189 research outputs found
ReMasker: Imputing Tabular Data with Masked Autoencoding
We present ReMasker, a new method of imputing missing values in tabular data
by extending the masked autoencoding framework. Compared with prior work,
ReMasker is both simple -- besides the missing values (i.e., naturally masked),
we randomly ``re-mask'' another set of values, optimize the autoencoder by
reconstructing this re-masked set, and apply the trained model to predict the
missing values; and effective -- with extensive evaluation on benchmark
datasets, we show that ReMasker performs on par with or outperforms
state-of-the-art methods in terms of both imputation fidelity and utility under
various missingness settings, while its performance advantage often increases
with the ratio of missing data. We further explore theoretical justification
for its effectiveness, showing that ReMasker tends to learn
missingness-invariant representations of tabular data. Our findings indicate
that masked modeling represents a promising direction for further research on
tabular data imputation. The code is publicly available
Sensory Manipulation as a Countermeasure to Robot Teleoperation Delays: System and Evidence
In the field of robotics, robot teleoperation for remote or hazardous
environments has become increasingly vital. A major challenge is the lag
between command and action, negatively affecting operator awareness,
performance, and mental strain. Even with advanced technology, mitigating these
delays, especially in long-distance operations, remains challenging. Current
solutions largely focus on machine-based adjustments. Yet, there's a gap in
using human perceptions to improve the teleoperation experience. This paper
presents a unique method of sensory manipulation to help humans adapt to such
delays. Drawing from motor learning principles, it suggests that modifying
sensory stimuli can lessen the perception of these delays. Instead of
introducing new skills, the approach uses existing motor coordination
knowledge. The aim is to minimize the need for extensive training or complex
automation. A study with 41 participants explored the effects of altered haptic
cues in delayed teleoperations. These cues were sourced from advanced physics
engines and robot sensors. Results highlighted benefits like reduced task time
and improved perceptions of visual delays. Real-time haptic feedback
significantly contributed to reduced mental strain and increased confidence.
This research emphasizes human adaptation as a key element in robot
teleoperation, advocating for improved teleoperation efficiency via swift human
adaptation, rather than solely optimizing robots for delay adjustment.Comment: Submitted to Scientific Report
Brain Functional Connectivity under Teleoperation Latency: a fNIRS Study
Objective: This study aims to understand the cognitive impact of latency in
teleoperation and the related mitigation methods, using functional
Near-Infrared Spectroscopy (fNIRS) to analyze functional connectivity.
Background: Latency between command, execution, and feedback in teleoperation
can impair performance and affect operators mental state. The neural
underpinnings of these effects are not well understood. Method: A human subject
experiment (n = 41) of a simulated remote robot manipulation task was
performed. Three conditions were tested: no latency, with visual and haptic
latency, with visual latency and no haptic latency. fNIRS and performance data
were recorded and analyzed. Results: The presence of latency in teleoperation
significantly increased functional connectivity within and between prefrontal
and motor cortexes. Maintaining visual latency while providing real-time haptic
feedback reduced the average functional connectivity in all cortical networks
and showed a significantly different connectivity ratio within prefrontal and
motor cortical networks. The performance results showed the worst performance
in the all-delayed condition and best performance in no latency condition,
which echoes the neural activity patterns. Conclusion: The study provides
neurological evidence that latency in teleoperation increases cognitive load,
anxiety, and challenges in motion planning and control. Real-time haptic
feedback, however, positively influences neural pathways related to cognition,
decision-making, and sensorimotor processes. Application: This research can
inform the design of ergonomic teleoperation systems that mitigate the effects
of latency.Comment: Submitted to Human Factor
Nonparametric Generative Modeling with Conditional Sliced-Wasserstein Flows
Sliced-Wasserstein Flow (SWF) is a promising approach to nonparametric
generative modeling but has not been widely adopted due to its suboptimal
generative quality and lack of conditional modeling capabilities. In this work,
we make two major contributions to bridging this gap. First, based on a
pleasant observation that (under certain conditions) the SWF of joint
distributions coincides with those of conditional distributions, we propose
Conditional Sliced-Wasserstein Flow (CSWF), a simple yet effective extension of
SWF that enables nonparametric conditional modeling. Second, we introduce
appropriate inductive biases of images into SWF with two techniques inspired by
local connectivity and multiscale representation in vision research, which
greatly improve the efficiency and quality of modeling images. With all the
improvements, we achieve generative performance comparable with many deep
parametric generative models on both conditional and unconditional tasks in a
purely nonparametric fashion, demonstrating its great potential.Comment: ICML 202
Efficient Diffusion Policies for Offline Reinforcement Learning
Offline reinforcement learning (RL) aims to learn optimal policies from
offline datasets, where the parameterization of policies is crucial but often
overlooked. Recently, Diffsuion-QL significantly boosts the performance of
offline RL by representing a policy with a diffusion model, whose success
relies on a parametrized Markov Chain with hundreds of steps for sampling.
However, Diffusion-QL suffers from two critical limitations. 1) It is
computationally inefficient to forward and backward through the whole Markov
chain during training. 2) It is incompatible with maximum likelihood-based RL
algorithms (e.g., policy gradient methods) as the likelihood of diffusion
models is intractable. Therefore, we propose efficient diffusion policy (EDP)
to overcome these two challenges. EDP approximately constructs actions from
corrupted ones at training to avoid running the sampling chain. We conduct
extensive experiments on the D4RL benchmark. The results show that EDP can
reduce the diffusion policy training time from 5 days to 5 hours on
gym-locomotion tasks. Moreover, we show that EDP is compatible with various
offline RL algorithms (TD3, CRR, and IQL) and achieves new state-of-the-art on
D4RL by large margins over previous methods. Our code is available at
https://github.com/sail-sg/edp.Comment: preprin
Neural Dynamics of Delayed Feedback in Robot Teleoperation: Insights from fNIRS Analysis
As robot teleoperation increasingly becomes integral in executing tasks in
distant, hazardous, or inaccessible environments, the challenge of operational
delays remains a significant obstacle. These delays are inherent in signal
transmission and processing and can adversely affect the operators performance,
particularly in tasks requiring precision and timeliness. While current
research has made strides in mitigating these delays through advanced control
strategies and training methods, a crucial gap persists in understanding the
neurofunctional impacts of these delays and the efficacy of countermeasures
from a cognitive perspective. Our study narrows this gap by leveraging
functional Near-Infrared Spectroscopy (fNIRS) to examine the neurofunctional
implications of simulated haptic feedback on cognitive activity and motor
coordination under delayed conditions. In a human-subject experiment (N=41), we
manipulated sensory feedback to observe its influences on various brain regions
of interest (ROIs) response during teleoperation tasks. The fNIRS data provided
a detailed assessment of cerebral activity, particularly in ROIs implicated in
time perception and the execution of precise movements. Our results reveal that
certain conditions, which provided immediate simulated haptic feedback,
significantly optimized neural functions related to time perception and motor
coordination, and improved motor performance. These findings provide empirical
evidence about the neurofunctional basis of the enhanced motor performance with
simulated synthetic force feedback in the presence of teleoperation delays.Comment: Submitted to Frontiers in Human Neuroscienc
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