator that interfaces with a
kinematics-based generative motion prior. Utilizing projective dynamics, DROP
allows flexible and simple integration of the learned motion prior as one of
the projective energies, seamlessly incorporating control provided by the
motion prior with Newtonian dynamics. Serving as a model-agnostic plug-in, DROP
enables us to fully leverage recent advances in generative motion models for
physics-based motion synthesis. We conduct extensive evaluations of our model
across different motion tasks and various physical perturbations, demonstrating
the scalability and diversity of responses.Comment: SIGGRAPH Asia 2023, Video https://youtu.be/tF5WW7qNMLI, Website:
https://stanford-tml.github.io/drop
Differential Privacy in Privacy-Preserving Big Data and Learning: Challenge and Opportunity
Differential privacy (DP) has become the de facto standard of privacy preservation due to its strong protection and sound mathematical foundation, which is widely adopted in different applications such as big data analysis, graph data process, machine learning, deep learning, and federated learning. Although DP has become an active and influential area, it is not the best remedy for all privacy problems in different scenarios. Moreover, there are also some misunderstanding, misuse, and great challenges of DP in specific applications. In this paper, we point out a series of limits and open challenges of corresponding research areas. Besides, we offer potentially new insights and avenues on combining differential privacy with other effective dimension reduction techniques and secure multiparty computing to clearly define various privacy models
- …